paper.bib

@ARTICLE{abernathey_2021_cloud,
  author={Abernathey, Ryan P. and Augspurger, Tom and Banihirwe, Anderson and Blackmon-Luca, Charles C. and Crone, Timothy J. and Gentemann, Chelle L. and Hamman, Joseph J. and Henderson, Naomi and Lepore, Chiara and McCaie, Theo A. and Robinson, Niall H. and Signell, Richard P.},
  journal={Computing in Science & Engineering}, 
  title={Cloud-Native Repositories for Big Scientific Data}, 
  year={2021},
  volume={23},
  number={2},
  pages={26-35},
  keywords={Cloud computing;Training data;Computational modeling;Reproducibility of results;Collaboration;Reliability;Distributed databases},
  doi={10.1109/MCSE.2021.3059437}}

@article{appel_2019_ondemand,
  title = {On-{{Demand Processing}} of {{Data Cubes}} from {{Satellite Image Collections}} with the Gdalcubes {{Library}}},
  author = {Appel, Marius and Pebesma, Edzer},
  year = {2019},
  month = jun,
  journal = {Data},
  volume = {4},
  number = {3},
  pages = {92},
  issn = {2306-5729},
  doi = {10.3390/data4030092},
  urldate = {2025-03-10},
  abstract = {Earth observation data cubes are increasingly used as a data structure to make large collections of satellite images easily accessible to scientists. They hide complexities in the data such that data users can concentrate on the analysis rather than on data management. However, the construction of data cubes is not trivial and involves decisions that must be taken with regard to any particular analyses. This paper proposes on-demand data cubes, which are constructed on the fly when data users process the data. We introduce the open-source C++ library and R package gdalcubes for the construction and processing of on-demand data cubes from satellite image collections, and show how it supports interactive method development workflows where data users can initially try methods on small subsamples before running analyses on high resolution and/or large areas. Two study cases, one on processing Sentinel-2 time series and the other on combining vegetation, land surface temperature, and precipitation data, demonstrate and evaluate this implementation. While results suggest that on-demand data cubes implemented in gdalcubes support interactivity and allow for combining multiple data products, the speed-up effect also strongly depends on how original data products are organized. The potential for cloud deployment is discussed.},
  copyright = {https://creativecommons.org/licenses/by/4.0/},
  langid = {english},
}

@misc{baumann_2017_datacube,
  title={The datacube manifesto},
  author={Baumann, Peter},
  url = {https://earthserver.eu/tech/datacube-manifesto/The-Datacube-Manifesto.pdf},
  year={2017}
}

@article{gentemann_2021_science,
    title = {Science {Storms} the {Cloud}},
    volume = {2},
    issn = {2576-604X, 2576-604X},
    url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020AV000354},
    doi = {10.1029/2020AV000354},
    language = {en},
    number = {2},
    urldate = {2023-08-31},
    journal = {AGU Advances},
    author = {Gentemann, C. L. and Holdgraf, C. and Abernathey, R. and Crichton, D. and Colliander, J. and Kearns, E. J. and Panda, Y. and Signell, R. P.},
    month = jun,
    year = {2021},
    pages = {e2020AV000354},
}

@article{giuliani_2019_EarthObservationOpen,
  title = {Earth {{Observation Open Science}}: {{Enhancing Reproducible Science Using Data Cubes}}},
  shorttitle = {Earth {{Observation Open Science}}},
  author = {Giuliani, Gregory and Camara, Gilberto and Killough, Brian and Minchin, Stuart},
  year = {2019},
  month = nov,
  journal = {Data},
  volume = {4},
  number = {4},
  pages = {147},
  issn = {2306-5729},
  doi = {10.3390/data4040147},
  urldate = {2025-03-09},
  abstract = {Earth Observation Data Cubes (EODC) have emerged as a promising solution to efficiently and effectively handle Big Earth Observation (EO) Data generated by satellites and made freely and openly available from different data repositories. The aim of this Special Issue, ``Earth Observation Data Cube'', in Data, is to present the latest advances in EODC development and implementation, including innovative approaches for the exploitation of satellite EO data using multi-dimensional (e.g., spatial, temporal, spectral) approaches. This Special Issue contains 14 articles covering a wide range of topics such as Synthetic Aperture Radar (SAR), Analysis Ready Data (ARD), interoperability, thematic applications (e.g., land cover, snow cover mapping), capacity development, semantics, processing techniques, as well as national implementations and best practices. These papers made significant contributions to the advancement of a more Open and Reproducible Earth Observation Science, reducing the gap between users' expectations for decision-ready products and current Big Data analytical capabilities, and ultimately unlocking the information power of EO data by transforming them into actionable knowledge.},
  copyright = {https://creativecommons.org/licenses/by/4.0/},
  langid = {english},
}

