myReferences.bib

@article{atzbergerTimeSeriesMonitoring2011,
  title = {A Time Series for Monitoring Vegetation Activity and Phenology at 10-Daily Time Steps Covering Large Parts of {{South America}}},
  author = {Atzberger, Clement and Eilers, Paul H.C.},
  date = {2011-09-01},
  journaltitle = {International Journal of Digital Earth},
  volume = {4},
  number = {5},
  pages = {365--386},
  publisher = {{Taylor \& Francis}},
  issn = {1753-8947},
  doi = {10.1080/17538947.2010.505664},
  url = {https://doi.org/10.1080/17538947.2010.505664},
  urldate = {2022-09-04},
  abstract = {It is widely accepted that natural resources should only be sustainably exploited and utilized to effectively preserve our planet for future generations. To better manage the natural resources, and to better understand the closely linked Earth systems, the concept of Digital Earth has been strongly promoted since US Vice President Al Gore's speech in 1998. One core element of Digital Earth is the use and integration of remote sensing data. Only satellite imagery can cover the entire globe repeatedly at a sufficient high-spatial resolution to map changes in land cover and land use, but also to detect more subtle changes related for instance to climate change. To uncover global change effects on vegetation activity and phenology, it is important to establish high quality time series characterizing the past situation against which the current state can be compared. With the present study we describe a time series of vegetation activity at 10-daily time steps between 1998 and 2008 covering large parts of South America at 1 km spatial resolution. Particular emphasis was put on noise removal. Only carefully filtered time series of vegetation indices can be used as a benchmark and for studying vegetation dynamics at a continental scale. Without temporal smoothing, subtle spatio-temporal patterns in vegetation composition, density and phenology would be hidden by atmospheric noise and undetected clouds. Such noise is immanent in data that have undergone solely a maximum value compositing. Within the present study, the Whittaker smoother (WS) was applied to a SPOT VGT time series. The WS balances the fidelity to the observations with the roughness of the smoothed curve. The algorithm is extremely fast, gives continuous control over smoothness with only one parameter, and interpolates automatically. The filtering efficiently removed the negatively biased noise present in the original data, while preserving the overall shape of the curves showing vegetation growth and development. Geostatistical variogram analysis revealed a significantly increased signal-to-noise ratio compared to the raw data. Analysis of the data also revealed spatially consistent key phenological markers. Extracted seasonality parameters followed a clear meridional trend. Compared to the unfiltered data, the filtered time series increased the separability of various land cover classes. It is thus expected that the data set holds great potential for environmental and vegetation related studies within the frame of Digital Earth.},
  keywords = {digital earth,earth observation,global data bases,global environmental change,image processing,natural resources,NDVI,remote sensing,seasonality,SPOT VGT,vegetation indices,whittaker,Whittaker smoother},
  annotation = {\_eprint: https://doi.org/10.1080/17538947.2010.505664}
}

@misc{barronGeneralAdaptiveRobust2019,
  title = {A {{General}} and {{Adaptive Robust Loss Function}}},
  author = {Barron, Jonathan T.},
  date = {2019-04-04},
  number = {arXiv:1701.03077},
  eprint = {1701.03077},
  eprinttype = {arxiv},
  primaryclass = {cs, stat},
  publisher = {{arXiv}},
  doi = {10.48550/arXiv.1701.03077},
  url = {http://arxiv.org/abs/1701.03077},
  urldate = {2022-09-09},
  abstract = {We present a generalization of the Cauchy/Lorentzian, Geman-McClure, Welsch/Leclerc, generalized Charbonnier, Charbonnier/pseudo-Huber/L1-L2, and L2 loss functions. By introducing robustness as a continuous parameter, our loss function allows algorithms built around robust loss minimization to be generalized, which improves performance on basic vision tasks such as registration and clustering. Interpreting our loss as the negative log of a univariate density yields a general probability distribution that includes normal and Cauchy distributions as special cases. This probabilistic interpretation enables the training of neural networks in which the robustness of the loss automatically adapts itself during training, which improves performance on learning-based tasks such as generative image synthesis and unsupervised monocular depth estimation, without requiring any manual parameter tuning.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning,robust-loss,Statistics - Machine Learning},
  file = {/home/lukas/Zotero/storage/XZK6KWDI/Barron - 2019 - A General and Adaptive Robust Loss Function.pdf;/home/lukas/Zotero/storage/DV2G3886/1701.html}
}

@article{battudeEstimatingMaizeBiomass2016,
  title = {Estimating Maize Biomass and Yield over Large Areas Using High Spatial and Temporal Resolution {{Sentinel-2}} like Remote Sensing Data},
  author = {Battude, Marjorie and Al Bitar, Ahmad and Morin, David and Cros, Jérôme and Huc, Mireille and Marais Sicre, Claire and Le Dantec, Valérie and Demarez, Valérie},
  date = {2016-10-01},
  journaltitle = {Remote Sensing of Environment},
  shortjournal = {Remote Sensing of Environment},
  volume = {184},
  pages = {668--681},
  issn = {0034-4257},
  doi = {10.1016/j.rse.2016.07.030},
  url = {https://www.sciencedirect.com/science/article/pii/S0034425716302875},
  urldate = {2022-03-08},
  abstract = {This study aims at developing a robust and generic methodology, based on the use of high resolution remote sensing data to provide accurate estimates of maize biomass and yield over large areas (i.e. at regional scale). We propose here a strategy of calibration and spatialization independent as much as possible of in situ measurements and reliable over large areas and under various climatic conditions. For this purpose, we combine the Simple Algorithm For Yield estimates (SAFY) model with high spatial and temporal resolution remote sensing data from several sensors: Formosat-2, SPOT4-Take5, Landsat-8 and Deimos-1. SPOT4-Take5 experiment conducted in 2013 was designed to simulate the temporal sampling of ESA's Sentinel-2 mission. This study led to a new version of the SAFY model that takes into account the seasonal variation of specific leaf area (SLA) and effective light use efficiency (ELUE). The study takes place in a temperate agrosystem located in the south west of France. The SAFY outputs were validated with local measurements of biomass and yield estimates at both local and regional scales using a multiannual dataset. Good results were obtained for both local biomass (R=0.98; RRMSE=14\%) and yield (R=0.81; RRMSE=8.9\%), and for yield estimations at regional scale (R=0.96; RRMSE=4.6\%). Results also showed that the use of a double logistic function to interpolate Green Area Index (GAI) time series permits to improve the estimations of biomass and yield when remote sensing data are missing. This work demonstrates the potential of high resolution remote sensing data to calibrate a simple crop model without resorting to in situ data and thus foreshadows the future applications using Sentinel-2 data.},
  langid = {english},
  keywords = {Biomass,Crop Modeling,Green Area Index,High spatial and temporal resolution,Maize,Regional scale,Yield},
  file = {/home/lukas/Zotero/storage/F3VETAQ5/S0034425716302875.html}
}

@article{beckImprovedMonitoringVegetation2006,
  title = {Improved Monitoring of Vegetation Dynamics at Very High Latitudes: {{A}} New Method Using {{MODIS NDVI}}},
  shorttitle = {Improved Monitoring of Vegetation Dynamics at Very High Latitudes},
  author = {Beck, Pieter S. A. and Atzberger, Clement and Høgda, Kjell Arild and Johansen, Bernt and Skidmore, Andrew K.},
  date = {2006-02-15},
  journaltitle = {Remote Sensing of Environment},
  shortjournal = {Remote Sensing of Environment},
  volume = {100},
  number = {3},
  pages = {321--334},
  issn = {0034-4257},
  doi = {10.1016/j.rse.2005.10.021},
  url = {https://www.sciencedirect.com/science/article/pii/S0034425705003640},
  urldate = {2022-04-11},
  abstract = {Current models of vegetation dynamics using the normalized vegetation index (NDVI) time series perform poorly for high-latitude environments. This is due partly to specific attributes of these environments, such as short growing season, long periods of darkness in winter, persistence of snow cover, and dominance of evergreen species, but also to the design of the models. We present a new method for monitoring vegetation activity at high latitudes, using Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI. It estimates the NDVI of the vegetation during winter and applies a double logistic function, which is uniquely defined by six parameters that describe the yearly NDVI time series. Using NDVI data from 2000 to 2004, we illustrate the performance of this method for an area in northern Scandinavia (35×162 km2, 68° N 23° E) and compare it to existing methods based on Fourier series and asymmetric Gaussian functions. The double logistic functions describe the NDVI data better than both the Fourier series and the asymmetric Gaussian functions, as quantified by the root mean square errors. Compared with the method based on Fourier series, the new method does not overestimate the duration of the growing season. In addition, it handles outliers effectively and estimates parameters that are related to phenological events, such as the timing of spring and autumn. This makes the method most suitable for both estimating biophysical parameters and monitoring vegetation phenology.},
  langid = {english},
  keywords = {Boreal forests,Climate change,Fennoscandia,Green up,Tundra},
  file = {/home/lukas/Zotero/storage/T5N3Q5XP/Beck et al. - 2006 - Improved monitoring of vegetation dynamics at very.pdf;/home/lukas/Zotero/storage/KHNAEET4/S0034425705003640.html}
}

@article{beckImprovedMonitoringVegetation2006a,
  title = {Improved Monitoring of Vegetation Dynamics at Very High Latitudes: {{A}} New Method Using {{MODIS NDVI}}},
  shorttitle = {Improved Monitoring of Vegetation Dynamics at Very High Latitudes},
  author = {Beck, Pieter S. A. and Atzberger, Clement and Høgda, Kjell Arild and Johansen, Bernt and Skidmore, Andrew K.},
  date = {2006-02-15},
  journaltitle = {Remote Sensing of Environment},
  shortjournal = {Remote Sensing of Environment},
  volume = {100},
  number = {3},
  pages = {321--334},
  issn = {0034-4257},
  doi = {10.1016/j.rse.2005.10.021},
  url = {https://www.sciencedirect.com/science/article/pii/S0034425705003640},
  urldate = {2022-08-31},
  abstract = {Current models of vegetation dynamics using the normalized vegetation index (NDVI) time series perform poorly for high-latitude environments. This is due partly to specific attributes of these environments, such as short growing season, long periods of darkness in winter, persistence of snow cover, and dominance of evergreen species, but also to the design of the models. We present a new method for monitoring vegetation activity at high latitudes, using Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI. It estimates the NDVI of the vegetation during winter and applies a double logistic function, which is uniquely defined by six parameters that describe the yearly NDVI time series. Using NDVI data from 2000 to 2004, we illustrate the performance of this method for an area in northern Scandinavia (35×162 km2, 68° N 23° E) and compare it to existing methods based on Fourier series and asymmetric Gaussian functions. The double logistic functions describe the NDVI data better than both the Fourier series and the asymmetric Gaussian functions, as quantified by the root mean square errors. Compared with the method based on Fourier series, the new method does not overestimate the duration of the growing season. In addition, it handles outliers effectively and estimates parameters that are related to phenological events, such as the timing of spring and autumn. This makes the method most suitable for both estimating biophysical parameters and monitoring vegetation phenology.},
  langid = {english},
  keywords = {Boreal forests,Climate change,double-logistic,Fennoscandia,Green up,Tundra},
  file = {/home/lukas/Zotero/storage/DPFDAYRV/Beck et al. - 2006 - Improved monitoring of vegetation dynamics at very.pdf;/home/lukas/Zotero/storage/Y4Y36YU7/S0034425705003640.html}
}

@article{breimanRandomForests2001,
  title = {Random {{Forests}}},
  author = {Breiman, Leo},
  date = {2001-10-01},
  journaltitle = {Machine Learning},
  shortjournal = {Machine Learning},
  volume = {45},
  number = {1},
  pages = {5--32},
  issn = {1573-0565},
  doi = {10.1023/A:1010933404324},
  url = {https://doi.org/10.1023/A:1010933404324},
  urldate = {2022-07-29},
  abstract = {Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund \& R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.},
  langid = {english},
  keywords = {classification,ensemble,randomForest,regression},
  file = {/home/lukas/Zotero/storage/2IWFSZ32/Breiman - 2001 - Random Forests.pdf}
}

@article{brockmannLocallyAdaptiveBandwidth1993,
  title = {Locally {{Adaptive Bandwidth Choice}} for {{Kernel Regression Estimators}}},
  author = {Brockmann, Michael and Gasser, Theo and Herrmann, Eva},
  date = {1993-12-01},
  journaltitle = {Journal of the American Statistical Association},
  volume = {88},
  number = {424},
  pages = {1302--1309},
  publisher = {{Taylor \& Francis}},
  issn = {0162-1459},
  doi = {10.1080/01621459.1993.10476411},
  url = {https://www.tandfonline.com/doi/abs/10.1080/01621459.1993.10476411},
  urldate = {2022-08-04},
  abstract = {Kernel estimators with a global bandwidth are commonly used to estimate regression functions. On the other hand, it is obvious that the choice of a local bandwidth can lead to better results, because a larger class of kernel estimators is available. Evidently, this may in turn affect variability. The optimal bandwidths depend essentially on the regression function itself and on the residual variance, and it is desirable to estimate them from the data. In this article, a local bandwidth estimator is studied. A comparison with its global bandwidth equivalent is performed both in theory and in simulations. As the main result it is shown that the possible gain in mean integrated squared error of the resulting regression estimator must be paid for by a larger variability of the estimator. This may lead to worse results if the sample size is small. An algorithm has been devised that puts special weight on stability aspects. Our simulation study shows that improvements over a global bandwidth estimator often can be realized even at small or moderate sample sizes.},
  keywords = {bandwidh,Local bandwidth selection,Nonparametric curve estimation,Plug-in estimator,Smoothing},
  annotation = {\_eprint: https://www.tandfonline.com/doi/pdf/10.1080/01621459.1993.10476411},
  file = {/home/lukas/Zotero/storage/WRTLD6LW/Brockmann et al. - 1993 - Locally Adaptive Bandwidth Choice for Kernel Regre.pdf}
}

@misc{buhlmanComputationalStatistics2020,
  title = {Computational {{Statistics}}},
  author = {Bühlman, Peter and Maechler, Martin},
  date = {2020-10-09},
  publisher = {{Seminar for Statistics, ETH Zürich}},
  file = {/home/lukas/Zotero/storage/B62WUKYL/_.pdf}
}

@article{caiPerformanceSmoothingMethods2017,
  title = {Performance of {{Smoothing Methods}} for {{Reconstructing NDVI Time-Series}} and {{Estimating Vegetation Phenology}} from {{MODIS Data}}},
  author = {Cai, Zhanzhang and Jönsson, Per and Jin, Hongxiao and Eklundh, Lars},
  date = {2017-12-07},
  journaltitle = {Remote Sensing},
  shortjournal = {Remote Sensing},
  volume = {9},
  number = {12},
  pages = {1271},
  issn = {2072-4292},
  doi = {10.3390/rs9121271},
  url = {http://www.mdpi.com/2072-4292/9/12/1271},
  urldate = {2022-08-31},
  abstract = {Many time-series smoothing methods can be used for reducing noise and extracting plant phenological parameters from remotely-sensed data, but there is still no conclusive evidence in favor of one method over others. Here we use moderate-resolution imaging spectroradiometer (MODIS) derived normalized difference vegetation index (NDVI) to investigate five smoothing methods: Savitzky-Golay fitting (SG), locally weighted regression scatterplot smoothing (LO), spline smoothing (SP), asymmetric Gaussian function fitting (AG), and double logistic function fitting (DL). We use ground tower measured NDVI (10 sites) and gross primary productivity (GPP, 4 sites) to evaluate the smoothed satellite-derived NDVI time-series, and elevation data to evaluate phenology parameters derived from smoothed NDVI. The results indicate that all smoothing methods can reduce noise and improve signal quality, but that no single method always performs better than others. Overall, the local filtering methods (SG and LO) can generate very accurate results if smoothing parameters are optimally calibrated. If local calibration cannot be performed, cross validation is a way to automatically determine the smoothing parameter. However, this method may in some cases generate poor fits, and when calibration is not possible the function fitting methods (AG and DL) provide the most robust description of the seasonal dynamics.},
  langid = {english},
  file = {/home/lukas/Zotero/storage/FQYIPJZE/Cai et al. - 2017 - Performance of Smoothing Methods for Reconstructin.pdf}
}

