United States Geological Survey (USGS)
https://fedeo.ceos.org/series/eo:organisationName/OpenGeoHub Foundation/landsat-tsirf-bimonthly-1/2022?httpAccept=application/ld%2Bjson
https://fedeo.ceos.org/series/eo:organisationName/OpenGeoHub Foundation/landsat-tsirf-bimonthly-1?httpAccept=application/ld%2Bjson
https://fedeo.ceos.org/series/eo:organisationName/OpenGeoHub Foundation/landsat-tsirf-bimonthly-1/2022/01?httpAccept=application/ld%2Bjson
https://fedeo.ceos.org/series/eo:organisationName/OpenGeoHub Foundation/landsat-tsirf-bimonthly-1/2022/02?httpAccept=application/ld%2Bjson
https://fedeo.ceos.org/series/eo:organisationName/OpenGeoHub Foundation/landsat-tsirf-bimonthly-1/2022/03?httpAccept=application/ld%2Bjson
https://fedeo.ceos.org/series/eo:organisationName/OpenGeoHub Foundation/landsat-tsirf-bimonthly-1/2022/04?httpAccept=application/ld%2Bjson
https://fedeo.ceos.org/series/eo:organisationName/OpenGeoHub Foundation/landsat-tsirf-bimonthly-1/2022/05?httpAccept=application/ld%2Bjson
https://fedeo.ceos.org/series/eo:organisationName/OpenGeoHub Foundation/landsat-tsirf-bimonthly-1/2022/06?httpAccept=application/ld%2Bjson
https://fedeo.ceos.org/series/eo:organisationName/OpenGeoHub Foundation/landsat-tsirf-bimonthly-1/2022/07?httpAccept=application/ld%2Bjson
https://fedeo.ceos.org/series/eo:organisationName/OpenGeoHub Foundation/landsat-tsirf-bimonthly-1/2022/08?httpAccept=application/ld%2Bjson
https://fedeo.ceos.org/series/eo:organisationName/OpenGeoHub Foundation/landsat-tsirf-bimonthly-1/2022/09?httpAccept=application/ld%2Bjson
https://fedeo.ceos.org/series/eo:organisationName/OpenGeoHub Foundation/landsat-tsirf-bimonthly-1/2022/10?httpAccept=application/ld%2Bjson
https://fedeo.ceos.org/series/eo:organisationName/OpenGeoHub Foundation/landsat-tsirf-bimonthly-1/2022/11?httpAccept=application/ld%2Bjson
https://fedeo.ceos.org/series/eo:organisationName/OpenGeoHub Foundation/landsat-tsirf-bimonthly-1/2022/12?httpAccept=application/ld%2Bjson
Brazil, cloud-free, and reconstructed historical Landsat spectral bands are provided at a spatial resolution of 30 meters and temporal resolution of bimonthly intervals (six images per year) from 1997 onwards. The dataset was generated using the GLAD Landsat Analysis Ready Data version 2 as the primary input for temporal aggregation and the imputation of missing values. Detailed information on the data processing methodology in [Consoli et al., 2024](https://peerj.com/articles/18585/). This subset of the collection provides Brazil mosaics for all Landsat bands (RGB, NIR, SWIR-1, SWIR-2 & Thermal) as Cloud Optimized GeoTIFF (COG).