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ptaconet committed Aug 26, 2024
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57 changes: 0 additions & 57 deletions joss-paper/paper.bib
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Expand Up @@ -72,48 +72,6 @@ @article{SkofronickJackson2017
pages = {1679–1695}
}

@Manual{terra,
title = {terra: Spatial Data Analysis},
author = {Robert J. Hijmans},
year = {2024},
note = {R package version 1.7-78},
url = {https://CRAN.R-project.org/package=terra},
doi = {10.32614/CRAN.package.terra}
}


@Article{tidyverse,
title = {Welcome to the {tidyverse}},
author = {Hadley Wickham and Mara Averick and Jennifer Bryan and Winston Chang and Lucy D'Agostino McGowan and Romain François and Garrett Grolemund and Alex Hayes and Lionel Henry and Jim Hester and Max Kuhn and Thomas Lin Pedersen and Evan Miller and Stephan Milton Bache and Kirill Müller and Jeroen Ooms and David Robinson and Dana Paige Seidel and Vitalie Spinu and Kohske Takahashi and Davis Vaughan and Claus Wilke and Kara Woo and Hiroaki Yutani},
year = {2019},
journal = {Journal of Open Source Software},
volume = {4},
number = {43},
pages = {1686},
doi = {10.21105/joss.01686},
}

@Manual{gdal,
title = {{GDAL/OGR} Geospatial Data Abstraction software Library},
author = {{GDAL/OGR contributors}},
organization = {Open Source Geospatial Foundation},
year = {2024},
url = {https://gdal.org},
doi = {10.5281/zenodo.5884351},
}

@Article{sf,
author = {Edzer Pebesma},
title = {{Simple Features for R: Standardized Support for Spatial Vector Data}},
year = {2018},
journal = {{The R Journal}},
doi = {10.32614/RJ-2018-009},
url = {https://doi.org/10.32614/RJ-2018-009},
pages = {439--446},
volume = {10},
number = {1},
}


@Manual{modistools,
title = "The MODISTools package: an interface to the MODIS Land Products Subsets Web Services",
Expand Down Expand Up @@ -158,18 +116,3 @@ @software{rgee
doi = {10.5281/zenodo.3945409},
url = {https://doi.org/10.5281/zenodo.3945409}
}

@article{GORELICK201718,
title = {Google Earth Engine: Planetary-scale geospatial analysis for everyone},
journal = {Remote Sensing of Environment},
volume = {202},
pages = {18-27},
year = {2017},
note = {Big Remotely Sensed Data: tools, applications and experiences},
issn = {0034-4257},
doi = {10.1016/j.rse.2017.06.031},
url = {https://www.sciencedirect.com/science/article/pii/S0034425717302900},
author = {Noel Gorelick and Matt Hancher and Mike Dixon and Simon Ilyushchenko and David Thau and Rebecca Moore},
keywords = {Cloud computing, Big data, Analysis, Platform, Data democratization, Earth Engine},
abstract = {Google Earth Engine is a cloud-based platform for planetary-scale geospatial analysis that brings Google's massive computational capabilities to bear on a variety of high-impact societal issues including deforestation, drought, disaster, disease, food security, water management, climate monitoring and environmental protection. It is unique in the field as an integrated platform designed to empower not only traditional remote sensing scientists, but also a much wider audience that lacks the technical capacity needed to utilize traditional supercomputers or large-scale commodity cloud computing resources.}
}
15 changes: 6 additions & 9 deletions joss-paper/paper.md
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# Statement of need

Data from EO satellites are a crucial and increasingly valuable resource for monitoring and understanding our planet, especially in the context of global change. EO data from the NASA are among the richest and longest-standing in the field. Iconic and widely NASA EO data collections include the MODIS Land products [@JUSTICE20023], the VIIRS products - which continue the legacy of MODIS [@ROMAN2024113963], and the GPM mission products [@SkofronickJackson2017]. Collectively, these products have provided essential data for over 20 years, enabling the study of Earth's dynamics. They support research in climate change, disaster response, biodiversity, ecosystem monitoring, ecology, public health, and more [@modis-applications].
EO satellite data are invaluable for monitoring and understanding our planet, with NASA's datasets like MODIS [@JUSTICE20023], VIIRS [@ROMAN2024113963], and GPM [@SkofronickJackson2017] among the most important. These collections have provided crucial data for over 20 years, supporting research in areas such as climate change, disaster response, biodiversity, public health, and more [@modis-applications].