@article{caiPerformanceSmoothingMethods2017a,
  title = {Performance of {{Smoothing Methods}} for {{Reconstructing NDVI Time-Series}} and {{Estimating Vegetation Phenology}} from {{MODIS Data}}},
  author = {Cai, Zhanzhang and Jönsson, Per and Jin, Hongxiao and Eklundh, Lars},
  date = {2017-12},
  journaltitle = {Remote Sensing},
  volume = {9},
  number = {12},
  pages = {1271},
  publisher = {{Multidisciplinary Digital Publishing Institute}},
  issn = {2072-4292},
  doi = {10.3390/rs9121271},
  url = {https://www.mdpi.com/2072-4292/9/12/1271},
  urldate = {2022-09-09},
  abstract = {Many time-series smoothing methods can be used for reducing noise and extracting plant phenological parameters from remotely-sensed data, but there is still no conclusive evidence in favor of one method over others. Here we use moderate-resolution imaging spectroradiometer (MODIS) derived normalized difference vegetation index (NDVI) to investigate five smoothing methods: Savitzky-Golay fitting (SG), locally weighted regression scatterplot smoothing (LO), spline smoothing (SP), asymmetric Gaussian function fitting (AG), and double logistic function fitting (DL). We use ground tower measured NDVI (10 sites) and gross primary productivity (GPP, 4 sites) to evaluate the smoothed satellite-derived NDVI time-series, and elevation data to evaluate phenology parameters derived from smoothed NDVI. The results indicate that all smoothing methods can reduce noise and improve signal quality, but that no single method always performs better than others. Overall, the local filtering methods (SG and LO) can generate very accurate results if smoothing parameters are optimally calibrated. If local calibration cannot be performed, cross validation is a way to automatically determine the smoothing parameter. However, this method may in some cases generate poor fits, and when calibration is not possible the function fitting methods (AG and DL) provide the most robust description of the seasonal dynamics.},
  issue = {12},
  langid = {english},
  keywords = {gross primary production (GPP),itpl-review,MODIS,normalized difference vegetation index (NDVI),phenology,smoothing methods,TIMESAT},
  file = {/home/lukas/Zotero/storage/7PVQMFRF/Cai et al. - 2017 - Performance of Smoothing Methods for Reconstructin.pdf;/home/lukas/Zotero/storage/CNVPZ8MJ/1271.html}
}

@article{caoSimpleMethodImprove2018,
  title = {A Simple Method to Improve the Quality of {{NDVI}} Time-Series Data by Integrating Spatiotemporal Information with the {{Savitzky-Golay}} Filter},
  author = {Cao, Ruyin and Chen, Yang and Shen, Miaogen and Chen, Jin and Zhou, Ji and Wang, Cong and Yang, Wei},
  date = {2018-11-01},
  journaltitle = {Remote Sensing of Environment},
  shortjournal = {Remote Sensing of Environment},
  volume = {217},
  pages = {244--257},
  issn = {0034-4257},
  doi = {10.1016/j.rse.2018.08.022},
  url = {https://www.sciencedirect.com/science/article/pii/S0034425718303985},
  urldate = {2022-04-25},
  abstract = {High-quality Normalized Difference Vegetation Index (NDVI) time-series data are important for many regional and global ecological and environmental applications. Unfortunately, residual noise in current NDVI time-series products greatly hinders their further applications. Several noise-reduction methods have been proposed during the past two decades, but two important issues remain to be resolved. First, the methods usually perform poorly for cases of continuous missing data in the NDVI time series. Second, they generally assume negatively biased noise in the NDVI time series and thus erroneously raise some local low NDVI values in certain cases (e.g., the harvest period for multi-season crops).We therefore developed a new noise-reduction algorithm called the Spatial-Temporal Savitzky-Golay (STSG) method. The new method assumes discontinuous clouds in space and employs neighboring pixels to assist in the noise reduction of the target pixel in a particular year. The relationship between the NDVI of neighboring pixels and that of the target pixel was obtained from multi-year NDVI time series thanks to the accumulation of NDVI data over many years, which would have been impossible a decade ago. We tested STSG on 16-day composite MODIS NDVI time-series data from 2001 to 2016 in regions of mainland China and 11 phenology camera sites in North American. The results showed that STSG performed significantly better compared with four previous widely used methods (i.e., the Asymmetric Gaussian, Double Logistic, Fourier-based, and Savitzky-Golay filter methods). One obvious advantage was that STSG was able to address the problem of temporally continuous NDVI gaps. STSG effectively increased local low NDVI values and simultaneously avoided overcorrecting low NDVI values during the crop harvest period. In addition, implementing STSG required only raw MODIS NDVI time-series products without any additional burden of data requirements. All of these advantages make STSG a promising noise-reduction method for generating high-quality NDVI time-series data.},
  langid = {english},
  keywords = {Continuous NDVI gaps,High-quality NDVI reconstruction,MODIS and SPOT NDVI,Noise-reduction filter,Spatial-temporal information,VIIRS NDVI},
  file = {/home/lukas/Zotero/storage/UGGKDNXC/S0034425718303985.html}
}

@article{caoSimpleMethodImprove2018a,
  title = {A Simple Method to Improve the Quality of {{NDVI}} Time-Series Data by Integrating Spatiotemporal Information with the {{Savitzky-Golay}} Filter},
  author = {Cao, Ruyin and Chen, Yang and Shen, Miaogen and Chen, Jin and Zhou, Ji and Wang, Cong and Yang, Wei},
  date = {2018-11-01},
  journaltitle = {Remote Sensing of Environment},
  shortjournal = {Remote Sensing of Environment},
  volume = {217},
  pages = {244--257},
  issn = {0034-4257},
  doi = {10.1016/j.rse.2018.08.022},
  url = {https://www.sciencedirect.com/science/article/pii/S0034425718303985},
  urldate = {2022-04-25},
  abstract = {High-quality Normalized Difference Vegetation Index (NDVI) time-series data are important for many regional and global ecological and environmental applications. Unfortunately, residual noise in current NDVI time-series products greatly hinders their further applications. Several noise-reduction methods have been proposed during the past two decades, but two important issues remain to be resolved. First, the methods usually perform poorly for cases of continuous missing data in the NDVI time series. Second, they generally assume negatively biased noise in the NDVI time series and thus erroneously raise some local low NDVI values in certain cases (e.g., the harvest period for multi-season crops).We therefore developed a new noise-reduction algorithm called the Spatial-Temporal Savitzky-Golay (STSG) method. The new method assumes discontinuous clouds in space and employs neighboring pixels to assist in the noise reduction of the target pixel in a particular year. The relationship between the NDVI of neighboring pixels and that of the target pixel was obtained from multi-year NDVI time series thanks to the accumulation of NDVI data over many years, which would have been impossible a decade ago. We tested STSG on 16-day composite MODIS NDVI time-series data from 2001 to 2016 in regions of mainland China and 11 phenology camera sites in North American. The results showed that STSG performed significantly better compared with four previous widely used methods (i.e., the Asymmetric Gaussian, Double Logistic, Fourier-based, and Savitzky-Golay filter methods). One obvious advantage was that STSG was able to address the problem of temporally continuous NDVI gaps. STSG effectively increased local low NDVI values and simultaneously avoided overcorrecting low NDVI values during the crop harvest period. In addition, implementing STSG required only raw MODIS NDVI time-series products without any additional burden of data requirements. All of these advantages make STSG a promising noise-reduction method for generating high-quality NDVI time-series data.},
  langid = {english},
  keywords = {Continuous NDVI gaps,High-quality NDVI reconstruction,MODIS and SPOT NDVI,Noise-reduction filter,Spatial-temporal information,VIIRS NDVI},
  file = {/home/lukas/Zotero/storage/EWJPSDKW/S0034425718303985.html}
}

@article{caoSimpleMethodImprove2018b,
  title = {A Simple Method to Improve the Quality of {{NDVI}} Time-Series Data by Integrating Spatiotemporal Information with the {{Savitzky-Golay}} Filter},
  author = {Cao, Ruyin and Chen, Yang and Shen, Miaogen and Chen, Jin and Zhou, Ji and Wang, Cong and Yang, Wei},
  date = {2018-11-01},
  journaltitle = {Remote Sensing of Environment},
  shortjournal = {Remote Sensing of Environment},
  volume = {217},
  pages = {244--257},
  issn = {0034-4257},
  doi = {10.1016/j.rse.2018.08.022},
  url = {https://www.sciencedirect.com/science/article/pii/S0034425718303985},
  urldate = {2022-04-25},
  abstract = {High-quality Normalized Difference Vegetation Index (NDVI) time-series data are important for many regional and global ecological and environmental applications. Unfortunately, residual noise in current NDVI time-series products greatly hinders their further applications. Several noise-reduction methods have been proposed during the past two decades, but two important issues remain to be resolved. First, the methods usually perform poorly for cases of continuous missing data in the NDVI time series. Second, they generally assume negatively biased noise in the NDVI time series and thus erroneously raise some local low NDVI values in certain cases (e.g., the harvest period for multi-season crops).We therefore developed a new noise-reduction algorithm called the Spatial-Temporal Savitzky-Golay (STSG) method. The new method assumes discontinuous clouds in space and employs neighboring pixels to assist in the noise reduction of the target pixel in a particular year. The relationship between the NDVI of neighboring pixels and that of the target pixel was obtained from multi-year NDVI time series thanks to the accumulation of NDVI data over many years, which would have been impossible a decade ago. We tested STSG on 16-day composite MODIS NDVI time-series data from 2001 to 2016 in regions of mainland China and 11 phenology camera sites in North American. The results showed that STSG performed significantly better compared with four previous widely used methods (i.e., the Asymmetric Gaussian, Double Logistic, Fourier-based, and Savitzky-Golay filter methods). One obvious advantage was that STSG was able to address the problem of temporally continuous NDVI gaps. STSG effectively increased local low NDVI values and simultaneously avoided overcorrecting low NDVI values during the crop harvest period. In addition, implementing STSG required only raw MODIS NDVI time-series products without any additional burden of data requirements. All of these advantages make STSG a promising noise-reduction method for generating high-quality NDVI time-series data.},
  langid = {english},
  keywords = {Continuous NDVI gaps,High-quality NDVI reconstruction,MODIS and SPOT NDVI,Noise-reduction filter,Spatial-temporal information,VIIRS NDVI},
  file = {/home/lukas/Zotero/storage/6WNCVCJY/Cao et al. - 2018 - A simple method to improve the quality of NDVI tim.pdf;/home/lukas/Zotero/storage/GKBVRLHY/S0034425718303985.html}
}

@article{cappellariATLAS3DProjectXX2013,
  title = {The {{ATLAS3D}} Project - {{XX}}. {{Mass-size}} and Mass-Sigma Distributions of Early-Type Galaxies: Bulge Fraction Drives Kinematics, Mass-to-Light Ratio, Molecular Gas Fraction and Stellar Initial Mass Function},
  shorttitle = {The {{ATLAS3D}} Project - {{XX}}. {{Mass-size}} and Mass-Sigma Distributions of Early-Type Galaxies},
  author = {Cappellari, Michele and McDermid, Richard M. and Alatalo, Katherine and Blitz, Leo and Bois, Maxime and Bournaud, Frédéric and Bureau, M. and Crocker, Alison F. and Davies, Roger L. and Davis, Timothy A. and de Zeeuw, P. T. and Duc, Pierre-Alain and Emsellem, Eric and Khochfar, Sadegh and Krajnović, Davor and Kuntschner, Harald and Morganti, Raffaella and Naab, Thorsten and Oosterloo, Tom and Sarzi, Marc and Scott, Nicholas and Serra, Paolo and Weijmans, Anne-Marie and Young, Lisa M.},
  options = {useprefix=true},
  date = {2013-07-01},
  journaltitle = {Monthly Notices of the Royal Astronomical Society},
  volume = {432},
  pages = {1862--1893},
  issn = {0035-8711},
  doi = {10.1093/mnras/stt644},
  url = {https://ui.adsabs.harvard.edu/abs/2013MNRAS.432.1862C},
  urldate = {2022-05-11},
  keywords = {Astrophysics - Cosmology and Extragalactic Astrophysics,cD,galaxies: elliptical and lenticular,galaxies: evolution,galaxies: formation,galaxies: kinematics and dynamics,galaxies: structure},
  annotation = {ADS Bibcode: 2013MNRAS.432.1862C},
  file = {/home/lukas/Zotero/storage/ET6L6UW6/Cappellari et al. - 2013 - The ATLAS3D project - XX. Mass-size and mass-σ dis.pdf}
}

@article{chandolaScalableTimeSeries2010,
  title = {Scalable Time Series Change Detection for Biomass Monitoring Using {{Gaussian Processes}}},
  author = {Chandola, Varun and Vatsavai, Ranga Raju},
  date = {2010},
  journaltitle = {Conference on Intelligent Data Understanding},
  pages = {14},
  abstract = {Biomass monitoring, specifically, detecting changes in the biomass or vegetation of a geographical region, is vital for studying the carbon cycle of the system and has significant implications in the context of understanding climate change and its impacts. Recently, several time series change detection methods have been proposed to identify land cover changes in temporal profiles (time series) of vegetation collected using remote sensing instruments. In this paper, we adapt Gaussian process regression to detect changes in such time series in an online fashion. While Gaussian process (GP) has been widely used as a kernel based learning method for regression and classification, their applicability to massive spatio-temporal data sets, such as remote sensing data, has been limited owing to the high computational costs involved. In our previous work we proposed an efficient Toeplitz matrix based solution for scalable GP parameter estimation. In this paper we apply these solutions to a GP based change detection algorithm. The proposed change detection algorithm requires a memory footprint which is linear in the length of the input time series and runs in time which is quadratic to the length of the input time series. Experimental results show that both serial and parallel implementations of our proposed method achieve significant speedups over the serial implementation. Finally, we demonstrate the effectiveness of the proposed change detection method in identifying changes in Normalized Difference Vegetation Index (NDVI) data.},
  langid = {english},
  file = {/home/lukas/Zotero/storage/I8KPN8NH/Chandola and Vatsavai - 2010 - SCALABLE TIME SERIES CHANGE DETECTION FOR BIOMASS .pdf}
}

@article{chenSimpleMethodReconstructing2004,
  title = {A Simple Method for Reconstructing a High-Quality {{NDVI}} Time-Series Data Set Based on the {{Savitzky-Golay}} Filter},
  author = {Chen, Jin and Jönsson, Per and Tamura, Masayuki and Gu, Zhihui and Matsushita, Bunkei and Eklundh, Lars},
  date = {2004-06-01},
  journaltitle = {Remote Sensing of Environment},
  shortjournal = {Remote Sensing of Environment},
  volume = {91},
  pages = {332--344},
  doi = {10.1016/j.rse.2004.03.014},
  abstract = {Although the Normalized Difference Vegetation Index (NDVI) time-series data, derived from NOAA/AVHRR, SPOT/VEGETATION, TERRA or AQUA/MODIS, has been successfully used in research regarding global environmental change, residual noise in the NDVI time-series data, even after applying strict pre-processing, impedes further analysis and risks generating erroneous results. Based on the assumptions that NDVI time-series follow annual cycles of growth and decline of vegetation, and that clouds or poor atmospheric conditions usually depress NDVI values, we have developed in the present study a simple but robust method based on the Savitzky–Golay filter to smooth out noise in NDVI time-series, specifically that caused primarily by cloud contamination and atmospheric variability. Our method was developed to make data approach the upper NDVI envelope and to reflect the changes in NDVI patterns via an iteration process. From the results obtained by applying the newly developed method to a 10-day MVC SPOT VGT-S product, we provide optimized parameters for the new method and compare this technique with the BISE algorithm and Fourier-based fitting method. Our results indicate that the new method is more effective in obtaining high-quality NDVI time-series.},
  file = {/home/lukas/Zotero/storage/YK6H6ADW/Chen et al. - 2004 - A simple method for reconstructing a high-quality .pdf}
}

@article{chenSimpleMethodReconstructing2004a,
  title = {A Simple Method for Reconstructing a High-Quality {{NDVI}} Time-Series Data Set Based on the {{Savitzky}}–{{Golay}} Filter},
  author = {Chen, Jin and Jönsson, Per. and Tamura, Masayuki and Gu, Zhihui and Matsushita, Bunkei and Eklundh, Lars},
  date = {2004-06-30},
  journaltitle = {Remote Sensing of Environment},
  shortjournal = {Remote Sensing of Environment},
  volume = {91},
  number = {3},
  pages = {332--344},
  issn = {0034-4257},
  doi = {10.1016/j.rse.2004.03.014},
  url = {https://www.sciencedirect.com/science/article/pii/S003442570400080X},
  urldate = {2022-04-01},
  abstract = {Although the Normalized Difference Vegetation Index (NDVI) time-series data, derived from NOAA/AVHRR, SPOT/VEGETATION, TERRA or AQUA/MODIS, has been successfully used in research regarding global environmental change, residual noise in the NDVI time-series data, even after applying strict pre-processing, impedes further analysis and risks generating erroneous results. Based on the assumptions that NDVI time-series follow annual cycles of growth and decline of vegetation, and that clouds or poor atmospheric conditions usually depress NDVI values, we have developed in the present study a simple but robust method based on the Savitzky–Golay filter to smooth out noise in NDVI time-series, specifically that caused primarily by cloud contamination and atmospheric variability. Our method was developed to make data approach the upper NDVI envelope and to reflect the changes in NDVI patterns via an iteration process. From the results obtained by applying the newly developed method to a 10-day MVC SPOT VGT-S product, we provide optimized parameters for the new method and compare this technique with the BISE algorithm and Fourier-based fitting method. Our results indicate that the new method is more effective in obtaining high-quality NDVI time-series.},
  langid = {english},
  keywords = {NDVI,Savitzky–Golay filter,SPOT vegetation,Time-series data set},
  file = {/home/lukas/Zotero/storage/LA592DEZ/Chen et al. - 2004 - A simple method for reconstructing a high-quality .pdf;/home/lukas/Zotero/storage/WH9SDL2L/S003442570400080X.html}
}

