adding references

.gitignore

@@ -30,6 +30,7 @@ share/python-wheels/
 .installed.cfg
 *.egg
 MANIFEST
+data/
 
 # PyInstaller
 #  Usually these files are written by a python script from a template

_config.yml

@@ -75,3 +75,6 @@ sphinx:
         - footer-logos
         - footer-info
         - footer-extra
+
+bibtex_bibfiles:
+  - notebooks/references.bib

environment.yml

@@ -2,6 +2,9 @@ name: cookbook-dev
 channels:
   - conda-forge
 dependencies:
+  - pip
   - jupyter-book
   - jupyterlab
   - sphinx-pythia-theme
+  - pip:
+    - sphinx-rtd-theme

notebooks/references.bib

@@ -0,0 +1,160 @@
+
+@misc{celik_colors_2015,
+	title = {Colors, {Methods} and {Mistakes} in {Spatial} {Data} {Visualization}},
+	url = {https://medium.com/@acelik/colors-methods-and-mistakes-in-spatial-data-visualization-60d02e7f09fd},
+	abstract = {Around ten days ago, I have made a presentation in an event organized by Data Science Istanbul group in Istanbul, Turkey. Seminar focused…},
+	language = {en},
+	urldate = {2024-05-28},
+	journal = {Medium},
+	author = {Celik, Anil},
+	month = aug,
+	year = {2015},
+	file = {Snapshot:/home/mschobbe/Zotero/storage/BCKHNHID/colors-methods-and-mistakes-in-spatial-data-visualization-60d02e7f09fd.html:text/html},
+}
+
+@article{nguyen_examining_2021,
+	title = {Examining data visualization pitfalls in scientific publications},
+	volume = {4},
+	issn = {2096-496X},
+	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8556474/},
+	doi = {10.1186/s42492-021-00092-y},
+	abstract = {Data visualization blends art and science to convey stories from data via graphical representations. Considering different problems, applications, requirements, and design goals, it is challenging to combine these two components at their full force. While the art component involves creating visually appealing and easily interpreted graphics for users, the science component requires accurate representations of a large amount of input data. With a lack of the science component, visualization cannot serve its role of creating correct representations of the actual data, thus leading to wrong perception, interpretation, and decision. It might be even worse if incorrect visual representations were intentionally produced to deceive the viewers. To address common pitfalls in graphical representations, this paper focuses on identifying and understanding the root causes of misinformation in graphical representations. We reviewed the misleading data visualization examples in the scientific publications collected from indexing databases and then projected them onto the fundamental units of visual communication such as color, shape, size, and spatial orientation. Moreover, a text mining technique was applied to extract practical insights from common visualization pitfalls. Cochran’s Q test and McNemar’s test were conducted to examine if there is any difference in the proportions of common errors among color, shape, size, and spatial orientation. The findings showed that the pie chart is the most misused graphical representation, and size is the most critical issue. It was also observed that there were statistically significant differences in the proportion of errors among color, shape, size, and spatial orientation.},
+	urldate = {2024-05-29},
+	journal = {Visual Computing for Industry, Biomedicine, and Art},
+	author = {Nguyen, Vinh T and Jung, Kwanghee and Gupta, Vibhuti},
+	month = oct,
+	year = {2021},
+	pmid = {34714412},
+	pmcid = {PMC8556474},
+	pages = {27},
+	file = {PubMed Central Full Text PDF:/home/mschobbe/Zotero/storage/5N6D56NV/Nguyen et al. - 2021 - Examining data visualization pitfalls in scientifi.pdf:application/pdf},
+}
+
+@article{ware_color_1988,
+	title = {Color sequences for univariate maps: theory, experiments and principles},
+	volume = {8},
+	copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
+	issn = {0272-1716},
+	shorttitle = {Color sequences for univariate maps},
+	url = {http://ieeexplore.ieee.org/document/7760/},
+	doi = {10.1109/38.7760},
+	language = {en},
+	number = {5},
+	urldate = {2024-05-29},
+	journal = {IEEE Computer Graphics and Applications},
+	author = {Ware, C.},
+	month = sep,
+	year = {1988},
+	pages = {41--49},
+	file = {Ware - 1988 - Color sequences for univariate maps theory, exper.pdf:/home/mschobbe/Zotero/storage/8WAHXK7V/Ware - 1988 - Color sequences for univariate maps theory, exper.pdf:application/pdf},
+}
+
+@misc{noauthor_httpsccomunhedusitesdefaultfilespublicationsware_1988_cga_color_sequences_univariate_mapspdf_nodate,
+	title = {https://ccom.unh.edu/sites/default/files/publications/{Ware}\_1988\_CGA\_Color\_sequences\_univariate\_maps.pdf},