@article{guo_2017_big,
    title = {Big {Earth} data: {A} new frontier in {Earth} and information sciences},
    volume = {1},
    issn = {2096-4471, 2574-5417},
    shorttitle = {Big {Earth} data},
    url = {https://www.tandfonline.com/doi/full/10.1080/20964471.2017.1403062},
    doi = {10.1080/20964471.2017.1403062},
    language = {en},
    number = {1-2},
    urldate = {2023-03-22},
    journal = {Big Earth Data},
    author = {Guo, Huadong},
    month = dec,
    year = {2017},
    pages = {4--20},
}
@inproceedings{lewis_2018_CEOSAnalysisReady,
  title = {{{CEOS Analysis Ready Data}} for {{Land}} ({{CARD4L}}) {{Overview}}},
  booktitle = {{{IGARSS}} 2018 - 2018 {{IEEE International Geoscience}} and {{Remote Sensing Symposium}}},
  author = {Lewis, Adam and Lacey, Jennifer and Mecklenburg, Susanne and Ross, Jonathon and Siqueira, Andreia and Killough, Brian and Szantoi, Zoltan and Tadono, Takeo and Rosenavist, Ake and Goryl, Philippe and Miranda, Nuno and Hosford, Steven},
  year = {2018},
  month = jul,
  pages = {7407--7410},
  publisher = {IEEE},
  address = {Valencia},
  doi = {10.1109/IGARSS.2018.8519255},
  urldate = {2025-03-10},
  abstract = {For many land monitoring applications using remote sensing, lack of data is no longer an issue, as it may have been in the past. Programs, such as Copernicus by the European Commission and the Landsat Missions by the United States Geological Survey, have adopted systematic acquisition strategies, and distribute vast amounts of satellite data under open licenses. In parallel, storage and computing capability have evolved to make it cost-effective and practical to process and analyze these data at various scales. Data architecture solutions, such as the Open Data Cube (ODC) and the Copernicus Data and Information Access Services (DIAS), are providing frameworks that make [scientific] analysis much simpler and straightforward.},
  isbn = {978-1-5386-7150-4},
  langid = {english},
}


@article{lund_2020_snowmelt,
  title = {Mapping Snowmelt Progression in the Upper Indus Basin with Synthetic Aperture Radar},
  author = {Lund, Jewell and Forster, Richard R. and Rupper, Summer B. and Deeb, Elias J. and Marshall, H. P. and Hashmi, Muhammad Zia and Burgess, Evan},
  year = {2020},
  journal = {Frontiers in Earth Science},
  volume = {7},
  issn = {2296-6463},
  doi = {10.3389/feart.2019.00318},
  abstract = {{\textexclamdown}p{\textquestiondown}The Indus River is a vital resource for food security, ecosystem services, hydropower, and economy for millions of people living in Pakistan, India, China, and Afghanistan. Glacier and snowmelt from the high altitude Himalaya, Karakoram, and Hindu Kush mountain ranges are the largest drivers of discharge in the upper Indus Basin (UIB), and contribute significantly to Indus flows. Complex climatology and topography, coupled with the challenges of field study and meteorological measurement in these rugged ranges, elicit notable uncertainties in predicting seasonal runoff as well as cryospheric response to changes in climate. Here we utilize Sentinel-1 synthetic aperture radar (SAR) imagery to track ablation season development of wet snow in the Shigar Watershed of the Karakoram Mountains in Pakistan from 2015 to 2018. We exploit opportune local image acquisition times to highlight diurnal differences in radar indications of wet snow, and examine the spatial and temporal contexts of radar diurnal differences for 2015, 2017, and 2018 ablation seasons. Radar classifications for each ablation season show spatial and temporal patterns that indicate a dry winter snowpack undergoing diurnal surface melt-refreeze cycles, transitioning to surface snow that remains wet both day and night, and finally snow free conditions following melt out. Diurnally differing SAR signals may offer insights into important snowpack energy balance processes that precede melt out, which could provide useful constraints for both glacier mass balance modeling and runoff forecasting in remote alpine watersheds.{\textexclamdown}/p{\textquestiondown}}
}
@article{mahecha_2020_EarthSystemData,
  title = {Earth System Data Cubes Unravel Global Multivariate Dynamics},
  author = {Mahecha, Miguel D. and Gans, Fabian and Brandt, Gunnar and Christiansen, Rune and Cornell, Sarah E. and Fomferra, Normann and Kraemer, Guido and Peters, Jonas and Bodesheim, Paul and {Camps-Valls}, Gustau and Donges, Jonathan F. and Dorigo, Wouter and {Estupinan-Suarez}, Lina M. and {Gutierrez-Velez}, Victor H. and Gutwin, Martin and Jung, Martin and Londo{\~n}o, Maria C. and Miralles, Diego G. and Papastefanou, Phillip and Reichstein, Markus},
  year = {2020},
  month = feb,
  journal = {Earth System Dynamics},
  volume = {11},
  number = {1},
  pages = {201--234},
  issn = {2190-4987},
  doi = {10.5194/esd-11-201-2020},
  urldate = {2025-03-10},
  abstract = {Abstract. Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model--data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1)~summary statistics for ecosystem and climate dynamics; (2)~intrinsic dimensionality analysis on multiple timescales; and (3)~model--data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model--data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries.},
  copyright = {https://creativecommons.org/licenses/by/4.0/},
  langid = {english},
}