@article{chenSimpleMethodReconstructing2004b,
  title = {A Simple Method for Reconstructing a High-Quality {{NDVI}} Time-Series Data Set Based on the {{Savitzky}}–{{Golay}} Filter},
  author = {Chen, Jin and Jönsson, Per. and Tamura, Masayuki and Gu, Zhihui and Matsushita, Bunkei and Eklundh, Lars},
  date = {2004-06-30},
  journaltitle = {Remote Sensing of Environment},
  shortjournal = {Remote Sensing of Environment},
  volume = {91},
  number = {3},
  pages = {332--344},
  issn = {0034-4257},
  doi = {10.1016/j.rse.2004.03.014},
  url = {https://www.sciencedirect.com/science/article/pii/S003442570400080X},
  urldate = {2022-08-31},
  abstract = {Although the Normalized Difference Vegetation Index (NDVI) time-series data, derived from NOAA/AVHRR, SPOT/VEGETATION, TERRA or AQUA/MODIS, has been successfully used in research regarding global environmental change, residual noise in the NDVI time-series data, even after applying strict pre-processing, impedes further analysis and risks generating erroneous results. Based on the assumptions that NDVI time-series follow annual cycles of growth and decline of vegetation, and that clouds or poor atmospheric conditions usually depress NDVI values, we have developed in the present study a simple but robust method based on the Savitzky–Golay filter to smooth out noise in NDVI time-series, specifically that caused primarily by cloud contamination and atmospheric variability. Our method was developed to make data approach the upper NDVI envelope and to reflect the changes in NDVI patterns via an iteration process. From the results obtained by applying the newly developed method to a 10-day MVC SPOT VGT-S product, we provide optimized parameters for the new method and compare this technique with the BISE algorithm and Fourier-based fitting method. Our results indicate that the new method is more effective in obtaining high-quality NDVI time-series.},
  langid = {english},
  keywords = {NDVI,savitzky-golay-filter,Savitzky–Golay filter,SPOT vegetation,Time-series data set},
  file = {/home/lukas/Zotero/storage/WTETJZEW/Chen et al. - 2004 - A simple method for reconstructing a high-quality .pdf;/home/lukas/Zotero/storage/RNBRRXCW/S003442570400080X.html}
}

@article{clevelandRobustLocallyWeighted1979,
  title = {Robust {{Locally Weighted Regression}} and {{Smoothing Scatterplots}}},
  author = {Cleveland, William S.},
  date = {1979-12-01},
  journaltitle = {Journal of the American Statistical Association},
  volume = {74},
  number = {368},
  pages = {829--836},
  issn = {0162-1459},
  doi = {10.1080/01621459.1979.10481038},
  url = {https://www.tandfonline.com/doi/abs/10.1080/01621459.1979.10481038},
  urldate = {2022-05-11},
  abstract = {The visual information on a scatterplot can be greatly enhanced, with little additional cost, by computing and plotting smoothed points. Robust locally weighted regression is a method for smoothing a scatterplot, (x i , y i ), i = 1, …, n, in which the fitted value at z k is the value of a polynomial fit to the data using weighted least squares, where the weight for (x i , y i ) is large if x i is close to x k and small if it is not. A robust fitting procedure is used that guards against deviant points distorting the smoothed points. Visual, computational, and statistical issues of robust locally weighted regression are discussed. Several examples, including data on lead intoxication, are used to illustrate the methodology.},
  keywords = {Graphics,Nonparametric regression,Robust estimation,Scatterplots,Smoothing},
  file = {/home/lukas/Zotero/storage/5VFB5Q3P/Cleveland - 1979 - Robust Locally Weighted Regression and Smoothing S.pdf;/home/lukas/Zotero/storage/U5XU6CT8/Cleveland - 1979 - Robust Locally Weighted Regression and Smoothing S.html}
}

@article{couraultSTICSCropModel2021,
  title = {{{STICS}} Crop Model and {{Sentinel-2}} Images for Monitoring Rice Growth and Yield in the {{Camargue}} Region},
  author = {Courault, Dominique and Hossard, Laure and Demarez, Valérie and Dechatre, Hélène and Irfan, Kamran and Baghdadi, Nicolas and Flamain, Fabrice and Ruget, Françoise},
  date = {2021-07-06},
  journaltitle = {Agronomy for Sustainable Development},
  shortjournal = {Agron. Sustain. Dev.},
  volume = {41},
  number = {4},
  pages = {49},
  issn = {1773-0155},
  doi = {10.1007/s13593-021-00697-w},
  url = {https://doi.org/10.1007/s13593-021-00697-w},
  urldate = {2022-09-02},
  abstract = {The assessment of rice yield at territory level is important for strategic economic decisions. Assessing spatial and temporal yield variability at regional scale is difficult because of the numerous factors involved, including agricultural practices, phenological calendars, and environmental contexts. New remote sensing data acquired at decametric resolution (Sentinel missions) can provide information on this spatial variability. The study objective was thus to evaluate the potential of Sentinel-2 images for monitoring rice cropping systems and yield from farm to region scales. The approach considered both observations and modeling. In-depth farmers surveys were carried out in the Camargue region, Southeastern France. The novelty was to use operational tools (BVNET and PHENOTB) to compute leaf area index, to daily interpolate this biophysical variable from 44 images acquired in 2016 and 2017 for each rice field, and to derive key phenological parameters from the analysis of the temporal profiles. The STICS crop model was spatially used, considering the biophysical variables derived from remote sensing. We tested four simulation strategies, differing in the integration intensity of remote sensing information into the model. Results have shown that (1) Sentinel-2 data allowed distinguishing early and late rice varieties. (2) The phenological stages mapped at the regional level allowed to better understand the agricultural practices of farmers. (3) The assimilation of remote sensing data to the STICS crop model significantly improved yield estimation and provided useful information on the spatial variability observed at regional scale. It was the first time that Sentinel-2 data are used with STICS crop model to assess rice yield at both farm and regional scale in the Camargue area. The proposed method is based on free open data and free access model, easily reproducible in other environmental contexts.},
  langid = {english},
  keywords = {crop-model-yield,Farm scale,LAI,Paddy,Production mapping,Remote sensing},
  file = {/home/lukas/Zotero/storage/U83TFWD3/Courault et al. - 2021 - STICS crop model and Sentinel-2 images for monitor.pdf}
}

@article{cravenSmoothingNoisyData1978,
  title = {Smoothing Noisy Data with Spline Functions},
  author = {Craven, Peter and Wahba, Grace},
  date = {1978-12-01},
  journaltitle = {Numerische Mathematik},
  shortjournal = {Numer. Math.},
  volume = {31},
  number = {4},
  pages = {377--403},
  issn = {0945-3245},
  doi = {10.1007/BF01404567},
  url = {https://doi.org/10.1007/BF01404567},
  urldate = {2022-08-31},
  abstract = {Smoothing splines are well known to provide nice curves which smooth discrete, noisy data. We obtain a practical, effective method for estimating the optimum amount of smoothing from the data. Derivatives can be estimated from the data by differentiating the resulting (nearly) optimally smoothed spline.},
  langid = {english},
  keywords = {CR:5.17,MOS:65D10,MOS:65D25,smoothing-splines-intro},
  file = {/home/lukas/Zotero/storage/IC8JJKEW/Craven and Wahba - 1978 - Smoothing noisy data with spline functions.pdf}
}

@article{deinesMillionKernelsTruth2021,
  title = {A Million Kernels of Truth: {{Insights}} into Scalable Satellite Maize Yield Mapping and Yield Gap Analysis from an Extensive Ground Dataset in the {{US Corn Belt}}},
  shorttitle = {A Million Kernels of Truth},
  author = {Deines, Jillian M. and Patel, Rinkal and Liang, Sang-Zi and Dado, Walter and Lobell, David B.},
  date = {2021-02-01},
  journaltitle = {Remote Sensing of Environment},
  shortjournal = {Remote Sensing of Environment},
  volume = {253},
  pages = {112174},
  issn = {0034-4257},
  doi = {10.1016/j.rse.2020.112174},
  url = {https://www.sciencedirect.com/science/article/pii/S0034425720305472},
  urldate = {2022-03-08},
  abstract = {Crop yield maps estimated from satellite data increasingly are used to understand drivers of yield trends and variability, yet satellite-derived regional maps are rarely compared with location-specific yields due to the difficulty of acquiring sub-field ground truth data at scale. In commercial agricultural systems, combine harvesters with onboard yield monitors collect real-time yield data during harvest with high spatial resolution, generating a large ground dataset. Here, we leveraged a yield monitor dataset of over one million maize field observations across the United States Corn Belt from 2008 to 2018 to evaluate the Scalable Crop Yield Mapper (SCYM). SCYM uses region-specific crop model simulations and climate data to interpret vegetation indices from satellite observations, thus enabling efficient sub-field yield estimation across large regions and multiple years without reliance on ground data calibration. We used the ground dataset to compare alternative SCYM model implementations, define minimum required satellite observation criteria, and evaluate the sensitivity of satellite-based yield estimates to on-the-ground variation in management, soil, and annual weather. We found that smoothing annual time series data with harmonic regression increased 30~m pixel-level accuracy from r2~=~0.31 to 0.40 and reduced dependency on specific satellite observation timing, enabling robust yield estimation on 97\% of annual maize area using only Landsat data. Agreement improved as the assessment was aggregated to field-level (r2~=~0.45) and county-level (r2~=~0.69) scales, demonstrating the need for fine-resolution ground truth data to better assess sub-field level accuracy in high resolution products. We found that SCYM and ground data showed a similar yield response to management and environmental variation, particularly capturing linear and nonlinear responses to sowing density, soil water holding capacity, and growing season precipitation. However, sensitivity to factors like soil quality and planting date was muted for SCYM estimates compared to ground-based yields. Random forest models were able to match SCYM performance when trained on at least 1000 ground observations, but performed poorly when tested on years and locations not represented in the training data. Our results demonstrate that satellite yield maps can provide much-needed information on multidecadal yield trends and inform yield gap analyses.},
  langid = {english},
  keywords = {Agricultural monitoring,Crop yields,Landsat,US Corn Belt},
  file = {/home/lukas/Zotero/storage/LJMJQY7Y/S0034425720305472.html}
}

@incollection{diggleBayesianInference2007,
  title = {Bayesian Inference},
  booktitle = {Model-Based {{Geostatistics}}},
  editor = {Diggle, Peter J. and Ribeiro, Paulo J.},
  date = {2007},
  pages = {157--198},
  publisher = {{Springer}},
  location = {{New York, NY}},
  doi = {10.1007/978-0-387-48536-2_7},
  url = {https://doi.org/10.1007/978-0-387-48536-2_7},
  urldate = {2022-03-29},
  abstract = {In Chapters 5 and 6 we discussed geostatistical inference from a classical or non-Bayesian perspective, treating parameter estimation and prediction as separate problems. We did this for two reasons, one philosophical the other practical. Firstly, in the non-Bayesian setting, there is a fundamental distinction between a parameter and a prediction target. A parameter has a fixed, but unknown value which represents a property of the processes which generate the data, whereas a prediction target is the realised value of a random variable associated with those same processes. Secondly, estimation and prediction are usually operationally separate in geostatistical practice, meaning that we first formulate our model and estimate its parameters, then plug the estimated parameter values into theoretical prediction equations as if they were the true values. An obvious concern with this two-phase approach is that ignoring uncertainty in the parameter estimates may lead to optimistic assessments of predictive accuracy. It is possible to address this concern in various ways without being Bayesian, but in our view the Bayesian approach gives a more elegant solution, and it is the one which we have adopted in our own work.},
  isbn = {978-0-387-48536-2},
  langid = {english},
  file = {/home/lukas/Zotero/storage/AQQMWIMU/Diggle and Ribeiro - 2007 - Bayesian inference.pdf}
}

@incollection{diggleClassicalParameterEstimation2007,
  title = {Classical Parameter Estimation},
  booktitle = {Model-Based {{Geostatistics}}},
  editor = {Diggle, Peter J. and Ribeiro, Paulo J.},
  date = {2007},
  pages = {99--133},
  publisher = {{Springer}},
  location = {{New York, NY}},
  doi = {10.1007/978-0-387-48536-2_5},
  url = {https://doi.org/10.1007/978-0-387-48536-2_5},
  urldate = {2022-03-29},
  abstract = {In this chapter, we discuss methods for formulating a suitable geostatistical model and estimating its parameters. We use the description "classical" in two different senses: firstly, as a reference to the variogram-based methods of estimation which are widely used in classical geostatistics as developed by the Fontainebleau school; secondly, within mainstream statistical methodology as a synonym for non-Bayesian. The chapter has a strong focus on the linear Gaussian model. This is partly because the Gaussian model is, from our perspective, implicit in much of classical geostatistical methodology, and partly because model-based estimation methods are most easily implemented in the linear Gaussian case. We discuss non-Bayesian estimation for generalized linear geostatistical models in Section 5.5, indicating in particular why maximum likelihood estimation is feasible in principle, but difficult to implement in practice.},
  isbn = {978-0-387-48536-2},
  langid = {english},
  file = {/home/lukas/Zotero/storage/TG99HBMM/Diggle and Ribeiro - 2007 - Classical parameter estimation.pdf}
}

@incollection{diggleGaussianModelsGeostatistical2007,
  title = {Gaussian Models for Geostatistical Data},
  booktitle = {Model-Based {{Geostatistics}}},
  author = {Diggle, Peter J. and Ribeiro, Paulo J.},
  date = {2007},
  pages = {46--78},
  publisher = {{Springer}},
  location = {{New York, NY}},
  doi = {10.1007/978-0-387-48536-2_3},
  url = {https://doi.org/10.1007/978-0-387-48536-2_3},
  urldate = {2022-03-29},
  abstract = {Gaussian stochastic processes are widely used in practice as models for geostatistical data. These models rarely have any physical justification. Rather, they are used as convenient empirical models which can capture a wide range of spatial behaviour according to the specification of their correlation structure. Historically, one very good reason for concentrating on Gaussian models was that they are uniquely tractable as models for dependent data. With the increasing use of computationally intensive methods, and in particular of simulation-based methods of inference, the analytic tractability of Gaussian models is becoming a less compelling reason to use them. Nevertheless, it is still convenient to work within a standard model class in routine applications. The scope of the Gaussian model class can be extended by using a transformation of the original response variable, and with this extra flexibility the model often provides a good empirical fit to data. Also, within the specific context of geostatistics, the Gaussian assumption is the model-based counterpart of some widely used geostatistical prediction methods, including simple, ordinary and universal kriging (Journel and Huijbregts, 1978; Chilès and Delfiner, 1999). We shall use the Gaussian model initially as a model in its own right for geostatistical data with a continuously varying response, and later as an important component of a hierarchically specified generalised linear model for geostatistical data with a discrete response variable, as previously discussed in Section 1.4.},
  isbn = {978-0-387-48536-2},
  langid = {english},
  file = {/home/lukas/Zotero/storage/UJGCJNY8/Diggle and Ribeiro - 2007 - Gaussian models for geostatistical data.pdf}
}

@incollection{diggleGeneralizedLinearModels2007,
  title = {Generalized Linear Models for Geostatistical Data},
  booktitle = {Model-Based {{Geostatistics}}},
  editor = {Diggle, Peter J. and Ribeiro, Paulo J.},
  date = {2007},
  pages = {79--98},
  publisher = {{Springer}},
  location = {{New York, NY}},
  doi = {10.1007/978-0-387-48536-2_4},
  url = {https://doi.org/10.1007/978-0-387-48536-2_4},
  urldate = {2022-03-29},
  abstract = {In the classical setting of independently replicated data, the generalized linear model (GLM) as introduced by Nelder and Wedderburn (1972) provides a unifying framework for regression modelling of continuous or discrete data. The original formulation has since been extended, in various ways, to accommodate dependent data. In this chapter we enlarge on the brief discussion of Section 1.4 to consider extensions of the classical GLM which are suitable for geostatistical applications.},
  isbn = {978-0-387-48536-2},
  langid = {english},
  file = {/home/lukas/Zotero/storage/5EFT7KYC/Diggle and Ribeiro - 2007 - Generalized linear models for geostatistical data.pdf}
}

@incollection{diggleGeostatisticalDesign2007,
  title = {Geostatistical Design},
  booktitle = {Model-Based {{Geostatistics}}},
  editor = {Diggle, Peter J. and Ribeiro, Paulo J.},
  date = {2007},
  pages = {199--212},
  publisher = {{Springer}},
  location = {{New York, NY}},
  doi = {10.1007/978-0-387-48536-2_8},
  url = {https://doi.org/10.1007/978-0-387-48536-2_8},
  urldate = {2022-03-29},
  abstract = {In this chapter, we consider the specific design problem of where to locate the sample points xi: i = 1, ..., n. In particular applications other design issues, such as what to measure at each location, what covariates to record and so forth, may be at least as important as the location of the sample points. But questions of this kind can only be addressed in specific contexts, whereas the sample-location problem can be treated generically.},
  isbn = {978-0-387-48536-2},
  langid = {english},
  file = {/home/lukas/Zotero/storage/GTAK3TYB/Diggle and Ribeiro - 2007 - Geostatistical design.pdf}
}

@incollection{diggleIntroduction2007,
  title = {Introduction},
  booktitle = {Model-Based {{Geostatistics}}},
  editor = {Diggle, Peter J. and Ribeiro, Paulo J.},
  date = {2007},
  pages = {1--26},
  publisher = {{Springer}},
  location = {{New York, NY}},
  doi = {10.1007/978-0-387-48536-2_1},
  url = {https://doi.org/10.1007/978-0-387-48536-2_1},
  urldate = {2022-03-29},
  abstract = {The term spatial statistics is used to describe a wide range of statistical models and methods intended for the analysis of spatially referenced data. Cressie (1993) provides a general overview. Within spatial statistics, the term geostatistics refers to models and methods for data with the following characteristics. Firstly, values Yi: i = 1, ..., n are observed at a discrete set of sampling locations xi within some spatial region A. Secondly, each observed value Yi is either a direct measurement of, or is statistically related to, the value of an underlying continuous spatial phenomenon, S(x), at the corresponding sampling location xi. This rather abstract formulation can be translated to a variety of more tangible scientific settings, as the following examples demonstrate.},
  isbn = {978-0-387-48536-2},
  langid = {english},
  file = {/home/lukas/Zotero/storage/EJE2SMNV/Diggle and Ribeiro - 2007 - Introduction.pdf}
}