+	url = {https://ccom.unh.edu/sites/default/files/publications/Ware_1988_CGA_Color_sequences_univariate_maps.pdf},
+	urldate = {2024-05-29},
+}
+
+@article{sibrel_relation_2020,
+	title = {The relation between color and spatial structure for interpreting colormap data visualizations},
+	volume = {20},
+	issn = {1534-7362},
+	url = {https://doi.org/10.1167/jov.20.12.7},
+	doi = {10.1167/jov.20.12.7},
+	abstract = {Interpreting colormap visualizations requires determining how dimensions of color in visualizations map onto quantities in data. People have color-based biases that influence their interpretations of colormaps, such as a dark-is-more bias—darker colors map to larger quantities. Previous studies of color-based biases focused on colormaps with weak data spatial structure, but color-based biases may not generalize to colormaps with strong data spatial structure, like “hotspots” typically found in weather maps and neuroimaging brain maps. There may be a hotspot-is-more bias to infer that colors within hotspots represent larger quantities, which may override the dark-is-more bias. We tested this possibility in four experiments. Participants saw colormaps with hotspots and a legend that specified the color-quantity mapping. Their task was to indicate which side of the colormap depicted larger quantities (left/right). We varied whether the legend specified dark-more mapping or light-more mapping across trials and operationalized a dark-is-more bias as faster response time (RT) when the legend specified dark-more mapping. Experiment 1 demonstrated robust evidence for the dark-is-more bias, without evidence for a hotspot-is-more bias. Experiments 2 to 4 suggest that a hotspot-is-more bias becomes relevant when hotspots are a statistically reliable cue to “more” (i.e., the locus of larger quantities) and when hotspots are more perceptually pronounced. Yet, comparing conditions in which the hotspots were “more,” RTs were always faster for dark hotspots than light hotspots. Thus, in the presence of strong spatial cues to the locus of larger quantities, color-based biases still influenced interpretations of colormap data visualizations.},
+	number = {12},
+	urldate = {2024-05-29},
+	journal = {Journal of Vision},
+	author = {Sibrel, Shannon C. and Rathore, Ragini and Lessard, Laurent and Schloss, Karen B.},
+	month = nov,
+	year = {2020},
+	pages = {7},
+	file = {Full Text:/home/mschobbe/Zotero/storage/BBK94XG6/Sibrel et al. - 2020 - The relation between color and spatial structure f.pdf:application/pdf;Snapshot:/home/mschobbe/Zotero/storage/8669JRUZ/article.html:text/html},
+}
+
+@article{bauer-marschallinger_satellite-based_2022,
+	title = {Satellite-{Based} {Flood} {Mapping} through {Bayesian} {Inference} from a {Sentinel}-1 {SAR} {Datacube}},
+	volume = {14},
+	copyright = {http://creativecommons.org/licenses/by/3.0/},
+	issn = {2072-4292},
+	url = {https://www.mdpi.com/2072-4292/14/15/3673},
+	doi = {10.3390/rs14153673},
+	abstract = {Spaceborne Synthetic Aperture Radar (SAR) are well-established systems for flood mapping, thanks to their high sensitivity towards water surfaces and their independence from daylight and cloud cover. Particularly able is the 2014-launched Copernicus Sentinel-1 C-band SAR mission, with its systematic monitoring schedule featuring global land coverage in a short revisit time and a 20 m ground resolution. Yet, variable environment conditions, low-contrasting land cover, and complex terrain pose major challenges to fully automated flood monitoring. To overcome these issues, and aiming for a robust classification, we formulate a datacube-based flood mapping algorithm that exploits the Sentinel-1 orbit repetition and a priori generated probability parameters for flood and non-flood conditions. A globally applicable flood signature is obtained from manually collected wind- and frost-free images. Through harmonic analysis of each pixel’s full time series, we derive a local seasonal non-flood signal comprising the expected backscatter values for each day-of-year. From those predefined probability distributions, we classify incoming Sentinel-1 images by simple Bayes inference, which is computationally slim and hence suitable for near-real-time operations, and also yields uncertainty values. The datacube-based masking of no-sensitivity resulting from impeding land cover and ill-posed SAR configuration enhances the classification robustness. We employed the algorithm on a 6-year Sentinel-1 datacube over Greece, where a major flood hit the region of Thessaly in 2018. In-depth analysis of model parameters and sensitivity, and the evaluation against microwave and optical reference flood maps, suggest excellent flood mapping skill, and very satisfying classification metrics with about 96\% overall accuracy and only few false positives. The presented algorithm is part of the ensemble flood mapping product of the Global Flood Monitoring (GFM) component of the Copernicus Emergency Management Service (CEMS).},