@article{mathieu_2017_esas,
    title = {The {ESA}'s {Earth} {Observation} {Open} {Science} {Program} [{Space} {Agencies}]},
    volume = {5},
    issn = {2168-6831, 2473-2397},
    url = {http://ieeexplore.ieee.org/document/7946222/},
    doi = {10.1109/MGRS.2017.2688704},
    language = {en},
    number = {2},
    urldate = {2024-01-19},
    journal = {IEEE Geoscience and Remote Sensing Magazine},
    author = {Mathieu, Pierre-Philippe and Borgeaud, Maurice and Desnos, Yves-Louis and Rast, Michael and Brockmann, Carsten and See, Linda and Kapur, Ravi and Mahecha, Miguel and Benz, Ursula and Fritz, Steffen},
    month = jun,
    year = {2017},
    pages = {86--96},
}
@article{montero_2024_EarthSystemData,
  title = {Earth {{System Data Cubes}}: {{Avenues}} for Advancing {{Earth}} System Research},
  shorttitle = {Earth {{System Data Cubes}}},
  author = {Montero, David and Kraemer, Guido and Anghelea, Anca and Aybar, C{\'e}sar and Brandt, Gunnar and {Camps-Valls}, Gustau and Cremer, Felix and Flik, Ida and Gans, Fabian and Habershon, Sarah and Ji, Chaonan and Kattenborn, Teja and {Mart{\'i}nez-Ferrer}, Laura and Martinuzzi, Francesco and Reinhardt, Martin and S{\"o}chting, Maximilian and Teber, Khalil and Mahecha, Miguel D.},
  year = {2024},
  journal = {Environmental Data Science},
  volume = {3},
  pages = {e27},
  issn = {2634-4602},
  doi = {10.1017/eds.2024.22},
  urldate = {2025-03-08},
  abstract = {Recent advancements in Earth system science have been marked by the exponential increase in the availability of diverse, multivariate datasets characterised by moderate to high spatio-temporal resolutions. Earth System Data Cubes (ESDCs) have emerged as one suitable solution for transforming this flood of data into a simple yet robust data structure. ESDCs achieve this by organising data into an analysis-ready format aligned with a spatiotemporal grid, facilitating user-friendly analysis and diminishing the need for extensive technical data processing knowledge. Despite these significant benefits, the completion of the entire ESDC life cycle remains a challenging task. Obstacles are not only of a technical nature but also relate to domain-specific problems in Earth system research. There exist barriers to realising the full potential of data collections in light of novel cloudbased technologies, particularly in curating data tailored for specific application domains. These include transforming data to conform to a spatio-temporal grid with minimum distortions and managing complexities such as spatio-temporal autocorrelation issues. Addressing these challenges is pivotal for the effective application of Artificial Intelligence (AI) approaches. Furthermore, adhering to open science principles for data dissemination, reproducibility, visualisation, and reuse is crucial for fostering sustainable research. Overcoming these challenges offers a substantial opportunity to advance data-driven Earth system research, unlocking the full potential of an integrated, multidimensional view of Earth system processes. This is particularly true when such research is coupled with innovative research paradigms and technological progress.},
  copyright = {http://creativecommons.org/licenses/by/4.0},
  langid = {english},
}