@incollection{diggleOverviewModelbasedGeostatistics2007,
  title = {An Overview of Model-Based Geostatistics},
  booktitle = {Model-Based {{Geostatistics}}},
  editor = {Diggle, Peter J. and Ribeiro, Paulo J.},
  date = {2007},
  pages = {27--45},
  publisher = {{Springer}},
  location = {{New York, NY}},
  doi = {10.1007/978-0-387-48536-2_2},
  url = {https://doi.org/10.1007/978-0-387-48536-2_2},
  urldate = {2022-03-29},
  abstract = {The aim of this chapter is to provide a short overview of model-based geostatistics, using the elevation data of Example 1.1 to motivate the various stages in the analysis. Although this example is very limited from a scientific point of view, its simplicity makes it well suited to the task in hand. Note however, that Handcock and Stein (1993) show how to construct a useful explanatory variable for these data using a map of streams which run through the study region.},
  isbn = {978-0-387-48536-2},
  langid = {english},
  file = {/home/lukas/Zotero/storage/A6VZQQ3K/Diggle and Ribeiro - 2007 - An overview of model-based geostatistics.pdf}
}

@incollection{diggleSpatialPrediction2007,
  title = {Spatial Prediction},
  booktitle = {Model-Based {{Geostatistics}}},
  editor = {Diggle, Peter J. and Ribeiro, Paulo J.},
  date = {2007},
  pages = {134--156},
  publisher = {{Springer}},
  location = {{New York, NY}},
  doi = {10.1007/978-0-387-48536-2_6},
  url = {https://doi.org/10.1007/978-0-387-48536-2_6},
  urldate = {2022-03-29},
  abstract = {In this chapter, we consider the problem of using the available data to predict aspects of the realised, but unobserved, signal S(·). More formally, our target for prediction is the realised value of a random variable T = T (S), where S denotes the complete set of realised values of S(x) as x varies over the spatial region of interest, A. The simplest example of this general problem is to predict the value of the signal, T = S(x), at an arbitrary location x, using observed data Y = (Y1, ..., Yn), where each Yi represents a possibly noisy version of the corresponding S(xi). Other common targets T include the integral of S(x) over a prescribed sub-region of A or, more challengingly, a non-linear functional such as the maximum of S(x), or the set of locations for which S(x) exceeds some prescribed value. In this chapter, we ignore the problem of parameter estimation, in effect treating all model parameters as known quantities.},
  isbn = {978-0-387-48536-2},
  langid = {english},
  file = {/home/lukas/Zotero/storage/YB5BCJ3M/Diggle and Ribeiro - 2007 - Spatial prediction.pdf}
}

@article{EarthMultivariateAdaptive,
  title = {Earth: {{Multivariate Adaptive Regression Splines}}},
  pages = {52},
  langid = {english},
  file = {/home/lukas/Zotero/storage/35C5RGL2/earth Multivariate Adaptive Regression Splines.pdf}
}

@article{eilersPerfectSmoother2003,
  title = {A {{Perfect Smoother}}},
  author = {Eilers, Paul H. C.},
  date = {2003-07-01},
  journaltitle = {Analytical Chemistry},
  shortjournal = {Anal. Chem.},
  volume = {75},
  number = {14},
  pages = {3631--3636},
  publisher = {{American Chemical Society}},
  issn = {0003-2700},
  doi = {10.1021/ac034173t},
  url = {https://doi.org/10.1021/ac034173t},
  urldate = {2022-04-12},
  abstract = {The well-known and popular Savitzky−Golay filter has several disadvantages. A very attractive alternative is a smoother based on penalized least squares, extending ideas presented by Whittaker 80 years ago. This smoother is extremely fast, gives continuous control over smoothness, interpolates automatically, and allows fast leave-one-out cross-validation. It can be programmed in a few lines of Matlab code. Theory, implementation, and applications are presented.},
  file = {/home/lukas/Zotero/storage/93LJZT5W/Eilers - 2003 - A Perfect Smoother.pdf;/home/lukas/Zotero/storage/TQ6FWWPC/ac034173t.html}
}

@online{esaEuropeanSpaceAgency2022,
  title = {European {{Space Agency}}: {{Level-2A Algorithm Overview}}},
  author = {ESA},
  date = {2022-08-31},
  url = {https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm},
  urldate = {2022-08-31}
}

@online{esaSentinel22022,
  title = {Sentinel-2},
  shorttitle = {Sentinel-2},
  author = {ESA},
  date = {2022-08-31},
  url = {https://sentinel.esa.int/web/sentinel/missions/sentinel-2},
  urldate = {2022-08-31},
  organization = {{European Space Agency: Sentinel-2}}
}

@online{EstimatingWheatYields,
  title = {Estimating Wheat Yields in {{Australia}} Using Climate Records, Satellite Image Time Series and Machine Learning Methods - {{ScienceDirect}}},
  url = {https://www.sciencedirect.com/science/article/pii/S092427161930262X?via=ihub},
  urldate = {2022-07-20},
  file = {/home/lukas/Zotero/storage/N9WE8HWV/S092427161930262X.html}
}

@article{friedmanMultivariateAdaptiveRegression1991,
  title = {Multivariate {{Adaptive Regression Splines}}},
  author = {Friedman, Jerome H.},
  date = {1991-03},
  journaltitle = {The Annals of Statistics},
  volume = {19},
  number = {1},
  pages = {1--67},
  publisher = {{Institute of Mathematical Statistics}},
  issn = {0090-5364, 2168-8966},
  doi = {10.1214/aos/1176347963},
  url = {https://projecteuclid.org/journals/annals-of-statistics/volume-19/issue-1/Multivariate-Adaptive-Regression-Splines/10.1214/aos/1176347963.full},
  urldate = {2022-07-29},
  abstract = {A new method is presented for flexible regression modeling of high dimensional data. The model takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations) are automatically determined by the data. This procedure is motivated by the recursive partitioning approach to regression and shares its attractive properties. Unlike recursive partitioning, however, this method produces continuous models with continuous derivatives. It has more power and flexibility to model relationships that are nearly additive or involve interactions in at most a few variables. In addition, the model can be represented in a form that separately identifies the additive contributions and those associated with the different multivariable interactions.},
  keywords = {62H30,62J02,65D07,65D10,65D15,68T05,68T10,90A19,93C35,93E11,93E14,AID,CART,mars,multivariable function approximation,multivariate smoothing,Nonparametric multiple regression,recursive partitioning,splines,statistical learning neural networks},
  file = {/home/lukas/Zotero/storage/JM7YWFQB/Friedman - 1991 - Multivariate Adaptive Regression Splines.pdf;/home/lukas/Zotero/storage/XQCE8KVT/1176347963.html}
}

@article{friedmanMultivariateAdaptiveRegression1991a,
  title = {Multivariate {{Adaptive Regression Splines}}},
  author = {Friedman, Jerome H.},
  date = {1991-03-01},
  journaltitle = {The Annals of Statistics},
  shortjournal = {Ann. Statist.},
  volume = {19},
  number = {1},
  issn = {0090-5364},
  doi = {10.1214/aos/1176347963},
  url = {https://projecteuclid.org/journals/annals-of-statistics/volume-19/issue-1/Multivariate-Adaptive-Regression-Splines/10.1214/aos/1176347963.full},
  urldate = {2022-07-29},
  keywords = {mars-r},
  file = {/home/lukas/Zotero/storage/YCRJDFAJ/Friedman - 1991 - Multivariate Adaptive Regression Splines.pdf}
}

@article{gamonRelationshipsNDVICanopy1995,
  title = {Relationships {{Between NDVI}}, {{Canopy Structure}}, and {{Photosynthesis}} in {{Three Californian Vegetation Types}}},
  author = {Gamon, John A. and Field, Christopher B. and Goulden, Michael L. and Griffin, Kevin L. and Hartley, Anne E. and Joel, Geeske and Penuelas, Josep and Valentini, Riccardo},
  date = {1995},
  journaltitle = {Ecological Applications},
  volume = {5},
  number = {1},
  pages = {28--41},
  issn = {1939-5582},
  doi = {10.2307/1942049},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.2307/1942049},
  urldate = {2022-09-08},
  abstract = {In a range of plant species from three Californian vegetation types, we examined the widely used 'normalized difference vegetation index' (NDVI) and 'simple ratio' (SR) as indicators of canopy structure, light absorption, and photosynthetic activity. These indices, which are derived from canopy reflectance in the red and near-infrared wavebands, highlighted phenological differences between evergreen and deciduous canopies. They were poor indicators of total canopy biomass due to the varying abundance of non-green standing biomass in these vegetation types. However, in sparse canopies (leaf area index [LAI]°0-2), NDVI was a sensitive indicator of canopy structure and chemical content (green biomass, green leaf area index, chlorophyll content, and foliar nitrogen content). At higher canopy green LAI values ({$>$}2; typical of dense shrubs and trees), NDVI was relatively insensitive to changes in canopy structure. Compared to SR, NDVI was better correlated with indicators of canopy structure and chemical content, but was equivalent to the logarithm of SR. In agreement with theoretical expectations, both NDVI and SR exhibited near-linear correlations with fractional PAR intercepted by green leaves over a wide range of canopy densities. Maximum daily photosynthetic rates were positively correlated with NDVI and SR in annual grassland and semideciduous shrubs where canopy development and photosynthetic activity were in synchrony. The indices were also correlated with peak springtime canopy photosynthetic rates in evergreens. However, over most of the year, these indices were poor predictors of photosynthetic performance in evergreen species due to seasonal reductions in photosynthetic radiation-use efficiency that occurred without substantial declines in canopy greenness. Our results support the use of these vegetation indices as remote indicators of PAR absorption, and thus potential photosynthetic activity, even in heterogeneous landscapes. To provide accurate estimates of vegetation-atmosphere gas fluxes, remote NDVI and SR measurements need to be coupled with careful estimates of canopy photosynthetic radiation-use efficiency.},
  langid = {english},
  annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.2307/1942049},
  file = {/home/lukas/Zotero/storage/C2ILDNVC/Gamon et al. - 1995 - Relationships Between NDVI, Canopy Structure, and .pdf;/home/lukas/Zotero/storage/Z7C3KVUS/1942049.html}
}

@article{gamonRelationshipsNDVICanopy1995a,
  title = {Relationships {{Between NDVI}}, {{Canopy Structure}}, and {{Photosynthesis}} in {{Three Californian Vegetation Types}}},
  author = {Gamon, John A. and Field, Christopher B. and Goulden, Michael L. and Griffin, Kevin L. and Hartley, Anne E. and Joel, Geeske and Penuelas, Josep and Valentini, Riccardo},
  date = {1995},
  journaltitle = {Ecological Applications},
  volume = {5},
  number = {1},
  pages = {28--41},
  issn = {1939-5582},
  doi = {10.2307/1942049},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.2307/1942049},
  urldate = {2022-09-08},
  abstract = {In a range of plant species from three Californian vegetation types, we examined the widely used 'normalized difference vegetation index' (NDVI) and 'simple ratio' (SR) as indicators of canopy structure, light absorption, and photosynthetic activity. These indices, which are derived from canopy reflectance in the red and near-infrared wavebands, highlighted phenological differences between evergreen and deciduous canopies. They were poor indicators of total canopy biomass due to the varying abundance of non-green standing biomass in these vegetation types. However, in sparse canopies (leaf area index [LAI]°0-2), NDVI was a sensitive indicator of canopy structure and chemical content (green biomass, green leaf area index, chlorophyll content, and foliar nitrogen content). At higher canopy green LAI values ({$>$}2; typical of dense shrubs and trees), NDVI was relatively insensitive to changes in canopy structure. Compared to SR, NDVI was better correlated with indicators of canopy structure and chemical content, but was equivalent to the logarithm of SR. In agreement with theoretical expectations, both NDVI and SR exhibited near-linear correlations with fractional PAR intercepted by green leaves over a wide range of canopy densities. Maximum daily photosynthetic rates were positively correlated with NDVI and SR in annual grassland and semideciduous shrubs where canopy development and photosynthetic activity were in synchrony. The indices were also correlated with peak springtime canopy photosynthetic rates in evergreens. However, over most of the year, these indices were poor predictors of photosynthetic performance in evergreen species due to seasonal reductions in photosynthetic radiation-use efficiency that occurred without substantial declines in canopy greenness. Our results support the use of these vegetation indices as remote indicators of PAR absorption, and thus potential photosynthetic activity, even in heterogeneous landscapes. To provide accurate estimates of vegetation-atmosphere gas fluxes, remote NDVI and SR measurements need to be coupled with careful estimates of canopy photosynthetic radiation-use efficiency.},
  langid = {english},
  keywords = {photosynthesis},
  annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.2307/1942049},
  file = {/home/lukas/Zotero/storage/GISFGB9J/Gamon et al. - 1995 - Relationships Between NDVI, Canopy Structure, and .pdf;/home/lukas/Zotero/storage/NZEZWFRU/1942049.html}
}

@article{guNDVISaturationAdjustment2013,
  title = {{{NDVI}} Saturation Adjustment: {{A}} New Approach for Improving Cropland Performance Estimates in the {{Greater Platte River Basin}}, {{USA}}},
  shorttitle = {{{NDVI}} Saturation Adjustment},
  author = {Gu, Yingxin and Wylie, Bruce K. and Howard, Daniel M. and Phuyal, Khem P. and Ji, Lei},
  date = {2013-07-01},
  journaltitle = {Ecological Indicators},
  shortjournal = {Ecological Indicators},
  volume = {30},
  pages = {1--6},
  issn = {1470-160X},
  doi = {10.1016/j.ecolind.2013.01.041},
  url = {https://www.sciencedirect.com/science/article/pii/S1470160X13000757},
  urldate = {2022-09-08},
  abstract = {In this study, we developed a new approach that adjusted normalized difference vegetation index (NDVI) pixel values that were near saturation to better characterize the cropland performance (CP) in the Greater Platte River Basin (GPRB), USA. The relationship between NDVI and the ratio vegetation index (RVI) at high NDVI values was investigated, and an empirical equation for estimating saturation-adjusted NDVI (NDVIsat\_adjust) based on RVI was developed. A 10-year (2000–2009) NDVIsat\_adjust data set was developed using 250-m 7-day composite historical eMODIS (expedited Moderate Resolution Imaging Spectroradiometer) NDVI data. The growing season averaged NDVI (GSN), which is a proxy for ecosystem performance, was estimated and long-term NDVI non-saturation- and saturation-adjusted cropland performance (CPnon\_sat\_adjust, CPsat\_adjust) maps were produced over the GPRB. The final CP maps were validated using National Agricultural Statistics Service (NASS) crop yield data. The relationship between CPsat\_adjust and the NASS average corn yield data (r=0.78, 113 samples) is stronger than the relationship between CPnon\_sat\_adjust and the NASS average corn yield data (r=0.67, 113 samples), indicating that the new CPsat\_adjust map reduces the NDVI saturation effects and is in good agreement with the corn yield ground observations. Results demonstrate that the NDVI saturation adjustment approach improves the quality of the original GSN map and better depicts the actual vegetation conditions of the GPRB cropland systems.},
  langid = {english},
  keywords = {Cropland performance,Ecosystem productivity,eMODIS,Greater Platte River Basin,NDVI saturation adjustment,ndvi-saturation,RVI,Vegetation index},
  file = {/home/lukas/Zotero/storage/HTNRCPZ3/Gu et al. - 2013 - NDVI saturation adjustment A new approach for imp.pdf;/home/lukas/Zotero/storage/KZY4EFYW/S1470160X13000757.html}
}