+	language = {en},
+	number = {15},
+	urldate = {2024-06-03},
+	journal = {Remote Sensing},
+	author = {Bauer-Marschallinger, Bernhard and Cao, Senmao and Tupas, Mark Edwin and Roth, Florian and Navacchi, Claudio and Melzer, Thomas and Freeman, Vahid and Wagner, Wolfgang},
+	month = jan,
+	year = {2022},
+	note = {Number: 15
+Publisher: Multidisciplinary Digital Publishing Institute},
+	keywords = {automatic flood monitoring, Bayes inference, datacube, flood mapping, SAR, Sentinel-1, time series analysis},
+	pages = {3673},
+	file = {Full Text PDF:/home/mschobbe/Zotero/storage/X4KXFTT7/Bauer-Marschallinger et al. - 2022 - Satellite-Based Flood Mapping through Bayesian Inf.pdf:application/pdf},
+}
+
+@article{massart_mitigating_2024,
+	title = {Mitigating the impact of dense vegetation on the {Sentinel}-1 surface soil moisture retrievals over {Europe}},
+	volume = {57},
+	issn = {null},
+	url = {https://doi.org/10.1080/22797254.2023.2300985},
+	doi = {10.1080/22797254.2023.2300985},
+	abstract = {The C-band Synthetic Aperture Radar (SAR) on board of the Sentinel-1 satellites have a strong potential to retrieve Surface Soil Moisture (SSM). Using a change detection model to Sentinel-1 backscatter, an SSM product at a kilometre scale resolution over Europe could be established in the Copernicus Global Land Service (CGLS). Over areas with dense vegetation and high biomass. The geometry and water content influence the seasonality of the backscatter dynamics and hamper the SSM retrieval quality from Sentinel-1. This study demonstrates the effect of woody vegetation on SSM retrievals and proposes a masking method at the native resolution of Sentinel-1’s Interferometric Wide (IW) swath mode. At a continental 20 m grid, four dense vegetation masks are implemented over Europe in the resampling of the backscatter to a kilometre scale. The resulting backscatter is then used as input for the TUWien (TUW) change detection model and compared to both in-situ and modelled SSM. This paper highlights the potential of high-resolution vegetation datasets to mask for non-soil moisture-sensitive pixels at a sub-kilometre resolution. Results show that both correlation and seasonality of the retrieved SSM are improved by masking the dense vegetation at a 20 m resolution. Dense vegetation reduces the ability to retrieve surface soil moisture at a kilometre scale from Sentinel-1 backscatter which is currently available on the Copernicus Global Land Service portal.Applying selective masking for vegetation during the resampling phase improves Sentinel-1 sensitivity to soil moisture.A novel vegetation-corrected Sentinel-1 surface soil moisture product is processed over Europe for the period 2016–2022 included.The Sentinel-1 forest mask improves the Sentinel-1 SSM product correlation and seasonality compared to both modelled and in-situ datasets. Dense vegetation reduces the ability to retrieve surface soil moisture at a kilometre scale from Sentinel-1 backscatter which is currently available on the Copernicus Global Land Service portal. Applying selective masking for vegetation during the resampling phase improves Sentinel-1 sensitivity to soil moisture. A novel vegetation-corrected Sentinel-1 surface soil moisture product is processed over Europe for the period 2016–2022 included. The Sentinel-1 forest mask improves the Sentinel-1 SSM product correlation and seasonality compared to both modelled and in-situ datasets.},
+	number = {1},
+	urldate = {2024-06-03},
+	journal = {European Journal of Remote Sensing},
+	author = {Massart, Samuel and Vreugdenhil, Mariette and Bauer-Marschallinger, Bernhard and Navacchi, Claudio and Raml, Bernhard and Wagner, Wolfgang},
+	month = dec,
+	year = {2024},
+	note = {Publisher: Taylor \& Francis
+\_eprint: https://doi.org/10.1080/22797254.2023.2300985},
+	keywords = {change detection, high-resolution, Sentinel-1, soil moisture, Synthetic Aperture Radar, vegetation},
+	pages = {2300985},
+	file = {Full Text PDF:/home/mschobbe/Zotero/storage/ME6MG37F/Massart et al. - 2024 - Mitigating the impact of dense vegetation on the S.pdf:application/pdf},
+}
+
+@article{bauer-marschallinger_toward_2019,
