Acknowledgements

Funding Sources

The authors acknowledge the funders that supported the development of sits:

• Amazon Fund, established by Brazil with financial contribution from Norway, through contract 17.2.0536.1. between the Brazilian Development Bank (BNDES) and the Foundation for Science, Technology, and Space Applications (FUNCATE), for the establishment of the Brazil Data Cube.

• Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for grants 312151/2014-4 and 140684/2016-6.

• Sao Paulo Research Foundation (FAPESP) under eScience Program grant 2014/08398-6, for providing MSc, PhD, and post-doc scholarships, equipment, and travel support.

• International Climate Initiative of the Germany Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (IKI) under grant 17-III-084-Global-A-RESTORE+ (“RESTORE+: Addressing Landscape Restoration on Degraded Land in Indonesia and Brazil”).

• Microsoft Planetary Computer initiative under the GEO-Microsoft Cloud Computer Grants Programme.

• Instituto Clima e Sociedade, under the project grant “Modernization of PRODES and DETER Amazon monitoring systems”.

• Open-Earth-Monitor Cyberinfrastructure project, which has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No. 101059548.

• FAO-EOSTAT initiative, which uses next generation Earth observation tools to produce land cover and land use statistics.

Community Contributions

The authors thank the R-spatial community for their foundational work, including Marius Appel, Tim Appelhans, Robert Hijmans, Jakub Nowosad, Edzer Pebesma, and Martijn Tennekes for their R packages gdalcubes, leafem, terra, supercells, sf/stars, and tmap. We are grateful for the work of Dirk Eddelbuettel on Rcpp and RcppArmadillo and Ron Wehrens in package kohonen. We are much indebted to Hadley Wickham for the tidyverse, Daniel Falbel for the torch and luz packages, and the RStudio team for package leaflet. The multiple authors of machine learning packages randomForest, e1071, and xgboost provided robust algorithms. We would like to thank Python developers who shared their deep learning algorithms for image time series classification: Vivien Sainte Fare Garnot, Zhiguang Wang, Maja Schneider, and Marc Rußwurm. The first author also thanks Roger Bivand for his benign influence in all things related to R.

Reproducible papers and books used in building sits

We thank the authors of the following papers for making their code and papers open and reusable. Their contribution has been essential to build sits.

• Edzer Pebesma, Simple Features for R: Standardized Support for Spatial Vector Data. R Journal, 10(1), 2018.

• Martin Tennekes, tmap: Thematic Maps in R. Journal of Statistical Software, 84(6), 1–39, 2018.

• Ron Wehrens and Johannes Kruisselbrink, Flexible Self-Organising Maps in kohonen 3.0. Journal of Statistical Software, 87, 7, 2018.

• Hassan Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, and Pierre-Alain Muller, Deep learning for time series classification: a review. Data Mining and Knowledge Discovery, 33(4): 917–963, 2019.

• Charlotte Pelletier, Geoffrey Webb, and Francois Petitjean. Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series. Remote Sensing 11 (5), 2019.

• Marc Rußwurm, Charlotte Pelletier, Maximilian Zollner, Sèbastien Lefèvre, and Marco Körner, Breizhcrops: a Time Series Dataset for Crop Type Mapping. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS, 2020.

• Marius Appel and Edzer Pebesma, On-Demand Processing of Data Cubes from Satellite Image Collections with the Gdalcubes Library. Data 4 (3): 1–16, 2020.

• Vivien Garnot, Loic Landrieu, Sebastien Giordano, and Nesrine Chehata, Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention, Conference on Computer Vision and Pattern Recognition, 2020.

• Vivien Garnot and Loic Landrieu, Lightweight Temporal Self-Attention for Classifying Satellite Images Time Series, 2020.

• Maja Schneider, Marco Körner, Re: Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention ReScience C 7 (2), 2021.

• Rolf Simoes, Felipe Souza, Mateus Zaglia, Gilberto Queiroz, Rafael dos Santos and Karine Ferreira, Rstac: An R Package to Access Spatiotemporal Asset Catalog Satellite Imagery. IGARSS, 2021, pp. 7674-7677.