@article{gurungPredictingEnhancedVegetation2009,
  title = {Predicting {{Enhanced Vegetation Index}} ({{EVI}}) Curves for Ecosystem Modeling Applications},
  author = {Gurung, Ram B. and Breidt, F. Jay and Dutin, Amandine and Ogle, Stephen M.},
  date = {2009-10-01},
  journaltitle = {Remote Sensing of Environment},
  shortjournal = {Remote Sensing of Environment},
  volume = {113},
  number = {10},
  pages = {2186--2193},
  issn = {0034-4257},
  doi = {10.1016/j.rse.2009.05.015},
  url = {https://www.sciencedirect.com/science/article/pii/S0034425709001746},
  urldate = {2022-08-31},
  abstract = {Vegetation indices derived from remote sensing data provide information about the variability in stature, growth and vigor of the vegetation across a region, and have been used to model plant processes. For example, the Enhanced Vegetation Index (EVI) provides a measure of greenness of the vegetation that can be used to predict net primary production. However, ecosystem models relying on remote sensing data for EVI or other vegetation indices are limited by the time series of the satellite data record. Our objective was to develop a statistical model to predict EVI in order to extend the time series for modeling applications. To explain the functional behavior of the seasonal EVI curves, a two-stage multiple regression fitting procedure within a semi-parametric mixed effect (SPME) model framework was used. First, a linear mixed effect (LME) model was fitted to the EVI with climate indexes, crop and irrigation information as predictor variables. Second, Penalized B-splines were used to explain the behavior of the smooth residuals, which result from a smooth model fit to the smooth EVI data curve, in order to describe the uncertainty of the EVI curve. Individual models were fit within individual Major Land Resources Areas (MLRAs). Predicted seasonal EVI, derived from our regression equations, showed a strong agreement with the observed EVI and was able to capture the site by site and year by year variation in the EVI curve. Out-of-sample prediction produced excellent results for a majority of the sites, except for sites without clear seasonal patterns, which may have resulted from cloud contamination and/or snow cover. Therefore, given the appropriate climate, crop, and irrigation information, the proposed approach can be used to predict seasonal EVI curves for extending the time series into the past and future.},
  langid = {english},
  keywords = {B-splines,Ecosystem models,Enhanced Vegetation Index,Semi-parametric mixed effect model},
  file = {/home/lukas/Zotero/storage/LI37X95Z/Gurung et al. - 2009 - Predicting Enhanced Vegetation Index (EVI) curves .pdf;/home/lukas/Zotero/storage/58HZRAI9/S0034425709001746.html}
}

@article{hastieGeneralizedAdditiveModels1987,
  title = {Generalized {{Additive Models}}: {{Some Applications}}},
  shorttitle = {Generalized {{Additive Models}}},
  author = {Hastie, Trevor and Tibshirani, Robert},
  date = {1987-06-01},
  journaltitle = {Journal of the American Statistical Association},
  volume = {82},
  number = {398},
  pages = {371--386},
  publisher = {{Taylor \& Francis}},
  issn = {0162-1459},
  doi = {10.1080/01621459.1987.10478440},
  url = {https://www.tandfonline.com/doi/abs/10.1080/01621459.1987.10478440},
  urldate = {2022-07-29},
  abstract = {Generalized additive models have the form η(x) = α + σ fj (x j ), where η might be the regression function in a multiple regression or the logistic transformation of the posterior probability Pr(y = 1 | x) in a logistic regression. In fact, these models generalize the whole family of generalized linear models η(x) = β′x, where η(x) = g(μ(x)) is some transformation of the regression function. We use the local scoring algorithm to estimate the functions fj (xj ) nonparametrically, using a scatterplot smoother as a building block. We demonstrate the models in two different analyses: a nonparametric analysis of covariance and a logistic regression. The procedure can be used as a diagnostic tool for identifying parametric transformations of the covariates in a standard linear analysis. A variety of inferential tools have been developed to aid the analyst in assessing the relevance and significance of the estimated functions: these include confidence curves, degrees of freedom estimates, and approximate hypothesis tests. The local scoring algorithm is analogous to the iterative reweighted least squares algorithm for solving likelihood and nonlinear regression equations. At each iteration, an adjusted dependent variable is formed and an additive regression model is fit using the backfitting algorithm. The backfitting algorithm cycles through the variables and estimates each coordinated function by smoothing the partial residuals.},
  keywords = {gam,Generalized linear model,Logistic regression,Nonparametric regression,Smooth},
  annotation = {\_eprint: https://www.tandfonline.com/doi/pdf/10.1080/01621459.1987.10478440},
  file = {/home/lukas/Zotero/storage/B9L2TA4Q/Hastie and Tibshirani - 1987 - Generalized Additive Models Some Applications.pdf;/home/lukas/Zotero/storage/UJPQ5VWF/01621459.1987.html}
}

@article{henitsSentinel2EnablesNationwide2022,
  title = {Sentinel-2 {{Enables Nationwide Monitoring}} of {{Single Area Payment Scheme}} and {{Greening Agricultural Subsidies}} in {{Hungary}}},
  author = {Henits, László and Szerletics, Ákos and Szokol, Dávid and Szlovák, Gergely and Gojdár, Emese and Zlinszky, András},
  date = {2022-01},
  journaltitle = {Remote Sensing},
  volume = {14},
  number = {16},
  pages = {3917},
  publisher = {{Multidisciplinary Digital Publishing Institute}},
  issn = {2072-4292},
  doi = {10.3390/rs14163917},
  url = {https://www.mdpi.com/2072-4292/14/16/3917},
  urldate = {2022-09-02},
  abstract = {The verification and monitoring of agricultural subsidy claims requires combined evaluation of several criteria at the scale of over a million cultivation units. Sentinel-2 satellite imagery is a promising data source and paying agencies are encouraged to test their pre-operational use. Here, we present the outcome of the Hungarian agricultural subsidy monitoring pilot: our goal was to propose a solution based on open-source components and evaluate the main strengths and weaknesses for Sentinel-2 in the framework of a complex set of tasks. These include the checking of the basic cultivation of grasslands and arable land and compliance to the criteria of ecological focus areas. The processing of the satellite data was conducted based on random forest for crop classification and the detection of cultivation events was conducted based on NDVI (Normalized Differential Vegetation Index) time series analysis results. The outputs of these processes were combined in a decision tree ruleset to provide the final results. We found that crop classification provided good performance (overall accuracy 88\%) for 22 vegetation classes and cultivation detection was also reliable when compared to on-screen visual interpretation. The main limitation was the size of fields, which were frequently small compared to the spatial resolution of the images: more than 4\% of the parcels had to be excluded, although these represent less than 3\% of the cultivated area of Hungary. Based on these results, we find that operational satellite-based monitoring is feasible for Hungary, and expect further improvements from integration with Sentinel-1 due to additional temporal resolution.},
  issue = {16},
  langid = {english},
  keywords = {agriculture monitoring,classification,Common Agricultural Policy,crop classification,machine learning,mowing detection,national scale,random forest,Sentinel-2},
  file = {/home/lukas/Zotero/storage/GZLGPR97/Henits et al. - 2022 - Sentinel-2 Enables Nationwide Monitoring of Single.pdf;/home/lukas/Zotero/storage/Q5ER4CN5/3917.html}
}

@article{holzkamperSpatialTemporalTrends2015,
  title = {Spatial and Temporal Trends in Agro-Climatic Limitations to Production Potentials for Grain Maize and Winter Wheat in {{Switzerland}}},
  author = {Holzkämper, Annelie and Fossati, Dario and Hiltbrunner, Jürg and Fuhrer, Jürg},
  date = {2015-01-01},
  journaltitle = {Regional Environmental Change},
  shortjournal = {Reg Environ Change},
  volume = {15},
  number = {1},
  pages = {109--122},
  issn = {1436-378X},
  doi = {10.1007/s10113-014-0627-7},
  url = {https://doi.org/10.1007/s10113-014-0627-7},
  urldate = {2022-09-07},
  abstract = {Climate is a major driver of agricultural production potentials. To make the best use of these potentials, agricultural management should be adjusted to local climatic conditions. As these conditions change over time, understanding climatic limitations and their trends in time and space is essential for the planning of suitable adaptation measures. In this study, we provide a detailed spatio-temporal analysis of climatic yield potentials for grain maize and winter wheat in Switzerland. We find that current climatic suitability for grain maize is mostly limited by sub-optimal temperatures, radiation and water scarcity, while climatic suitability for winter wheat is mostly limited through excess water, insufficient radiation, as well as frost and heat stress. Over the investigated period from 1983 to 2010, few regional trends in climate suitability were identified for the two crops, indicating that grain maize has benefitted slightly from increasing growth temperatures with recent warming (0.5~°C/decade), while winter wheat suitability decreased slightly due to suboptimal radiation/temperature ratios with warming. Despite only small trends in climate suitabilities, which are restricted to particular regions, future climatic changes could lead to more pronounced shifts. The tendencies of climate limitations identified in this study are mostly consistent with findings from other studies, and it can thus be anticipated that maize may continue to benefit from increasing temperatures on the short term, but may also be increasingly limited by water scarcity as summer precipitation decreases. For winter wheat, the relevance of heat stress is likely to increase with increasing temperatures. These results may help to support short-term adaptation planning. However, more detailed analyses of climate projections will be necessary to investigate “critical transitions” and provide more specific information to support long-term climate change adaptation planning (e.g. for irrigation and breeding programmes).},
  langid = {english},
  keywords = {Agricultural productivity,Climate impacts,Climate limitations,Climate suitability,gdd,Switzerland},
  file = {/home/lukas/Zotero/storage/DH85L6ER/Holzkämper et al. - 2015 - Spatial and temporal trends in agro-climatic limit.pdf}
}

@article{homolovaReviewOpticalbasedRemote2013,
  title = {Review of Optical-Based Remote Sensing for Plant Trait Mapping},
  author = {Homolová, Lucie and Malenovský, Zbyněk and Clevers, Jan G. P. W. and García-Santos, Glenda and Schaepman, Michael E.},
  date = {2013-09-01},
  journaltitle = {Ecological Complexity},
  shortjournal = {Ecological Complexity},
  volume = {15},
  pages = {1--16},
  issn = {1476-945X},
  doi = {10.1016/j.ecocom.2013.06.003},
  url = {https://www.sciencedirect.com/science/article/pii/S1476945X13000524},
  urldate = {2022-03-08},
  abstract = {Plant trait data have been used in various studies related to ecosystem functioning, community ecology, and assessment of ecosystem services. Evidences are that plant scientists agree on a set of key plant traits, which are relatively easy to measure and have a stable and strong predictive response to ecosystem functions. However, the field measurements of plant trait data are still limited to small area, to a certain moment in time and to certain number of species only. Therefore, remote sensing (RS) offers potential to complement or even replace field measurements of some plant traits. It offers instantaneous spatially contiguous information, covers larger areas and in case of satellite observations profits from their revisit capacity. In this review, we first introduce RS concepts of light–vegetation interactions, RS instruments for vegetation studies, RS methods, and scaling between field and RS observations. Further we discuss in detail current achievements and challenges of optical RS for mapping of key plant traits. We concentrate our discussion on three categorical plant traits (plant growth and life forms, flammability properties and photosynthetic pathways and activity) and on five continuous plant traits (plant height, leaf phenology, leaf mass per area, nitrogen and phosphorous concentration or content). We review existing literature to determine the retrieval accuracy of the continuous plant traits. The relative estimation error using RS ranged between 10\% and 45\% of measured mean value, i.e. around 10\% for plant height of tall canopies, 20\% for plant height of short canopies, 15\% for plant nitrogen, 25\% for plant phosphorus content/concentration, and 45\% for leaf mass per area estimates. The potential of RS to map plant traits is particularly high when traits are related to leaf biochemistry, photosynthetic processes and canopy structure. There are also other plant traits, i.e. leaf chlorophyll content, water content and leaf area index, which can be retrieved from optical RS well and can be of importance for plant scientists. We underline the need that future assessments of ecosystem functioning using RS should require comprehensive and integrated measurements of various plant traits together with leaf and canopy spectral properties. By doing so, the interplay between plant structural, physiological, biochemical, phenological and spectral properties can be better understood.},
  langid = {english},
  keywords = {Accuracy,Plant ecology,Plant traits,Remote sensing,Scaling},
  file = {/home/lukas/Zotero/storage/HV52N4LW/Homolová et al. - 2013 - Review of optical-based remote sensing for plant t.pdf;/home/lukas/Zotero/storage/EWFFZN4P/S1476945X13000524.html}
}

@article{huberRobustEstimationLocation1964,
  title = {Robust {{Estimation}} of a {{Location Parameter}}},
  author = {Huber, Peter J.},
  date = {1964},
  journaltitle = {The Annals of Mathematical Statistics},
  volume = {35},
  number = {1},
  eprint = {2238020},
  eprinttype = {jstor},
  pages = {73--101},
  publisher = {{Institute of Mathematical Statistics}},
  issn = {0003-4851},
  abstract = {This paper contains a new approach toward a theory of robust estimation; it treats in detail the asymptotic theory of estimating a location parameter for contaminated normal distributions, and exhibits estimators--intermediaries between sample mean and sample median--that are asymptotically most robust (in a sense to be specified) among all translation invariant estimators. For the general background, see Tukey (1960) (p. 448 ff.) Let x1, ⋯, xn be independent random variables with common distribution function F(t - ξ). The problem is to estimate the location parameter ξ, but with the complication that the prototype distribution F(t) is only approximately known. I shall primarily be concerned with the model of indeterminacy F = (1 - ε)Φ + ε H, where \$0 \textbackslash leqq \textbackslash epsilon {$<$} 1\$ is a known number, Φ(t) = (2π)-1/2 ∫t -∞ exp(-1/2s2) ds is the standard normal cumulative and H is an unknown contaminating distribution. This model arises for instance if the observations are assumed to be normal with variance 1, but a fraction ε of them is affected by gross errors. Later on, I shall also consider other models of indeterminacy, e.g., \$\textbackslash sup\_t |F(t) - \textbackslash Phi(t)| \textbackslash leqq \textbackslash epsilon\$. Some inconvenience is caused by the fact that location and scale parameters are not uniquely determined: in general, for fixed ε, there will be several values of ξ and σ such that \$\textbackslash sup\_t|F(t) - \textbackslash Phi((t - \textbackslash xi)/\textbackslash sigma)| \textbackslash leqq \textbackslash epsilon\$, and similarly for the contaminated case. Although this inherent and unavoidable indeterminacy is small if ε is small and is rather irrelevant for practical purposes, it poses awkward problems for the theory, especially for optimality questions. To remove this difficulty, one may either (i) restrict attention to symmetric distributions, and estimate the location of the center of symmetry (this works for ξ but not for σ); or (ii) one may define the parameter to be estimated in terms of the estimator itself, namely by its asymptotic value for sample size n → ∞; or (iii) one may define the parameters by arbitrarily chosen functionals of the distribution (e.g., by the expectation, or the median of F). All three possibilities have unsatisfactory aspects, and I shall usually choose the variant which is mathematically most convenient. It is interesting to look back to the very origin of the theory of estimation, namely to Gauss and his theory of least squares. Gauss was fully aware that his main reason for assuming an underlying normal distribution and a quadratic loss function was mathematical, i.e., computational, convenience. In later times, this was often forgotten, partly because of the central limit theorem. However, if one wants to be honest, the central limit theorem can at most explain why many distributions occurring in practice are approximately normal. The stress is on the word "approximately." This raises a question which could have been asked already by Gauss, but which was, as far as I know, only raised a few years ago (notably by Tukey): What happens if the true distribution deviates slightly from the assumed normal one? As is now well known, the sample mean then may have a catastrophically bad performance: seemingly quite mild deviations may already explode its variance. Tukey and others proposed several more robust substitutes--trimmed means, Winsorized means, etc.--and explored their performance for a few typical violations of normality. A general theory of robust estimation is still lacking; it is hoped that the present paper will furnish the first few steps toward such a theory. At the core of the method of least squares lies the idea to minimize the sum of the squared "errors," that is, to adjust the unknown parameters such that the sum of the squares of the differences between observed and computed values is minimized. In the simplest case, with which we are concerned here, namely the estimation of a location parameter, one has to minimize the expression ∑i (xi - T)2; this is of course achieved by the sample mean T = ∑i xi/n. I should like to emphasize that no loss function is involved here; I am only describing how the least squares estimator is defined, and neither the underlying family of distributions nor the true value of the parameter to be estimated enters so far. It is quite natural to ask whether one can obtain more robustness by minimizing another function of the errors than the sum of their squares. We shall therefore concentrate our attention to estimators that can be defined by a minimum principle of the form (for a location parameter): T = Tn(x1, ⋯, xn) minimizes ∑i ρ(xi - T), \textbackslash begin\{equation*\} \textbackslash tag\{M\} where \textbackslash rho is a non-constant function. \textbackslash end\{equation*\} Of course, this definition generalizes at once to more general least squares type problems, where several parameters have to be determined. This class of estimators contains in particular (i) the sample mean (ρ(t) = t2), (ii) the sample median (ρ(t) = |t|), and more generally, (iii) all maximum likelihood estimators (ρ(t) = -log f(t), where f is the assumed density of the untranslated distribution). These (M)-estimators, as I shall call them for short, have rather pleasant asymptotic properties; sufficient conditions for asymptotic normality and an explicit expression for their asymptotic variance will be given. How should one judge the robustness of an estimator Tn(x) = Tn(x1, ⋯, xn)? Since ill effects from contamination are mainly felt for large sample sizes, it seems that one should primarily optimize large sample robustness properties. Therefore, a convenient measure of robustness for asymptotically normal estimators seems to be the supremum of the asymptotic variance (n → ∞) when F ranges over some suitable set of underlying distributions, in particular over the set of all F = (1 - ε)Φ + ε H for fixed ε and symmetric H. On second thought, it turns out that the asymptotic variance is not only easier to handle, but that even for moderate values of n it is a better measure of performance than the actual variance, because (i) the actual variance of an estimator depends very much on the behavior of the tails of H, and the supremum of the actual variance is infinite for any estimator whose value is always contained in the convex hull of the observations. (ii) If an estimator is asymptotically normal, then the important central part of its distribution and confidence intervals for moderate confidence levels can better be approximated in terms of the asymptotic variance than in terms of the actual variance. If we adopt this measure of robustness, and if we restrict attention to (M)-estimators, then it will be shown that the most robust estimator is uniquely determined and corresponds to the following ρ:ρ(t) = 1/2t2 for \$|t| {$<$} k, \textbackslash rho(t) = k|t| - \textbackslash frac\{1\}\{2\}k\^2\$ for |t| ≥ k, with k depending on ε. This estimator is most robust even among all translation invariant estimators. Sample mean (k = ∞) and sample median (k = 0) are limiting cases corresponding to ε = 0 and ε = 1, respectively, and the estimator is closely related and asymptotically equivalent to Winsorizing. I recall the definition of Winsorizing: assume that the observations have been ordered, x1 ≤ x2 ≤ ⋯ ≤ xn, then the statistic T = n-1(gxg + 1 + xg + 1 + xg + 2 + ⋯ + xn - h + hxn - h) is called the Winsorized mean, obtained by Winsorizing the g leftmost and the h rightmost observations. The above most robust (M)-estimators can be described by the same formula, except that in the first and in the last summand, the factors xg + 1 and xn - h have to be replaced by some numbers u, v satisfying xg ≤ u ≤ xg + 1 and xn - h ≤ v ≤ xn - h + 1, respectively; g, h, u and v depend on the sample. In fact, this (M)-estimator is the maximum likelihood estimator corresponding to a unique least favorable distribution F0 with density f0(t) = (1 - ε)(2π)-1/2e-ρ(t). This f0 behaves like a normal density for small t, like an exponential density for large t. At least for me, this was rather surprising--I would have expected an f0 with much heavier tails. This result is a particular case of a more general one that can be stated roughly as follows: Assume that F belongs to some convex set C of distribution functions. Then the most robust (M)-estimator for the set C coincides with the maximum likelihood estimator for the unique F0 ε C which has the smallest Fisher information number I(F) = ∫ (f'/f)2f dt among all F ε C. Miscellaneous related problems will also be treated: the case of non-symmetric contaminating distributions; the most robust estimator for the model of indeterminacy \$\textbackslash sup\_t|F(t) - \textbackslash Phi(t)| \textbackslash leqq \textbackslash epsilon\$; robust estimation of a scale parameter; how to estimate location, if scale and ε are unknown; numerical computation of the estimators; more general estimators, e.g., minimizing \$\textbackslash sum\_\{i {$<$} j\} \textbackslash rho(x\_i - T, x\_j - T)\$, where ρ is a function of two arguments. Questions of small sample size theory will not be touched in this paper.},
  keywords = {huber},
  file = {/home/lukas/Zotero/storage/DX3LYDJC/Huber - 1964 - Robust Estimation of a Location Parameter.pdf}
}

