rsi: Efficiently Retrieve and Process Satellite Imagery

Downloads spatial data from spatiotemporal asset catalogs ('STAC'), computes standard spectral indices from the Awesome Spectral Indices project (Montero et al. (2023) doi:10.1038/s41597-023-02096-0 ) against raster data, and glues the outputs together into predictor bricks. Methods focus on interoperability with the broader spatial ecosystem; function arguments and outputs use classes from 'sf' and 'terra', and data downloading functions support complex 'CQL2' queries using 'rstac'.

See also

Useful links:

• https://github.com/Permian-Global-Research/rsi

• https://permian-global-research.github.io/rsi/

• Report bugs at https://github.com/Permian-Global-Research/rsi/issues

Author

Maintainer: Michael Mahoney mike.mahoney.218@gmail.com (ORCID)

Other contributors:

• Felipe Carvalho (Felipe reviewed the package (v. 0.3.0) for rOpenSci, see <https://github.com/ropensci/software-review/issues/636>) [reviewer]

• Michael Sumner (Michael reviewed the package (v. 0.3.0) for rOpenSci, see <https://github.com/ropensci/software-review/issues/636>) [reviewer]

• Permian Global [copyright holder, funder]