@article{palumbo_2017_building,
    title = {Building capacity in remote sensing for conservation: present and future challenges},
    volume = {3},
    issn = {2056-3485, 2056-3485},
    shorttitle = {Building capacity in remote sensing for conservation},
    url = {https://zslpublications.onlinelibrary.wiley.com/doi/10.1002/rse2.31},
    doi = {10.1002/rse2.31},
    language = {en},
    number = {1},
    urldate = {2024-01-19},
    journal = {Remote Sensing in Ecology and Conservation},
    author = {Palumbo, Ilaria and Rose, Robert A. and Headley, Rachel M. K. and Nackoney, Janet and Vodacek, Anthony and Wegmann, Martin},
    editor = {Nagendra, Harini and Rocchini, Duccio},
    month = mar,
    year = {2017},
    pages = {21--29},
}
@article{radocaj_2020_global,
    title = {Global {Open} {Data} {Remote} {Sensing} {Satellite} {Missions} for {Land} {Monitoring} and {Conservation}: {A} {Review}},
    volume = {9},
    issn = {2073-445X},
    shorttitle = {Global {Open} {Data} {Remote} {Sensing} {Satellite} {Missions} for {Land} {Monitoring} and {Conservation}},
    url = {https://www.mdpi.com/2073-445X/9/11/402},
    doi = {10.3390/land9110402},
    language = {en},
    number = {11},
    urldate = {2024-01-19},
    journal = {Land},
    author = {Radočaj, Dorijan and Obhođaš, Jasmina and Jurišić, Mladen and Gašparović, Mateo},
    month = oct,
    year = {2020},
    pages = {402},
}


@article{ramachandran_2021_open,
    title = {From {Open} {Data} to {Open} {Science}},
    volume = {8},
    issn = {2333-5084, 2333-5084},
    url = {https://onlinelibrary.wiley.com/doi/10.1029/2020EA001562},
    doi = {10.1029/2020EA001562},
    language = {en},
    number = {5},
    urldate = {2023-03-22},
    journal = {Earth and Space Science},
    author = {Ramachandran, Rahul and Bugbee, Kaylin and Murphy, Kevin},
    month = may,
    year = {2021},
}

@article{Sudmanns_2020_big,
  title = {Big {{Earth}} Data: Disruptive Changes in {{Earth}} Observation Data Management and Analysis?},
  author = {Martin Sudmanns, Dirk Tiede, Stefan Lang and Blaschke, Thomas},
  year = {2020},
  journal = {International Journal of Digital Earth},
  volume = {13},
  number = {7},
  eprint = {https://doi.org/10.1080/17538947.2019.1585976},
  pages = {832--850},
  publisher = {Taylor \& Francis},
  doi = {10.1080/17538947.2019.1585976}
}

@article{truckenbrodt_2019_Sentinel1ARD,
  title = {Towards {{Sentinel-1 SAR Analysis-Ready Data}}: {{A Best Practices Assessment}} on {{Preparing Backscatter Data}} for the {{Cube}}},
  shorttitle = {Towards {{Sentinel-1 SAR Analysis-Ready Data}}},
  author = {Truckenbrodt, John and Freemantle, Terri and Williams, Chris and Jones, Tom and Small, David and Dubois, Cl{\'e}mence and Thiel, Christian and Rossi, Cristian and Syriou, Asimina and Giuliani, Gregory},
  year = {2019},
  month = jul,
  journal = {Data},
  volume = {4},
  number = {3},
  pages = {93},
  issn = {2306-5729},
  doi = {10.3390/data4030093},
  urldate = {2025-03-09},
  abstract = {This study aims at assessing the feasibility of automatically producing analysis-ready radiometrically terrain-corrected (RTC) Synthetic Aperture Radar (SAR) gamma nought backscatter data for ingestion into a data cube for use in a large spatio-temporal data environment. As such, this study investigates the analysis readiness of different openly available digital elevation models (DEMs) and the capability of the software solutions SNAP and GAMMA in terms of overall usability as well as backscatter data quality. To achieve this, the study builds on the Python library pyroSAR for providing the workflow implementation test bed and provides a Jupyter notebook for transparency and future reproducibility of performed analyses. Two test sites were selected, over the Alps and Fiji, to be able to assess regional differences and support the establishment of the Swiss and Common Sensing Open Data cubes respectively.},
  copyright = {https://creativecommons.org/licenses/by/4.0/},
  langid = {english},
}