@article{huntHighResolutionWheat2019,
  title = {High Resolution Wheat Yield Mapping Using {{Sentinel-2}}},
  author = {Hunt, Merryn L. and Blackburn, George Alan and Carrasco, Luis and Redhead, John W. and Rowland, Clare S.},
  date = {2019-11-01},
  journaltitle = {Remote Sensing of Environment},
  shortjournal = {Remote Sensing of Environment},
  volume = {233},
  pages = {111410},
  issn = {0034-4257},
  doi = {10.1016/j.rse.2019.111410},
  url = {https://www.sciencedirect.com/science/article/pii/S0034425719304298},
  urldate = {2022-03-08},
  abstract = {Accurate crop yield estimates are important for governments, farmers, scientists and agribusiness. This paper provides a novel demonstration of the use of freely available Sentinel-2 data to estimate within-field wheat yield variability in a single year. The impact of data resolution and availability on yield estimation is explored using different combinations of input data. This was achieved by combining Sentinel-2 with environmental data (e.g. meteorological, topographical, soil moisture) for different periods throughout the growing season. Yield was estimated using Random Forest (RF) regression models. They were trained and validated using a dataset containing over 8000 points collected by combine harvester yield monitors from 39 wheat fields in the UK. The results demonstrate that it is possible to produce accurate maps of within-field yield variation at 10\,m resolution using Sentinel-2 data (RMSE 0.66\,t/ha). When combined with environmental data further improvements in accuracy can be obtained (RMSE 0.61\,t/ha). We demonstrate that with knowledge of crop-type distribution it is possible to use these models, trained with data from a few fields, to estimate within-field yield variability on a landscape scale. Applying this method gives us a range of crop yield across the landscape of 4.09 to 12.22\,t/ha, with a total crop production of approx. 289,000\,t.},
  langid = {english},
  keywords = {Combine harvester,Random forest regression,Sentinel-2,Yield estimation,Yield mapping},
  file = {/home/lukas/Zotero/storage/5TCNJSIA/Hunt et al. - 2019 - High resolution wheat yield mapping using Sentinel.pdf;/home/lukas/Zotero/storage/BYJGGTJN/S0034425719304298.html}
}

@inproceedings{jaramazESASentinel2Mission2013,
  title = {The {{ESA Sentinel-2}} Mission {{Vegetation}} Variables for {{Remote}} Sensing of {{Plant}} Monitoring},
  author = {Jaramaz, Darko and Perović, Veljko and Belanovic Simic, Snezana and Saljnikov, Elmira and Cakmak, Dragan and Mrvić, Vesna and Zivotic, Ljubomir},
  date = {2013-05-22},
  abstract = {Remote sensing is a convenient method for plant monitoring by employing vegetation variables which is changed dynamically in time and space. Furthermore, in the last few decades the number and quality of information that can be derived from remotely sensed images is rapidly increased. The widespread usage of information from satellite-born sensors provides a new tool for studying the biophysical properties of vegetation by mapping vegetation resources and changes that arise over extended periods of time. The European Space Agency (ESA) Sentinel-2 mission, as a part of the European Global Monitoring for Environment (GMES) initiative, is a polar-orbiting, superspectral high resolution imaging mission that envisaged flying a pair of satellites; the first planned to launch in 2013. The Sentinel-2 mission combine a large swath, frequent revisit and systematic acquisition of high-spatial resolution land surfaces imagery with a large number of spectral bands; that will provide accurate and easily accessible data for the environmental management. The most important impact of Sentinel-2 for plant monitoring is expected to be the improved plant parameters: Fraction of Vegetation Cover (FVC), Leaf Area Index (LAI), Leaf Chlorophyll Content (Cab), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and Leaf Water Content (Cw); by incorporating three new spectral bands in the red-edge region, which are centered at 705, 740 and 783 nm. The goal is to improve site-specific plant management by the retrieving different plant parameters as an input for management measures aimed to non-destructive monitoring of plant development and the detection of many environmental stresses, which can limit plant productivity.},
  keywords = {s2,sentinel},
  file = {/home/lukas/Zotero/storage/49VHQQAP/Jaramaz et al. - 2013 - The ESA Sentinel-2 mission Vegetation variables fo.pdf}
}

@article{kamirEstimatingWheatYields2020,
  title = {Estimating Wheat Yields in {{Australia}} Using Climate Records, Satellite Image Time Series and Machine Learning Methods},
  author = {Kamir, Elisa and Waldner, François and Hochman, Zvi},
  date = {2020-02-01},
  journaltitle = {ISPRS Journal of Photogrammetry and Remote Sensing},
  shortjournal = {ISPRS Journal of Photogrammetry and Remote Sensing},
  volume = {160},
  pages = {124--135},
  issn = {0924-2716},
  doi = {10.1016/j.isprsjprs.2019.11.008},
  url = {https://www.sciencedirect.com/science/article/pii/S092427161930262X},
  urldate = {2022-03-08},
  abstract = {Closing the yield gap between actual and potential wheat yields in Australia is important to meet the growing global demand for food. The identification of hotspots of the yield gap, where the potential for improvement is the greatest, is a necessary step towards this goal. While crop growth models are well suited to quantify potential yields, they lack the ability to provide accurate large-scale estimates of actual yields, owing to the sheer quantity of data they require for parameterisation. In this context, we sought to provide accurate estimates of actual wheat yields across the Australian wheat belt based on machine-learning regression methods, climate records and satellite image time series. Out of nine base learners and two ensembles, support vector regression with radial basis function emerged as the single best learner (root mean square error of 0.55\,t\,ha−1 and R2 of 0.77 at the pixel level). At national scale, this model explained 73\% of the yield variability observed across statistical units. Benchmark approaches based on peak Normalised Difference Vegetation Index (NDVI) and on a harvest index were largely outperformed by the machine-learning regression models (R2\,{$<$}\,0.46). Climate variables such as maximum temperatures and accumulated rainfall provided additional information to the 16-day NDVI time series as they significantly improved yield predictions. Variables observed up to and around the flowering period had a particularly high predictive power with additional information gained from data during grain filling. We further showed that, while all models were sensitive to a reduction of the training set size, a large majority had not reached saturation with a data set of 125 fields (2000\,pixels). This indicates that additional training data are likely to further improve the skill of the models. We estimated that observations from 75 fields (1200\,pixels) are required for the best single model to reach an R2 of 0.7. We contend that machine-learning regression methods applied to climate and satellite image time series can achieve reliable crop yield monitoring across years at both the pixel and the country scale. The resulting yield estimates meet the accuracy requirements for mapping the yield gap and identifying yield gap hotspots which could be targeted for further work by agricultural researchers and advisers.},
  langid = {english},
  keywords = {Actual yield,Agriculture,Machine learning,National scale,Regression,Remote sensing,Yield Gap},
  file = {/home/lukas/Zotero/storage/SUAYDGJ7/S092427161930262X.html}
}

@article{liemohnRMSENotEnough2021,
  title = {{{RMSE}} Is Not Enough: {{Guidelines}} to Robust Data-Model Comparisons for Magnetospheric Physics},
  shorttitle = {{{RMSE}} Is Not Enough},
  author = {Liemohn, Michael W. and Shane, Alexander D. and Azari, Abigail R. and Petersen, Alicia K. and Swiger, Brian M. and Mukhopadhyay, Agnit},
  date = {2021-07-01},
  journaltitle = {Journal of Atmospheric and Solar-Terrestrial Physics},
  shortjournal = {Journal of Atmospheric and Solar-Terrestrial Physics},
  volume = {218},
  pages = {105624},
  issn = {1364-6826},
  doi = {10.1016/j.jastp.2021.105624},
  url = {https://www.sciencedirect.com/science/article/pii/S1364682621000857},
  urldate = {2022-09-09},
  abstract = {The magnetospheric physics research community uses a broad array of quantitative data-model comparison methods (metrics) when conducting their research investigations. It is often the case, though, that any particular study will only use one or two metrics, with the two most common being Pearson correlation coefficient and root mean square error (RMSE). Because metrics are designed to test a specific aspect of the data-model relationship, limiting the comparison to only one or two metrics reduces the physical insights that can be gleaned from the analysis, restricting the possible findings from modeling studies. Additional physical insights can be obtained when many types of metrics are applied. We organize metrics into two primary groups: 1) fit performance metrics, often based on the data-model value difference; and 2) event detection metrics, which use a discrete event classification of data and model values determined by a specified threshold. In addition to these groups, there are several major categories of metrics based on the aspect of the data-model relationship that the metric assesses: 1) accuracy; 2) bias; 3) precision; 4) association; 5) and extremes. Another category is skill, which is a measure of any of these metrics against the performance of a reference model. These can be applied to a subset of either the data or the model values, known as reliability and discrimination assessments. In the context of magnetospheric physics examples, we discuss best practices for choosing metrics for particular studies.},
  langid = {english},
  keywords = {Data-model comparisons,Event detection,Fit performance,Forecasting,Magnetospheric physics,Metrics,rmse,Space weather},
  file = {/home/lukas/Zotero/storage/SD2NSXNU/Liemohn et al. - 2021 - RMSE is not enough Guidelines to robust data-mode.pdf;/home/lukas/Zotero/storage/VKAEKFAK/S1364682621000857.html}
}

@article{liHighqualityVegetationIndex2021,
  title = {High-Quality Vegetation Index Product Generation: {{A}} Review of {{NDVI}} Time Series Reconstruction Techniques},
  shorttitle = {High-Quality Vegetation Index Product Generation},
  author = {Li, Shuang and Xu, Liang and Jing, Yinghong and Yin, Hang and Li, Xinghua and Guan, Xiaobin},
  date = {2021-12-25},
  journaltitle = {International Journal of Applied Earth Observation and Geoinformation},
  shortjournal = {International Journal of Applied Earth Observation and Geoinformation},
  volume = {105},
  pages = {102640},
  issn = {0303-2434},
  doi = {10.1016/j.jag.2021.102640},
  url = {https://www.sciencedirect.com/science/article/pii/S0303243421003470},
  urldate = {2022-04-12},
  abstract = {Normalized difference vegetation index (NDVI) derived from satellites has been ubiquitously utilized in the field of remote sensing. Nevertheless, there are multitudinous contaminations in NDVI time series because of the atmospheric disturbance, cloud cover, sensor failure, and so on. It is crucial to remove the noises prior to further applications. Numerous techniques have been proposed to alleviate this issue in the last few decades. To the best of our knowledge, there hasn’t been a systematical study to summarize and analyze the status of NDVI time series reconstruction techniques since 1980s. As a result, our goal is to recapitulate the current approaches for reconstructing high-quality NDVI time series, followed by an interpretation on the principle, merits and demerits of different kinds of methods. They were mainly classified into temporal-based methods, frequency-based methods and hybrid methods. The evaluation approaches on the quality of NDVI reconstruction were introduced, accompanied with the future development tendency.},
  langid = {english},
  keywords = {High-quality data,NDVI time series,Reconstruction,Spatio-temporal},
  file = {/home/lukas/Zotero/storage/NS95Y2AI/Li et al. - 2021 - High-quality vegetation index product generation .pdf;/home/lukas/Zotero/storage/B8SMRPJE/S0303243421003470.html}
}

@unpublished{lycheSplineMethods2005,
  title = {Spline {{Methods}}},
  author = {Lyche, Tom and Morken, Knut},
  date = {2005-01-05},
  location = {{University of Oslo}},
  url = {https://www.uio.no/studier/emner/matnat/ifi/nedlagte-emner/INF-MAT5340/v05/undervisningsmateriale},
  urldate = {2022-04-01},
  langid = {english},
  keywords = {BSplines},
  file = {/home/lukas/Zotero/storage/MC542TKZ/hele.pdf;/home/lukas/Zotero/storage/IWFMTTZF/undervisningsmateriale.html}
}

@article{mcmasterGrowingDegreedaysOne1997,
  title = {Growing Degree-Days: One Equation, Two Interpretations},
  shorttitle = {Growing Degree-Days},
  author = {McMaster, Gregory S. and Wilhelm, W. W.},
  date = {1997-12-01},
  journaltitle = {Agricultural and Forest Meteorology},
  shortjournal = {Agricultural and Forest Meteorology},
  volume = {87},
  number = {4},
  pages = {291--300},
  issn = {0168-1923},
  doi = {10.1016/S0168-1923(97)00027-0},
  url = {https://www.sciencedirect.com/science/article/pii/S0168192397000270},
  urldate = {2022-07-28},
  abstract = {Heat units, expressed in growing degree-days (GDD), are frequently used to describe the timing of biological processes. The basic equation used is GDD = [(TMAX + TMIN)2]−TBASE, where TMAX and TMIN are daily maximum and minimum air temperature, respectively, and TBASE is the base temperature. Two methods of interpreting this equation for calculating GDD are: (1) if the daily mean temperature is less than the base, it is set equal to the base temperature, or (2) if TMAX or TMIN {$<$} TBASE they are reset equal to TBASE. The objective of this paper is to show the differences which can result from using these two methods to estimate GDD, and make researchers and practitioners aware of the need to report clearly which method was used in the calculations. Although percent difference between methods of calculation are dependent on TMAX and TMIN data used to compute GDD, our comparisons have produced differences up to 83\% when using a 0°C base for wheat (Triticum aestivum L.). Greater differences were found for corn (Zea mays L.) when using a base temperature of 10°C. Differences between the methods occur if only TMIN is less than TBASE, and then Method 1 accumulates fewer GDD than Method 2. When incorporating an upper threshold, as commonly done with corn, there was a greater difference between the two methods. Not recognizing the discrepancy between methods can result in confusion and add error in quantifying relationships between heat unit accumulation and timing of events in crop development and growth, particularly in crop simulation models. This paper demonstrates the need for authors to clearly communicate the method of calculating GDD so others can correctly interpret and apply reported results.},
  langid = {english},
  keywords = {Corn,Crop simulation model,gdd,Growing degree-days,Timing of biological processes},
  file = {/home/lukas/Zotero/storage/4WAARLD9/McMaster and Wilhelm - 1997 - Growing degree-days one equation, two interpretat.pdf;/home/lukas/Zotero/storage/EYZ32N63/S0168192397000270.html}
}

@article{millerUsingGrowingDegree2018,
  title = {Using {{Growing Degree Days}} to {{Predict Plant Stages}}},
  author = {Miller, Perry and Lanier, Will and Brandt, Stu},
  date = {2018-07-01},
  shortjournal = {MontGuide},
  langid = {english},
  file = {/home/lukas/Zotero/storage/DIH5RAG2/Bailey - Using Growing Degree Days to Predict Plant Stages.pdf}
}