Despite the growing availability of EO data, accessing and utilizing them remains challenging due to large file sizes and complex multidimensional layers [@AGNOLI2023122357], especially for long time series and in areas with limited internet. This complexity can lead to underutilization or fragmented data processing workflows, hindering transparent and reproducible open science. While powerful tools like [Google Earth Engine](https://earthengine.google.com/) [@GORELICK201718] offer some solutions, they have drawbacks such as proprietary and energy-intensive software. To fully realize the potential of EO data for global research and decision-making, it is crucial to simplify, lighten and streamline access while maintaining an open-source framework.
However, despite the increasing availability of EO data, accessing and utilizing them remains challenging [@AGNOLI2023122357]. The large file sizes and complex multidimensional layers make it difficult to work with long time series, especially in regions with limited internet infrastructure. This complexity often leads to underutilization of data, fragmented workflows, and reliance on proprietary, energy-intensive tools like [Google Earth Engine](https://earthengine.google.com/), which can hinder transparent and reproducible Open Science.

In this context we have developed `modisfast`, an open-source R package [@R] designed to simplify, streamline, and speed-up the download and import of MODIS, VIIRS, and GPM time series for R users. Enhancing the existing R ecosystem for accessing MODIS data, `modisfast` introduces new features and supports additional data sources. Built on the [OPeNDAP](https://www.opendap.org/) protocol, `modisfast` allows users to apply spatial, temporal, and band/layer filters during the download phase, optimizing data retrieval and processing. The package also supports parallelized downloads for increased efficiency. Thus, `modisfast` facilitates access to a set of EO data for R users, while using and promoting open-source international standards for data access.
To address these challenges, we developed `modisfast`, an R [@R] package designed to simplify and speed-up the download and import of MODIS, VIIRS, and GPM time series for R users. Built on the [OPeNDAP](https://www.opendap.org/) protocol, `modisfast` enhances the existing R ecosystem of tools for accessing MODIS data by introducing new features and supporting additional data sources. It allows users to apply spatial, temporal, and band/layer filters during the download phase, optimizing data retrieval and processing while promoting open-source international standards for data access.

# Target audience

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# Alternatives

Besides `modisfast`, there are several open-source R packages available for accessing MODIS or VIIRS Land products. Table 1 summarizes the main features of these packages. A thorough comparison of `modisfast` with these R packages in terms of data access time can be found in the [package documentation](https://ptaconet.github.io/modisfast/articles/perf_comp.html).
Besides `modisfast`, there are several open-source R packages available for accessing MODIS or VIIRS Land products. Table 1 summarizes the main features of these packages. A thorough comparison of `modisfast` with these R packages (including data access time) can be found in the [package documentation](https://ptaconet.github.io/modisfast/articles/perf_comp.html).

| | `modisfast` | `appeears` | `MODIS` | `MODIStsp` | `MODIStools` | `rgee` |
|-----------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
Expand All @@ -87,15 +87,12 @@ Besides `modisfast`, there are several open-source R packages available for acce
| Website | [`GitHub`](https://github.com/ptaconet/modisfast) | [`GitHub`](https://github.com/bluegreen-labs/appeears) | [`GitHub`](https://github.com/fdetsch/MODIS) | [`GitHub`](https://github.com/ropensci/MODIStsp) | [`GitHub`](https://github.com/ropensci/MODISTools) | [`GitHub`](https://github.com/r-spatial/rgee) |
| Publication | | @rgee | NA | @MODIStsp | @modistools | @rgee |

Table 1: Comparison of `modisfast` with other popular alternatives.
Table 1: Comparison of `modisfast` with other alternatives.

# Acknowledgements

We thank NASA and its partners for making all their Earth science data freely available, and financing and implementing open data access protocols such as OPeNDAP. We also thank the non-profit [OPeNDAP, Inc.](https://www.opendap.org/about/) for developing and maintaining the eponym tool, and the developers of the R packages `modisfast` depends on.

This work has been developed over the course of several research projects :

- the REACT 1 and REACT 2 projects funded by the *l'Initiative - Expertise France* ;
- the ANORHYTHM (ANR-16-CE35-008) and DIV-YOO project (ANR-23-CE35-0005) funded by the French National Research Agency (ANR).
This work has been developed over the course of several research projects (REACT 1, REACT 2, ANORHYTHM and DIV-YOO) funded by Expertise France and the French National Research Agency (ANR).

# References

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