@article{wagemann_2021_user,
    title = {A user perspective on future cloud-based services for {Big} {Earth} data},
    volume = {14},
    issn = {1753-8947, 1753-8955},
    url = {https://www.tandfonline.com/doi/full/10.1080/17538947.2021.1982031},
    doi = {10.1080/17538947.2021.1982031},
    language = {en},
    number = {12},
    urldate = {2024-01-19},
    journal = {International Journal of Digital Earth},
    author = {Wagemann, Julia and Siemen, Stephan and Seeger, Bernhard and Bendix, Jörg},
    month = dec,
    year = {2021},
    pages = {1758--1774},
}

@article{wagemann_2022_FiveGuidingPrinciples,
  title = {Five {{Guiding Principles}} to {{Make Jupyter Notebooks Fit}} for {{Earth Observation Data Education}}},
  author = {Wagemann, Julia and Fierli, Federico and Mantovani, Simone and Siemen, Stephan and Seeger, Bernhard and Bendix, J{\"o}rg},
  year = {2022},
  month = jul,
  journal = {Remote Sensing},
  volume = {14},
  number = {14},
  pages = {3359},
  issn = {2072-4292},
  doi = {10.3390/rs14143359},
  urldate = {2025-04-28},
  abstract = {There is a growing demand to train Earth Observation (EO) data users in how to access and use existing and upcoming data. A promising tool for data-related training is computational notebooks, which are interactive web applications that combine text, code and computational output. Here, we present the Learning Tool for Python (LTPy), which is a training course (based on Jupyter notebooks) on atmospheric composition data. LTPy consists of more than 70 notebooks and has taught over 1000 EO data users so far, whose feedback is overall positive. We adapted five guiding principles from different fields (mainly scientific computing and Jupyter notebook research) to make the Jupyter notebooks more educational and reusable. The Jupyter notebooks developed (i) follow the literate programming paradigm by a text/code ratio of 3, (ii) use instructional design elements to improve navigation and user experience, (iii) modularize functions to follow best practices for scientific computing, (iv) leverage the wider Jupyter ecosystem to make content accessible and (v) aim for being reproducible. We see two areas for future developments: first, to collect feedback and evaluate whether the instructional design elements proposed meet their objective; and second, to develop tools that automatize the implementation of best practices.},
  copyright = {https://creativecommons.org/licenses/by/4.0/},
  langid = {english},
}

@misc{Gardner_Scambos_2022, 
    title={MEaSUREs ITS_LIVE Landsat Image-Pair Glacier and Ice Sheet Surface Velocities, Version 1}, 
    url={https://nsidc.org/data/NSIDC-0775/versions/1}, DOI={10.5067/IMR9D3PEI28U}, 
    publisher={NASA National Snow and Ice Data Center Distributed Active Archive Center}, 
    author={Gardner, A., M. Fahnestock and Scambos, T.}, year={2022} }

 @misc{RGI_Consortium_2023, 
    title={Randolph Glacier Inventory - A Dataset of Global Glacier Outlines, Version 7}, 
    url={https://nsidc.org/data/NSIDC-0770/versions/7}, 
    DOI={10.5067/F6JMOVY5NAVZ}, 
    publisher={National Snow and Ice Data Center}, author={RGI Consortium, .}, 
    year={2023} }

@misc{ASF_Sentinel1,
    title={Sentinel-1 Sentinel-1 RTC Product Guide},
    url={https://hyp3-docs.asf.alaska.edu/guides/rtc_product_guide/},
    publisher={Alaska Satellite Facility Distributed Active Archive Center},
    year={2023},
    
}

@misc{Procida_Diataxis_documentation_framework,
author = {Procida, Daniele},
title = {{Diátaxis documentation framework}},
url = {https://diataxis.fr/}
}