@article{omoriAssessmentPaddyFields2021,
  title = {Assessment of Paddy Fields’ Damage Caused by {{Cyclone Nargis}} Using {{MODIS}} Time-Series Images (2004–2013)},
  author = {Omori, Keisuke and Sakai, Toru and Miyamoto, Jun and Itou, Akihiko and Oo, Aung Naing and Hirano, Akira},
  date = {2021-04-01},
  journaltitle = {Paddy and Water Environment},
  shortjournal = {Paddy Water Environ},
  volume = {19},
  number = {2},
  pages = {271--281},
  issn = {1611-2504},
  doi = {10.1007/s10333-020-00829-0},
  url = {https://doi.org/10.1007/s10333-020-00829-0},
  urldate = {2022-08-31},
  abstract = {The Ayeyarwady Delta in the Bay of Bengal, the rice bowl of Myanmar, depends on natural conditions, especially rainfall. During the dry season, the delta’s coastal zone experiences saline water intrusion due to its low-lying topography. On May 2, 2008, Cyclone Nargis made landfall and crossed Ayeyarwady Region and Yangon City, affecting more than 50 townships and causing massive destruction of personal property and natural ecosystems. There is no doubt that Nargis caused an unprecedented large-scale disaster, but there is no objective method to quantify crop yield and salinity damage in the delta post-Nargis. The purpose of this study, therefore, is to clarify the changes in vegetation in paddy fields in the Ayeyarwady Delta using Moderate Resolution Imaging Spectroradiometer data pre- and post-Nargis and determine whether this method can be applied to measure crop and salinity damage. The study used daily composite data at a 250-m resolution (MOD09GQ, collection 6) from 2004 to 2013 and calculated NDVI and salinity indices smoothed by locally weighted regression (Lowess). Based on the results of our studies, NDVI peak value in 2008 was lower by 19\% compared to 2007 data, and that the NDVI peak values declined for three straight years since May 2008 when Nargis struck. However, salinity damage evaluation pre- and post-Nargis (using the salinity index equation) showed that soil electrical conductivity did not tend to move up in the post-Nargis dry season (2009), indicating that the decrease in NDVI values was not due to salinity damage.},
  langid = {english},
  keywords = {Ayeyarwady delta,Cyclone Nargis,loess,MODIS,NDVI,Salinity index},
  file = {/home/lukas/Zotero/storage/SFG7LSKF/Omori et al. - 2021 - Assessment of paddy fields’ damage caused by Cyclo.pdf}
}

@article{perez-valenciaTwostageApproachSpatiotemporal2022,
  title = {A Two-Stage Approach for the Spatio-Temporal Analysis of High-Throughput Phenotyping Data},
  author = {Pérez-Valencia, Diana M. and Rodríguez-Álvarez, María Xosé and Boer, Martin P. and Kronenberg, Lukas and Hund, Andreas and Cabrera-Bosquet, Llorenç and Millet, Emilie J. and van Eeuwijk, Fred A.},
  date = {2022-02-24},
  journaltitle = {Scientific Reports},
  shortjournal = {Sci Rep},
  volume = {12},
  number = {1},
  pages = {1--16},
  publisher = {{Nature Publishing Group}},
  issn = {2045-2322},
  doi = {10.1038/s41598-022-06935-9},
  url = {https://www.nature.com/articles/s41598-022-06935-9},
  urldate = {2022-03-18},
  abstract = {High throughput phenotyping (HTP) platforms and devices are increasingly used for the characterization of growth and developmental processes for large sets of plant genotypes. Such HTP data require challenging statistical analyses in which longitudinal genetic signals need to be estimated against a background of spatio-temporal noise processes. We propose a two-stage approach for the analysis of such longitudinal HTP data. In a first stage, we correct for design features and spatial trends per time point. In a second stage, we focus on the longitudinal modelling of the spatially corrected data, thereby taking advantage of shared longitudinal features between genotypes and plants within genotypes. We propose a flexible hierarchical three-level P-spline growth curve model, with plants/plots nested in genotypes, and genotypes nested in populations. For selection of genotypes in a plant breeding context, we show how to extract new phenotypes, like growth rates, from the estimated genotypic growth curves and their first-order derivatives. We illustrate our approach on HTP data from the PhenoArch greenhouse platform at INRAE Montpellier and the outdoor Field Phenotyping platform at ETH Zürich.},
  issue = {1},
  langid = {english},
  keywords = {Agricultural genetics,Plant breeding,Statistics},
  file = {/home/lukas/Zotero/storage/CFQLUFYJ/Pérez-Valencia et al. - 2022 - A two-stage approach for the spatio-temporal analy.pdf;/home/lukas/Zotero/storage/9CKTY9UD/s41598-022-06935-9.html}
}

@article{perichCropNitrogenRetrieval2021,
  title = {Crop {{Nitrogen Retrieval Methods}} for {{Simulated Sentinel-2 Data Using In-Field Spectrometer Data}}},
  author = {Perich, Gregor and Aasen, Helge and Verrelst, Jochem and Argento, Francesco and Walter, Achim and Liebisch, Frank},
  date = {2021-01},
  journaltitle = {Remote Sensing},
  volume = {13},
  number = {12},
  pages = {2404},
  publisher = {{Multidisciplinary Digital Publishing Institute}},
  issn = {2072-4292},
  doi = {10.3390/rs13122404},
  url = {https://www.mdpi.com/2072-4292/13/12/2404},
  urldate = {2022-09-02},
  abstract = {Nitrogen (N) is one of the key nutrients supplied in agricultural production worldwide. Over-fertilization can have negative influences on the field and the regional level (e.g., agro-ecosystems). Remote sensing of the plant N of field crops presents a valuable tool for the monitoring of N flows in agro-ecosystems. Available data for validation of satellite-based remote sensing of N is scarce. Therefore, in this study, field spectrometer measurements were used to simulate data of the Sentinel-2 (S2) satellites developed for vegetation monitoring by the ESA. The prediction performance of normalized ratio indices (NRIs), random forest regression (RFR) and Gaussian processes regression (GPR) for plant-N-related traits was assessed on a diverse real-world dataset including multiple crops, field sites and years. The plant N traits included the mass-based N measure, N concentration in the biomass (Nconc), and an area-based N measure approximating the plant N uptake (NUP). Spectral indices such as normalized ratio indices (NRIs) performed well, but the RFR and GPR methods outperformed the NRIs. Key spectral bands for each trait were identified using the RFR variable importance measure and the Gaussian processes regression band analysis tool (GPR-BAT), highlighting the importance of the short-wave infrared (SWIR) region for estimation of plant Nconc—and to a lesser extent the NUP. The red edge (RE) region was also important. The GPR-BAT showed that five bands were sufficient for plant N trait and leaf area index (LAI) estimation and that a surplus of bands effectively reduced prediction performance. A global sensitivity analysis (GSA) was performed on all traits simultaneously, showing the dominance of the LAI in the mixed remote sensing signal. To delineate the plant-N-related traits from this signal, regional and/or national data collection campaigns producing large crop spectral libraries (CSL) are needed. An improved database will likely enable the mapping of N at the agro-ecosystem level or for use in precision farming by farmers in the future.},
  issue = {12},
  langid = {english},
  keywords = {agro-ecosystem monitoring,ARTMO toolbox,chlorophyll,gaussian processes regression,leaf area index,nitrogen,random forest,spectral indices},
  file = {/home/lukas/Zotero/storage/Q99KPLWQ/Perich et al. - 2021 - Crop Nitrogen Retrieval Methods for Simulated Sent.pdf;/home/lukas/Zotero/storage/H4WKZ92X/2404.html}
}

@unpublished{perichPixelbasedCropYield2022,
  title = {Pixel-Based Crop Yield Mapping and Prediction Using Spectral Indices and Neural Networks on {{Sentinel-2}} Time Series Data},
  author = {Perich, Gregor and Turkoglu, Mehmet Ozgur and Graf, Lukas Valentin and Wegner, Jan Dirk and Aasen, Helge and Walter, Achim and Liebisch, Frank},
  date = {2022-07-18},
  langid = {english},
  file = {/home/lukas/Zotero/storage/GCPPK5LM/Perich et al. - Pixel-based yield mapping and prediction from Sent.pdf}
}

@article{perichPixelbasedYieldMapping2022,
  title = {Pixel-Based Yield Mapping and Prediction from {{Sentinel-2}} Using Spectral Indices and Neural Networks},
  shorttitle = {Pixel-Based Yield Mapping and Prediction from {{Sentinel-2}} Using Spectral Indices and Neural Networks},
  author = {Perich, Gregor and Turkoglu, Mehmet Ozgur and Graf, Lukas Valentin and Wegner, Jan Dirk and Aasen, Helge and Walter, Achim and Liebisch, Frank},
  date = {2022-07-18},
  journaltitle = {Preprint submitted to Field Crops Research},
  shortjournal = {---},
  series = {A},
  volume = {0},
  number = {0},
  pages = {0},
  issn = {000ISN000},
  doi = {000DOI000},
  url = {C},
  abstract = {---}
}

@unpublished{perichPixelbasedYieldMapping2022a,
  title = {Pixel-Based Yield Mapping and Prediction from {{Sentinel-2}} Using Spectral Indices and Neural Networks},
  author = {Perich, Gregor and Turkoglu, Mehmet Ozgur and Graf, Lukas Valentin and Wegner, Jan Dirk and Aasen, Helge and Walter, Achim and Liebisch, Frank},
  date = {2022-07-18},
  abstract = {B},
  howpublished = {A},
  langid = {english},
  annotation = {D},
  file = {/home/lukas/Zotero/storage/BBFCEFA7/Perich et al. - Pixel-based yield mapping and prediction from Sent.pdf}
}

@article{raiyaniSentinel2ImageScene2021,
  title = {Sentinel-2 {{Image Scene Classification}}: {{A Comparison}} between {{Sen2Cor}} and a {{Machine Learning Approach}}},
  shorttitle = {Sentinel-2 {{Image Scene Classification}}},
  author = {Raiyani, Kashyap and Gonçalves, Teresa and Rato, Luís and Salgueiro, Pedro and Marques da Silva, José R.},
  date = {2021-01},
  journaltitle = {Remote Sensing},
  volume = {13},
  number = {2},
  pages = {300},
  publisher = {{Multidisciplinary Digital Publishing Institute}},
  issn = {2072-4292},
  doi = {10.3390/rs13020300},
  url = {https://www.mdpi.com/2072-4292/13/2/300},
  urldate = {2022-08-20},
  abstract = {Given the continuous increase in the global population, the food manufacturers are advocated to either intensify the use of cropland or expand the farmland, making land cover and land usage dynamics mapping vital in the area of remote sensing. In this regard, identifying and classifying a high-resolution satellite imagery scene is a prime challenge. Several approaches have been proposed either by using static rule-based thresholds (with limitation of diversity) or neural network (with data-dependent limitations). This paper adopts the inductive approach to learning from surface reflectances. A manually labeled Sentinel-2 dataset was used to build a Machine Learning (ML) model for scene classification, distinguishing six classes (Water, Shadow, Cirrus, Cloud, Snow, and Other). This models was accessed and further compared to the European Space Agency (ESA) Sen2Cor package. The proposed ML model presents a Micro-F1 value of 0.84, a considerable improvement when compared to the Sen2Cor corresponding performance of 0.59. Focusing on the problem of optical satellite image scene classification, the main research contributions of this paper are: (a) an extended manually labeled Sentinel-2 database adding surface reflectance values to an existing dataset; (b) an ensemble-based and a Neural-Network-based ML models; (c) an evaluation of model sensitivity, biasness, and diverse ability in classifying multiple classes over different geographic Sentinel-2 imagery, and finally, (d) the benchmarking of the ML approach against the Sen2Cor package.},
  issue = {2},
  langid = {english},
  keywords = {artificial intelligence,high-resolution imagery,machine learning,scene classification,Sen2Cor,sentinel,Sentinel-2,surface reflectance},
  file = {/home/lukas/Zotero/storage/PQXI69MJ/Raiyani et al. - 2021 - Sentinel-2 Image Scene Classification A Compariso.pdf;/home/lukas/Zotero/storage/PEAJWQFH/300.html}
}

@online{ripleyFitSmoothingSpline2022,
  title = {R: {{Fit}} a {{Smoothing Spline}}},
  author = {Ripley, B. D. and Maechler, Martin},
  date = {2022-08-19},
  url = {https://stat.ethz.ch/R-manual/R-patched/library/stats/html/smooth.spline.html},
  urldate = {2022-08-15},
  keywords = {R-smoothingspline},
  file = {/home/lukas/Zotero/storage/F3EGCKXW/smooth.spline.html}
}

@article{robinsonComparingPerformanceTechniques2005,
  title = {Comparing the Performance of Techniques to Improve the Quality of Yield Maps},
  author = {Robinson, T. P. and Metternicht, G.},
  date = {2005-07-01},
  journaltitle = {Agricultural Systems},
  shortjournal = {Agricultural Systems},
  volume = {85},
  number = {1},
  pages = {19--41},
  issn = {0308-521X},
  doi = {10.1016/j.agsy.2004.07.010},
  url = {https://www.sciencedirect.com/science/article/pii/S0308521X04001404},
  urldate = {2022-09-08},
  abstract = {Yield maps contain a wealth of information and can be an important tool for making informed decisions on paddock management. However, yield datasets obtained from combine harvesters often have many errors arising from a variety of sources. It is therefore important to attempt to rectify as many of these errors as possible so that the yield map represents the true yield as accurately as possible, rather than some systematic or operator error. This research defines the most significant errors associated with raw yield datasets and presents and applies a methodology for dealing with the unknown crop width, the time lag of grain, inappropriate GPS recordings, yield surges, and other outlying values. In total, 16.6\% of the original yield dataset acquired over a 96 ha paddock located in the wheat belt of Western Australia was removed because they exhibited one or more of the aforementioned errors. The amount of uncertainty in the filtered dataset was substantially reduced. The accuracy of three spatial interpolation techniques was assessed over the entire paddock using the root mean squared error (RMSE). Finally, it is discussed that the map used to assist paddock management should be chosen depending on how much smoothing and data aggregation is desirable and allowable, not simply on the RMSE statistic.},
  langid = {english},
  keywords = {combine-harvester,Errors,Filtering,Precision agriculture,Spatial interpolation,Yield maps},
  file = {/home/lukas/Zotero/storage/RZ2K4CHW/Robinson and Metternicht - 2005 - Comparing the performance of techniques to improve.pdf;/home/lukas/Zotero/storage/GWZUDWI6/S0308521X04001404.html}
}

@report{rouseMonitoringVernalAdvancement1974,
  title = {Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation},
  author = {Rouse, J. W.},
  date = {1974-05-27},
  number = {NASA-CR-139243},
  url = {https://ntrs.nasa.gov/citations/19740022555},
  urldate = {2022-08-19},
  abstract = {The author has identified the following significant results. From the data that have been analyzed thus far, it is apparent that certain parameters of the vegetation (green biomass and vegetation moisture content) are well correlated with ERTS data at some sites, yet the relationship is poor at other locations. The analyses indicate that the green wave development (spring) can be readily detected in the Great Plains Corridor. Consequently, ERTS-1 data provide a new tool for monitoring range readiness on a regional basis. The onset of summer drought and its duration can also be monitored. Data from ten G.P.C. sites suggest that the satellite coverage has been adequate to monitor the status of rangeland vegetation for regional management purposes.},
  keywords = {GEOPHYSICS,NDVI},
  annotation = {NTRS Author Affiliations: Texas A\&M Univ. NTRS Document ID: 19740022555 NTRS Research Center: Legacy CDMS (CDMS)},
  file = {/home/lukas/Zotero/storage/YDB9TU5F/Rouse - 1974 - Monitoring the vernal advancement and retrogradati.pdf;/home/lukas/Zotero/storage/MLUGUU6G/19740022555.html}
}

@book{sahajpalOptimizingCropCut2020,
  title = {Optimizing {{Crop Cut Collection}} for {{Determining Field-Scale Yields}} in an {{Insurance Context}}},
  author = {Sahajpal, Ritvik and Becker-Reshef, Inbal and Coutu, Sylvain},
  date = {2020-11-10},
  doi = {10.31223/X5J59H},
  file = {/home/lukas/Zotero/storage/I4VSKHI7/Sahajpal et al. - 2020 - Optimizing Crop Cut Collection for Determining Fie.pdf}
}

@article{savitzkySmoothingDifferentiationData1964,
  title = {Smoothing and {{Differentiation}} of {{Data}} by {{Simplified Least Squares Procedures}}.},
  author = {Savitzky, Abraham. and Golay, M. J. E.},
  date = {1964-07-01},
  journaltitle = {Analytical Chemistry},
  shortjournal = {Anal. Chem.},
  volume = {36},
  number = {8},
  pages = {1627--1639},
  publisher = {{American Chemical Society}},
  issn = {0003-2700},
  doi = {10.1021/ac60214a047},
  url = {https://doi.org/10.1021/ac60214a047},
  urldate = {2022-04-01},
  file = {/home/lukas/Zotero/storage/ADSDG5PJ/Savitzky and Golay - 1964 - Smoothing and Differentiation of Data by Simplifie.pdf;/home/lukas/Zotero/storage/CISYLUNA/ac60214a047.html}
}