• Jakub Nowosad, Tomasz Stepinksi, Extended SLIC superpixels algorithm for applications to non-imagery geospatial rasters. International Journal of Applied Earth Observations and Geoinformation, 2022.

• Sigrid Keydana, Deep Learning and Scientific Computing with R torch, Chapman and Hall/CRC, London, 2023.

• Robin Lovelace, Jakub Nowosad, Jannes Münchow, Geocomputation with R. Chapman and Hall/CRC, London, 2023.

• Edzer Pebesma, Roger Bivand, Spatial Data Science: With applications in R. Chapman and Hall/CRC, London, 2023.

Publications using sits

This section gathers the publications that have used sits to generate their results.

2024

• Giuliani, Gregory. Time-First Approach for Land Cover Mapping Using Big Earth Observation Data Time-Series in a Data Cube – a Case Study from the Lake Geneva Region (Switzerland). Big Earth Data, 2024.

• Werner, João, Mariana Belgiu et al., Mapping Integrated Crop–Livestock Systems Using Fused Sentinel-2 and PlanetScope Time Series and Deep Learning. Remote Sensing 16, no. 8 (January 2024): 1421.

2023

• Hadi, Firman, Laode Muhammad Sabri, Yudo Prasetyo, and Bambang Sudarsono. Leveraging Time-Series Imageries and Open Source Tools for Enhanced Land Cover Classification. In IOP Conference Series: Earth and Environmental Science, 1276:012035. IOP Publishing, 2023.

• Bruno Adorno, Thales Körting, and Silvana Amaral, Contribution of time-series data cubes to classify urban vegetation types by remote sensing. Urban Forest & Urban Greening, 79, 127817, 2023.

2021

• Lorena Santos, Karine R. Ferreira, Gilberto Camara, Michelle Picoli, and Rolf Simoes, Quality control and class noise reduction of satellite image time series. ISPRS Journal of Photogrammetry and Remote Sensing, 177, 75–88, 2021.

• Lorena Santos, Karine Ferreira, Michelle Picoli, Gilberto Camara, Raul Zurita-Milla and Ellen-Wien Augustijn, Identifying Spatiotemporal Patterns in Land Use and Cover Samples from Satellite Image Time Series. Remote Sensing, 13(5), 974, 2021.

2020

• Rolf Simoes, Michelle Picoli, Gilberto Camara, Adeline Maciel, Lorena Santos, Pedro Andrade, Alber Sánchez, Karine Ferreira, and Alexandre Carvalho, Land use and cover maps for Mato Grosso State in Brazil from 2001 to 2017. Nature Scientific Data, 7, article 34, 2020.

• Michelle Picoli, Ana Rorato, Pedro Leitão, Gilberto Camara, Adeline Maciel, Patrick Hostert, and Ieda Sanches, Impacts of Public and Private Sector Policies on Soybean and Pasture Expansion in Mato Grosso—Brazil from 2001 to 2017. Land, 9(1), 2020.

• Karine Ferreira, Gilberto Queiroz et al., Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products. Remote Sensing, 12, 4033, 2020.

• Adeline Maciel, Lubia Vinhas, Michelle Picoli, and Gilberto Camara, Identifying Land Use Change Trajectories in Brazil’s Agricultural Frontier. Land, 9, 506, 2020.

2018

• Michelle Picoli, Gilberto Camara, et al., Big Earth Observation Time Series Analysis for Monitoring Brazilian Agriculture. ISPRS Journal of Photogrammetry and Remote Sensing, 145, 328–339, 2018.

AI support in preparing the book

The authors have use Generative AI tools (Chat-GPT, Grammarly and ProWritingAid) to improve readability and language of the work. The core technical and scientific content of the book has been prepared exclusively by the authors. Assistance from Generative AI has been limited to improving definitions and making the text easier to follow.