@Conference{Marshall_Cherian_Henderson_2023, 
    title={Tidy geospatial data cubes}, 
    url={https://doi.org/10.25080/gerudo-f2bc6f59-034}, 
    DOI={10.25080/gerudo-f2bc6f59-034}, 
    publisher={SciPy Conference 2023}, 
    author={Marshall, Emma and Cherian, Deepak and Henderson, Scott}, 
    year={2023}, month=aug }

@article{Kluyver2016jupyter,
    Title = {Jupyter Notebooks -- a publishing format for reproducible computational workflows},
    Author = {Thomas Kluyver and Benjamin Ragan-Kelley and Fernando P{\'e}rez and Brian Granger and Matthias Bussonnier and Jonathan Frederic and Kyle Kelley and Jessica Hamrick and Jason Grout and Sylvain Corlay and Paul Ivanov and Dami{\'a}n Avila and Safia Abdalla and Carol Willing},
    url={https://ebooks.iospress.nl/publication/42900},
    DOI={10.3233/978-1-61499-649-1-87},
    Booktitle = {Positioning and Power in Academic Publishing: Players, Agents and Agendas},
    Editor = {F. Loizides and B. Schmidt},
    Organization = {IOS Press},
    Pages = {87 - 90},
    Year = {2016}
}

 @software{Caswell_Droettboom_Lee_Andrade_Hoffmann_Hunter_Klymak_Firing_Stansby_Varoquaux_etal_2021, 
        title={matplotlib/matplotlib: REL: v3.5.1}, 
        url={https://doi.org/10.5281/zenodo.5773480}, 
        DOI={10.5281/zenodo.5773480}, 
        publisher={Zenodo}, 
        author={Caswell, Thomas A. and Droettboom, Michael and Lee, Antony and Andrade, Elliott Sales de and Hoffmann, Tim and Hunter, John and Klymak, Jody and Firing, Eric and Stansby, David and Varoquaux, Nelle and Nielsen, Jens Hedegaard and Root, Benjamin and May, Ryan and Elson, Phil and Seppänen, Jouni K. and Dale, Darren and Lee, Jae-Joon and McDougall, Damon and Straw, Andrew and Hobson, Paul and hannah and Gohlke, Christoph and Vincent, Adrien F. and Yu, Tony S. and Ma, Eric and Silvester, Steven and Moad, Charlie and Kniazev, Nikita and Ernest, Elan and Ivanov, Paul}, 
        year={2021}, month=dec }
        

 @software{JupyterBookCommunity_2021, 
        title={Jupyter Book}, 
        url={https://doi.org/10.5281/zenodo.4539666}, 
        DOI={10.5281/zenodo.4539666}, 
        publisher={Zenodo}, 
        author={{Executable Books Community}}, 
        year={2021}, month=feb }

 @software{Gillies_van_der_Wel_Van_den_Bossche_Taves_Arnott_Ward_others_2022, 
        title={Shapely}, 
        url={https://doi.org/10.5281/zenodo.7428463}, 
        DOI={10.5281/zenodo.7428463}, 
        publisher={Zenodo}, 
        author={Gillies, Sean and van der Wel, Casper and Van den Bossche, Joris and Taves, Mike W. and Arnott, Joshua and Ward, Brendan C. and others}, 
        year={2022}, month=dec }

 @article{Hoyer_Hamman_2017, 
        title={xarray: N-D labeled Arrays and Datasets in Python}, 
        volume={5}, 
        ISSN={2049-9647}, 
        url={https://openresearchsoftware.metajnl.com/article/10.5334/jors.148/}, 
        DOI={10.5334/jors.148}, number={1}, 
        journal={Journal of Open Research Software}, 
        author={Hoyer, Stephan and Hamman, Joe}, 
        year={2017}, month=apr, pages={10}, language={en} }

 @software{Jordahl_Bossche_Fleischmann_McBride_Wasserman_Badaracco_Gerard_Snow_Tratner_Perry_etal_2021, 
        title={geopandas/geopandas: v0.10.0}, 
        url={https://doi.org/10.5281/zenodo.5546558}, 
        DOI={10.5281/zenodo.5546558}, 
        publisher={Zenodo}, 
        author={Jordahl, Kelsey and Bossche, Joris Van den and Fleischmann, Martin and McBride, James and Wasserman, Jacob and Badaracco, Adrian Garcia and Gerard, Jeffrey and Snow, Alan D. and Tratner, Jeff and Perry, Matthew and Farmer, Carson and Hjelle, Geir Arne and Cochran, Micah and Gillies, Sean and Culbertson, Lucas and Bartos, Matt and Ward, Brendan and Caria, Giacomo and Taves, Mike and Eubank, Nick and sangarshanan and Flavin, John and Richards, Matt and Rey, Sergio and maxalbert and Bilogur, Aleksey and Ren, Christopher and Arribas-Bel, Dani and Mesejo-León, Daniel and Wasser, Leah}, 
        year={2021}, month=oct }

