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.

Publications using sits

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

2024

2023

2021

2020

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.