@article{schaferWhatSavitzkyGolayFilter2011,
  title = {What {{Is}} a {{Savitzky-Golay Filter}}? [{{Lecture Notes}}]},
  shorttitle = {What {{Is}} a {{Savitzky-Golay Filter}}?},
  author = {Schafer, Ronald W.},
  date = {2011-07},
  journaltitle = {IEEE Signal Processing Magazine},
  volume = {28},
  number = {4},
  pages = {111--117},
  issn = {1558-0792},
  doi = {10.1109/MSP.2011.941097},
  abstract = {This article has attempted to answer the question "What is a Savitky-Golay filter?" in terms that will be familiar to the DSP community and readers of IEEE Signal Processing Magazine. This article reviewed the definition and properties of S-G filters and showed how they can be designed easily using polynomial approximation of an impulse sequence. In contrast to most discussions of S-G filters, they focused on the frequency domain properties, and offered an approximate formula for the 3-dB cutoff frequency as a function of polynomial order N and impulse response half length M. Engineers with a frequency domain mindset may find this useful if they choose to use S-G filters in their application.},
  eventtitle = {{{IEEE Signal Processing Magazine}}},
  keywords = {Approximation methods,Cutoff frequency,Filters,Finite impulse response filter,Polynomials,Smoothing methods},
  file = {/home/lukas/Zotero/storage/UF2JH7P7/Schafer - 2011 - What Is a Savitzky-Golay Filter [Lecture Notes].pdf;/home/lukas/Zotero/storage/AVBY9ED3/5888646.html}
}

@online{Sentinel2MissionsSentinel,
  title = {Sentinel-2 - {{Missions}} - {{Sentinel Online}} - {{Sentinel Online}}},
  url = {https://sentinel.esa.int/web/sentinel/missions/sentinel-2},
  urldate = {2022-08-21},
  keywords = {S2},
  file = {/home/lukas/Zotero/storage/9PNIEVV2/sentinel-2.html}
}

@article{skakunWinterWheatYield2019,
  title = {Winter {{Wheat Yield Assessment}} from {{Landsat}} 8 and {{Sentinel-2 Data}}: {{Incorporating Surface Reflectance}}, {{Through Phenological Fitting}}, into {{Regression Yield Models}}},
  shorttitle = {Winter {{Wheat Yield Assessment}} from {{Landsat}} 8 and {{Sentinel-2 Data}}},
  author = {Skakun, Sergii and Vermote, Eric and Franch, Belen and Roger, Jean-Claude and Kussul, Nataliia and Ju, Junchang and Masek, Jeffrey},
  date = {2019-07-27},
  journaltitle = {Remote Sensing},
  shortjournal = {Remote Sensing},
  volume = {11},
  number = {15},
  pages = {1768},
  issn = {2072-4292},
  doi = {10.3390/rs11151768},
  url = {https://www.mdpi.com/2072-4292/11/15/1768},
  urldate = {2022-08-16},
  abstract = {A combination of Landsat 8 and Sentinel-2 offers a high frequency of observations (3–5 days) at moderate spatial resolution (10–30 m), which is essential for crop yield studies. Existing methods traditionally apply vegetation indices (VIs) that incorporate surface reflectances (SRs) in two or more spectral bands into a single variable, and rarely address the incorporation of SRs into empirical regression models of crop yield. In this work, we address these issues by normalizing satellite data (both VIs and SRs) derived from NASA’s Harmonized Landsat Sentinel-2 (HLS) product, through a phenological fitting. We apply a quadratic function to fit VIs or SRs against accumulated growing degree days (AGDDs), which affects the rate of crop development. The derived phenological metrics for VIs and SRs, namely peak, area under curve (AUC), and fitting coefficients from a quadratic function, were used to build empirical regression winter wheat models at a regional scale in Ukraine for three years, 2016–2018. The best results were achieved for the model with near infrared (NIR) and red spectral bands and derived AUC, constant, linear, and quadratic coefficients of the quadratic model. The best model yielded a root mean square error (RMSE) of 0.201 t/ha (5.4\%) and coefficient of determination R2 = 0.73 on cross-validation.},
  langid = {english},
  file = {/home/lukas/Zotero/storage/9VXICC9W/Skakun et al. - 2019 - Winter Wheat Yield Assessment from Landsat 8 and S.pdf}
}

@software{stephenEarthMultivariateAdaptive2021,
  title = {Earth: {{Multivariate Adaptive Regression Splines}}},
  shorttitle = {Earth},
  author = {Stephen, Milborrow},
  date = {2021-07-20},
  url = {https://CRAN.R-project.org/package=earth},
  urldate = {2022-08-08},
  abstract = {Build regression models using the techniques in Friedman's papers "Fast MARS" and "Multivariate Adaptive Regression Splines" {$<$}doi:10.1214/aos/1176347963{$>$}. (The term "MARS" is trademarked and thus not used in the name of the package.)},
  version = {5.3.1},
  keywords = {Environmetrics,MachineLearning}
}

@article{stockliEuropeanPlantPhenology2004,
  title = {European Plant Phenology and Climate as Seen in a 20-Year {{AVHRR}} Land-Surface Parameter Dataset},
  author = {Stöckli, R. and Vidale, P. L.},
  date = {2004-09-01},
  journaltitle = {International Journal of Remote Sensing},
  volume = {25},
  number = {17},
  pages = {3303--3330},
  publisher = {{Taylor \& Francis}},
  issn = {0143-1161},
  doi = {10.1080/01431160310001618149},
  url = {https://doi.org/10.1080/01431160310001618149},
  urldate = {2022-08-31},
  abstract = {Vegetation distribution and state have been measured since 1981 by the AVHRR (Advanced Very High Resolution Radiometer) instrument through satellite remote sensing. In this study a correction method is applied to the Pathfinder NDVI (Normalized Difference Vegetation Index) data to create a continuous European vegetation phenology dataset of a 10-day temporal and 0.1° spatial resolution; additionally, land surface parameters for use in biosphere–atmosphere modelling are derived. The analysis of time-series from this dataset reveals, for the years 1982–2001, strong seasonal and interannual variability in European land surface vegetation state. Phenological metrics indicate a late and short growing season for the years 1985–1987, in addition to early and prolonged activity in the years 1989, 1990, 1994 and 1995. These variations are in close agreement with findings from phenological measurements at the surface; spring phenology is also shown to correlate particularly well with anomalies in winter temperature and winter North Atlantic Oscillation (NAO) index. Nevertheless, phenological metrics, which display considerable regional differences, could only be determined for vegetation with a seasonal behaviour. Trends in the phenological phases reveal a general shift to earlier (−0.54 days year−1) and prolonged (0.96 days year−1) growing periods which are statistically significant, especially for central Europe.},
  keywords = {fourier},
  annotation = {\_eprint: https://doi.org/10.1080/01431160310001618149},
  file = {/home/lukas/Zotero/storage/6GQ4TCA6/Stöckli and Vidale - 2004 - European plant phenology and climate as seen in a .pdf}
}

@article{strbacEstimationEvapotrasnpirationUrban2017,
  title = {Estimation the  Evapotrasnpiration of Urban Parks with Field Based and Remotely Sensed Datasets},
  author = {Strbac, Orhideja and Milanovic, Misko and Ogrizovic, Vukan},
  date = {2017-07-01},
  pages = {13},
  abstract = {Climate data and remote sensing images are used in this study to estimate the evapotranspiration of urban landscape vegetation. The study was conducted on a historic public park in Vršac (Serbia), which is an important Serbian national heritage site. After comparing recordings from 14 weather stations in the region with recordings from the City Park, the weather station in Vrsac was chosen. The daily averaged values of climatic data for March, June, July and October of each year between 1949 and 2016, were used to compute the daily reference evapotranspiration ETl (mm/day), according to the FAO-56 Penman-Monteith equation. A landscape coefficient was estimated through field monitoring based on the Water Use Classification of Landscape Species (WUCOLS) principles. Also, thermal infrared images from Landsat 8 and the Normalized Difference Vegetation Index (NDVI) from the QuickBird Satellite data are used as the remote sensing inputs to model daily evapotranspiration. This research explored the relationship between urban vegetation ETl computed by the FAO-56 PenmanMonteith method with the required meteorological data and by means of the remote sensing. The analysis revealed the significant correlation between the average daily evapotranspiration estimates from 0.98 to 0.99 by the Nadaraya-Watson kernel regression and the weak Spearman’s Rank correlation (r ranges from -0.006 to 0.384) leading to the conclusion that the Nadaraya-Watson kernel regression is more suitable for evaluating urban landscape water requirements.},
  langid = {english},
  file = {/home/lukas/Zotero/storage/Y25CBUJT/Štrbac et al. - ESTIMATION THE EVAPOTRANSPIRATION OF URBAN PARKS W.pdf}
}

@article{thenkabailSelectionHyperspectralNarrowbands2013,
  title = {Selection of {{Hyperspectral Narrowbands}} ({{HNBs}}) and {{Composition}} of {{Hyperspectral Twoband Vegetation Indices}} ({{HVIs}}) for {{Biophysical Characterization}} and {{Discrimination}} of {{Crop Types Using Field Reflectance}} and {{Hyperion}}/{{EO-1 Data}}},
  author = {Thenkabail, Prasad S. and Mariotto, Isabella and Gumma, Murali Krishna and Middleton, Elizabeth M. and Landis, David R. and Huemmrich, K. Fred},
  date = {2013-04},
  journaltitle = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  volume = {6},
  number = {2},
  pages = {427--439},
  issn = {2151-1535},
  doi = {10.1109/JSTARS.2013.2252601},
  abstract = {The overarching goal of this study was to establish optimal hyperspectral vegetation indices (HVIs) and hyperspectral narrowbands (HNBs) that best characterize, classify, model, and map the world's main agricultural crops. The primary objectives were: (1) crop biophysical modeling through HNBs and HVIs, (2) accuracy assessment of crop type discrimination using Wilks' Lambda through a discriminant model, and (3) meta-analysis to select optimal HNBs and HVIs for applications related to agriculture. The study was conducted using two Earth Observing One (EO-1) Hyperion scenes and other surface hyperspectral data for the eight leading worldwide crops (wheat, corn, rice, barley, soybeans, pulses, cotton, and alfalfa) that occupy \$\textbackslash sim \$70\% of all cropland areas globally. This study integrated data collected from multiple study areas in various agroecosystems of Africa, the Middle East, Central Asia, and India. Data were collected for the eight crop types in six distinct growth stages. These included (a) field spectroradiometer measurements (350–2500 nm) sampled at 1-nm discrete bandwidths, and (b) field biophysical variables (e.g., biomass, leaf area index) acquired to correspond with spectroradiometer measurements. The eight crops were described and classified using \$\textbackslash sim \$20 HNBs. The accuracy of classifying these 8 crops using HNBs was around 95\%, which was \$\textbackslash sim \$25\% better than the multi-spectral results possible from Landsat-7's Enhanced Thematic Mapper+ or EO-1's Advanced Land Imager. Further, based on this research and meta-analysis involving over 100 papers, the study established 33 optimal HNBs and an equal number of specific two-band normalized difference HVIs to best model and study specific biophysical and biochemical quantities of major agricultural crops of the world. Redundant bands identified in this study will help overcome the Hughes Phenomenon (or “the curse of high dimensionality”) in hyperspectral data for a particular application (e.g., biophysical characterization of crops). The findings of this study will make a significant contribution to future hyperspectral missions such as NASA's HyspIRI.},
  eventtitle = {{{IEEE Journal}} of {{Selected Topics}} in {{Applied Earth Observations}} and {{Remote Sensing}}},
  keywords = {Agriculture,biophysical parameters,broadbands,Earth,field reflectance,Hyperion,Hyperspectral imaging,hyperspectral narrowbands,hyperspectral vegetation indices,HyspIRI,imaging spectroscopy,Narrowband,Spectroradiometers,Vegetation mapping},
  file = {/home/lukas/Zotero/storage/JK4WBSZA/Thenkabail et al. - 2013 - Selection of Hyperspectral Narrowbands (HNBs) and .pdf;/home/lukas/Zotero/storage/G8GPE67C/6507245.html}
}

@article{tibshiraniRegressionShrinkageSelection2011,
  title = {Regression Shrinkage and Selection via the Lasso: A Retrospective},
  shorttitle = {Regression Shrinkage and Selection via the Lasso},
  author = {Tibshirani, Robert},
  date = {2011},
  journaltitle = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  volume = {73},
  number = {3},
  pages = {273--282},
  issn = {1467-9868},
  doi = {10.1111/j.1467-9868.2011.00771.x},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-9868.2011.00771.x},
  urldate = {2022-07-28},
  abstract = {Summary. In the paper I give a brief review of the basic idea and some history and then discuss some developments since the original paper on regression shrinkage and selection via the lasso.},
  langid = {english},
  keywords = {l1-penalty,lasso,Penalization,Regularization},
  annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-9868.2011.00771.x},
  file = {/home/lukas/Zotero/storage/62Q5TAER/j.1467-9868.2011.00771.html}
}

@online{UsingGrowingDegree,
  title = {Using {{Growing Degree Days}} to {{Predict Plant Stages}} - [{{PDF Document}}]},
  url = {https://vdocuments.net/using-growing-degree-days-to-predict-plant-stages.html},
  urldate = {2022-09-10},
  abstract = {Itâs tough to predict plant growth based on the calendar because temperatures can vary greatly from year to year. Instead, growing degree days, which are based on actual…},
  langid = {english},
  organization = {{vdocuments.net}},
  keywords = {test},
  file = {/home/lukas/Zotero/storage/8YI762YC/using-growing-degree-days-to-predict-plant-stages.html}
}

@online{UsingGrowingDegree2018,
  title = {Using {{Growing Degree Days}} to {{Predict Plant Stages}} / Using-Growing-Degree-Days-to-Predict-Plant-Stages.Pdf / {{PDF4PRO}}},
  date = {2018-10-26T15:26:44+00:00},
  url = {https://pdf4pro.com/amp/view/using-growing-degree-days-to-predict-plant-stages-383ac5.html},
  urldate = {2022-09-10},
  abstract = {MontGuide For More Online MontGides Visit www.setension.org MT200103AG Reprinted 7/18 Using Growing Degree Days to Predict Plant Stages by P. erry Miller, MSU Assistant Professor, Will Lanier, IPM Assistant, and Stu},
  langid = {american},
  organization = {{PDF4PRO}},
  keywords = {test},
  file = {/home/lukas/Zotero/storage/GQUHEQGR/using-growing-degree-days-to-predict-plant-stages-383ac5.html}
}

@inproceedings{verhoevenLookingBlackTintedGlasses2006,
  title = {Looking through {{Black-Tinted Glasses}} – {{A Remotely Controlled Infrared Eye}} in the {{Sky}}},
  author = {Verhoeven, Geert and Jo, Loenders},
  date = {2006-12-07},
  keywords = {plant-refelctance},
  file = {/home/lukas/Zotero/storage/TZX47V7J/Verhoeven and Jo - 2006 - Looking through Black-Tinted Glasses – A Remotely .pdf}
}

@online{WinterWheatGrowth,
  title = {Winter {{Wheat Growth Stage}} v1 ({{USAF}} 557th {{WW}})},
  url = {https://ipad.fas.usda.gov/cropexplorer/description.aspx?legendid=313&regionid=na},
  urldate = {2022-08-20},
  keywords = {GDD},
  file = {/home/lukas/Zotero/storage/BX2ZJP2V/description.html}
}

@article{woodSmoothingParameterModel2016,
  title = {Smoothing {{Parameter}} and {{Model Selection}} for {{General Smooth Models}}},
  author = {Wood, Simon N. and Pya, Natalya and Säfken, Benjamin},
  date = {2016-10-01},
  journaltitle = {Journal of the American Statistical Association},
  shortjournal = {Journal of the American Statistical Association},
  volume = {111},
  number = {516},
  pages = {1548--1563},
  issn = {0162-1459, 1537-274X},
  doi = {10.1080/01621459.2016.1180986},
  url = {https://www.tandfonline.com/doi/full/10.1080/01621459.2016.1180986},
  urldate = {2022-04-01},
  langid = {english},
  file = {/home/lukas/Zotero/storage/FHLAWSRN/Wood et al. - 2016 - Smoothing Parameter and Model Selection for Genera.pdf}
}

@article{yuanEstimatingCropYield2016,
  title = {Estimating Crop Yield Using a Satellite-Based Light Use Efficiency Model},
  author = {Yuan, Wenping and Chen, Yang and Xia, Jiangzhou and Dong, Wenjie and Magliulo, Vincenzo and Moors, Eddy and Olesen, Jørgen Eivind and Zhang, Haicheng},
  date = {2016-01-01},
  journaltitle = {Ecological Indicators},
  shortjournal = {Ecological Indicators},
  volume = {60},
  pages = {702--709},
  issn = {1470-160X},
  doi = {10.1016/j.ecolind.2015.08.013},
  url = {https://www.sciencedirect.com/science/article/pii/S1470160X15004343},
  urldate = {2022-03-08},
  abstract = {Satellite-based techniques that provide temporally and spatially continuous information over vegetated surfaces have become increasingly important in monitoring the global agriculture yield. In this study, we examine the performance of a light use efficiency model (EC-LUE) for simulating the gross primary production (GPP) and yield of crops. The EC-LUE model can explain on average approximately 90\% of the variability in GPP for 36 FLUXNET sites globally. The results indicate that a universal set of parameters, independent of crop species (except for C4 crops), can be adopted in the EC-LUE model for simulating crops' GPP. At both irrigated and rainfed sites, the EC-LUE model exhibits a similar level of performance. However, large errors are found when simulating yield based on crop harvest index. This analysis highlights the need to improve the representation of the harvest index and carbon allocation for improving crop yield estimations from satellite-based methods.},
  langid = {english},
  keywords = {Crop yield,EC-LUE,Gross primary production,Light use efficiency},
  file = {/home/lukas/Zotero/storage/6LABQ8E4/S1470160X15004343.html}
}