The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MYD11A2) Version 6.1 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MYD11A2 is a simple average of all the corresponding MYD11A1 LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types.

The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MOD11A2) Version 6.1 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MOD11A2 is a simple average of all the corresponding MOD11A1 LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types.

This is a land cover classification map of Brazilian Mata Atlantica, from January to December of 2017. This classification was made on top of Landsat-8 days data cubes with spatial resolution of 30 meters, using the best pixel composition function named as Least Cloud Cover First (LCF), which was previously named Stack in BDC older versions. The input datacube was Landsat-8 - OLI - Cube Stack 16 days - v001, which was deprecated. The classification model was trained using 13442 sample points of the classes Agriculture 2668, Planted forest 823, Forest (Formação florestal) 3754, Pasture 6197. The spectral bands used were B1, B2, B3, B4, B5, B6, B7, along with the vegetation indices EVI and NDVI; the clouded observation were identified using the Fmask algorithm and estimated using linear interpolation. The classification algorithm was Random Forest. The post-processing consisted on cropping the images to the biome's boundary. This product was funded by the Brazilian Development Bank (BNDES).

This land cover classification refers to a study area in Mato Grosso state, in the Cerrado biome. For this map, the CBERS-4/WFI monthly data cube was used, with a spatial resolution of 64 meters, using the best pixel composition function named as Least Cloud Cover First (LCF), which was previously named Stack in BDC older versions. This experiment uses the time series of an agricultural calendar year, from September 2018 to August 2019, extracted from the CBERS-4/WFI data cube. The input datacube was CBERS-4 (WFI) Cube Identity - v001, which was deprecated. The classification was made using 852 samples (Annual Crop: 257; Natural Vegetation: 245; Pasture: 216; Semi-Perennial Crop: 134) and the following data cube bands: bands red, green, blue, and near-infrared along with the EVI, NDVI, GEMI, GNDVI, NDWI2, PVR indices. We trained a multi-layer perceptron for a deep learning classification network to classify the data cube using sits R package. This product was funded by the Brazilian Development Bank (BNDES).

This is a land cover classification map of Brazilian Cerrado, from August 29th 2017 to August 29th 2018. This classification was made on top of Landsat-8 days data cubes with spatial resolution of 30 meters, using the best pixel composition function named as Least Cloud Cover First (LCF), which was previously named Stack in BDC older versions. The input datacube was Landsat-8 - OLI - Cube Stack 16 days - v001, which was deprecated. The classification model was trained using 48850 sample points spread across the Cerrado biome (Annual Crop 6887, Cerradao 4211, Cerrado 16251, Natural Non Vegetation 38, Open_Cerrado 5658, Pasture 12894, Perennial Crop 68, Silviculture 805, Sugarcane 1775, Water 263). The spectral band used were B1, B2, B3, B4, B5, B6, and B7 along with the vegetation indices EVI and NDVI; the clouded observation were identified using the Fmask4 algorithm and estimated using linear interpolation. The classification algorithm was TempCNN (Deep Learning). This product was funded by the Brazilian Development Bank (BNDES).

This is a land cover classification map of Brazilian Cerrado, from August 2017 to August 2018. This classification was made on top of Landsat-8 monthly cubes with spatial resolution of 30 meters, using the best pixel composition function named as Least Cloud Cover First (LCF), which was previously named Stack in BDC older versions. The input datacube was Landsat-8 - OLI - Cube Stack 16 days - v001, which was deprecated. The classification model was trained using 67359 sample points spread across the whole Cerrado biome (Annual Crop: 9390, Dune Beach: 35, Forest: 5439, Pasture: 19697, Savanna: 30014, Semi-Perennial Crop: 1161, Silviculture: 1268, Water: 355). The spectral band used were B1, B2, B3, B4, B5, B6, and B7 along with the vegetation indices EVI and NDVI; the clouded observation were identified using the Fmask4 algorithm and estimated using linear interpolation. The classification algorithm was Multi-Layer Perceptron (Deep Learning). This product was funded by the Brazilian Development Bank (BNDES).

Earth Observation Data Cube generated from CBERS-4/WFI and CBERS-4A/WFI Level-4 SR products over Brazil extension. This dataset is provided in Cloud Optimized GeoTIFF (COG) file format. The dataset is processed with 64 meters of spatial resolution, reprojected and cropped to BDC_LG grid Version 2 (BDC_LG V2), considering a temporal compositing function of 8 days using the Least Cloud Cover First (LCF) best pixel approach.

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13Q1) Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MYD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value.

Earth Observation Data Cube generated from Copernicus Sentinel-2/MSI Level-2A product over Brazil. This dataset is provided in Cloud Optimized GeoTIFF (COG) file format. The dataset is processed with 10 meters of spatial resolution, reprojected and cropped to BDC_SM grid Version 2 (BDC_SM V2), considering a temporal compositing function of 16 days using the Least Cloud Cover First (LCF) best pixel approach.

This is a land cover classification map of Brazilian Caatinga, from January to December of 2017. This classification was made on top of Landsat-8 16 days data cubes with spatial resolution of 30 meters, using the best pixel composition function named as Least Cloud Cover First (LCF), which was previously named Stack in BDC older versions. The input datacube was Landsat-8 - OLI - Cube Stack 16 days - v001, which was deprecated. The classification model was trained using 9324 sample points of the classes Agriculture 172, Country formation 577, Forest (Formação florestal) 222, Savanna 4819, Pasture 3538. The spectral band used were B1, B2, B3, B4, B5, B6, B7, along with the vegetation indices EVI and NDVI; the clouded observation were identified using the Fmask algorithm and estimated using linear interpolation. The classification algorithm was Random Forest. The post-processing consisted on cropping the images to the biome's boundary. This product was funded by the Brazilian Development Bank (BNDES).