+	title = {Toward {Global} {Soil} {Moisture} {Monitoring} {With} {Sentinel}-1: {Harnessing} {Assets} and {Overcoming} {Obstacles}},
+	volume = {57},
+	issn = {1558-0644},
+	shorttitle = {Toward {Global} {Soil} {Moisture} {Monitoring} {With} {Sentinel}-1},
+	url = {https://ieeexplore.ieee.org/document/8444430},
+	doi = {10.1109/TGRS.2018.2858004},
+	abstract = {Soil moisture is a key environmental variable, important to, e.g., farmers, meteorologists, and disaster management units. Here, we present a method to retrieve surface soil moisture (SSM) from the Sentinel-1 (S-1) satellites, which carry C-band Synthetic Aperture Radar (CSAR) sensors that provide the richest freely available SAR data source so far, unprecedented in accuracy and coverage. Our SSM retrieval method, adapting well-established change detection algorithms, builds the first globally deployable soil moisture observation data set with 1-km resolution. This paper provides an algorithm formulation to be operated in data cube architectures and high-performance computing environments. It includes the novel dynamic Gaussian upscaling method for spatial upscaling of SAR imagery, harnessing its field-scale information and successfully mitigating effects from the SAR's high signal complexity. Also, a new regression-based approach for estimating the radar slope is defined, coping with Sentinel-1's inhomogeneity in spatial coverage. We employ the S-1 SSM algorithm on a 3-year S-1 data cube over Italy, obtaining a consistent set of model parameters and product masks, unperturbed by coverage discontinuities. An evaluation of therefrom generated S-1 SSM data, involving a 1-km soil water balance model over Umbria, yields high agreement over plains and agricultural areas, with low agreement over forests and strong topography. While positive biases during the growing season are detected, the excellent capability to capture small-scale soil moisture changes as from rainfall or irrigation is evident. The S-1 SSM is currently in preparation toward operational product dissemination in the Copernicus Global Land Service.},
+	number = {1},
+	urldate = {2024-06-03},
+	journal = {IEEE Transactions on Geoscience and Remote Sensing},
+	author = {Bauer-Marschallinger, Bernhard and Freeman, Vahid and Cao, Senmao and Paulik, Christoph and Schaufler, Stefan and Stachl, Tobias and Modanesi, Sara and Massari, Christian and Ciabatta, Luca and Brocca, Luca and Wagner, Wolfgang},
+	month = jan,
+	year = {2019},
+	note = {Conference Name: IEEE Transactions on Geoscience and Remote Sensing},
+	keywords = {Change detection algorithms, Copernicus, Image resolution, image sampling, Monitoring, Sensors, Sentinel-1, Signal resolution, soil moisture, Soil moisture, Synthetic aperture radar},
+	pages = {520--539},
+	file = {IEEE Xplore Abstract Record:/home/mschobbe/Zotero/storage/5QJ4V6PY/8444430.html:text/html},
+}
+
+@article{quast_getting_2024,
+  author       = {Raphael Quast},
+  title        = {EOmaps: A python package to visualize and analyze geographical datasets.},
+  doi          = {10.5281/zenodo.6459598},
+  url          = {https://doi.org/10.5281/zenodo.6459598}
+}
+
+@article{rouse1974monitoring,
+  title={Monitoring vegetation systems in the Great Plains with ERTS},
+  author={Rouse, John Wilson and Haas, R{\"u}diger H and Schell, John A and Deering, Donald W and others},
+  journal={NASA Spec. Publ},
+  volume={351},
+  number={1},
+  pages={309},
+  year={1974}
+}
+
+@online{nasa2020,
+    title = {Earth Observatory},
+    author = {NASA},
+    year = {2020},
+    url = {https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_2.php},
+    urldate = {2024-07-26}
+}

notebooks/references.ipynb

@@ -4,15 +4,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# References\n",
-    "\n",
-    "Bauer-Marschallinger, Bernhard, Senmao Cao, Mark Edwin Tupas, Florian Roth, Claudio Navacchi, Thomas Melzer, Vahid Freeman, and Wolfgang Wagner. 2022. “Satellite-Based Flood Mapping Through Bayesian Inference from a Sentinel-1 SAR Datacube.” Remote Sensing 14 (15): 3673. https://doi.org/10.3390/rs14153673.\n",
-    "\n",
-    "NASA. 2020. “Earth Observatory.” 2020. https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_2.php.\n",
-    "\n",
-    "Quast, Raphael. n.d. “EOmaps: A Python Package to Visualize and Analyze Geographical Datasets.” https://doi.org/10.5281/zenodo.6459598.\n",
-    "\n",
-    "Rouse, John Wilson, Rüdiger H Haas, John A Schell, Donald W Deering, et al. 1974. “Monitoring Vegetation Systems in the Great Plains with ERTS.” NASA Spec. Publ 351 (1): 309."
+    "## References\n",
+    "```{bibliography}\n",
+    ":style: unsrt\n",
+    "```"
    ]
   }
  ],