 @software{Miles_Kirkham_Durant_Bourbeau_Onalan_Hamman_Patel_shikharsg_Rocklin_dussin_etal_2020, 
        title={zarr-developers/zarr-python: v2.4.0}, 
        url={https://doi.org/10.5281/zenodo.3773450}, 
        DOI={10.5281/zenodo.3773450}, publisher={Zenodo}, 
        author={Miles, Alistair and Kirkham, John and Durant, Martin and Bourbeau, James and Onalan, Tarik and Hamman, Joe and Patel, Zain and shikharsg and Rocklin, Matthew and dussin, raphael and Schut, Vincent and Andrade, Elliott Sales de and Abernathey, Ryan and Noyes, Charles and sbalmer and bot, pyup io and Tran, Tommy and Saalfeld, Stephan and Swaney, Justin and Moore, Josh and Jevnik, Joe and Kelleher, Jerome and Funke, Jan and Sakkis, George and Barnes, Chris and Banihirwe, Anderson}, 
        year={2020}, month=apr }
 @software{Snow_Brochart_Raspaud_Taves_Bell_RichardScottOZ_Chegini_Amici_Braun_Annex_etal_2022, 
        title={corteva/rioxarray: 0.12.1}, 
        url={https://doi.org/10.5281/zenodo.7080195}, 
        DOI={10.5281/zenodo.7080195}, 
        publisher={Zenodo}, 
        author={Snow, Alan D. and Brochart, David and Raspaud, Martin and Taves, Mike and Bell, Ray and RichardScottOZ and Chegini, Taher and Amici, Alessandro and Braun, Rémi and Annex, Andrew and Brandt, Carlos H. and Hoese, David and Bunt, Fred and GBallesteros and Scheick, Jessica and Hamman, Joe and jonasViehweger and Zehner, Markus and Cordeiro, Mauricio and Henderson, Scott and Miller, Seth and Badger, The Gitter and Augspurger, Tom and apiwat-chantawibul and pmallas}, 
        year={2022}, month=sep }
 @software{Source_McFarland_Emanuele_Morris_Augspurger_2022, 
        title={microsoft/PlanetaryComputer: October 2022}, 
        url={https://doi.org/10.5281/zenodo.7261897},
        DOI={10.5281/zenodo.7261897}, 
        publisher={Zenodo}, 
        author={{Microsoft Open Source} and McFarland, Matt and Emanuele, Rob and Morris, Dan and Augspurger, Tom}, 
        year={2022}, month=oct }
 
  @software{Pandasteam_2024,
        title = {Pandas-Dev/Pandas: {{Pandas}}},
        author = {{The pandas development team}},
        year = {2024},
        month = jan,
        publisher = {Zenodo},
        version = {latest},
        doi = {10.5281/zenodo.10537285}
      }
@InProceedings{ mckinney-proc-scipy-2010,
        author    = { {W}es {M}c{K}inney },
        title     = { {D}ata {S}tructures for {S}tatistical {C}omputing in {P}ython },
        booktitle = { {P}roceedings of the 9th {P}ython in {S}cience {C}onference },
        pages     = { 56 - 61 },
        year      = { 2010 },
        editor    = { {S}t\'efan van der {W}alt and {J}arrod {M}illman },
        doi       = { 10.25080/Majora-92bf1922-00a }
      }


@InProceedings{DaskLibrary,
  author    = { {M}atthew {R}ocklin },
  title     = { {D}ask: {P}arallel {C}omputation with {B}locked algorithms and {T}ask {S}cheduling },
  booktitle = { {P}roceedings of the 14th {P}ython in {S}cience {C}onference },
  pages     = { 126 - 132 },
  year      = { 2015 },
  editor    = { {K}athryn {H}uff and {J}ames {B}ergstra },
  doi       = { 10.25080/Majora-7b98e3ed-013 }
}