This land cover classification refers to a study area in Mato Grosso state, in the Cerrado biome. For this map, the CBERS-4/AWFI monthly data cube was used, with a spatial resolution of 64 meters, using the best pixel composition function (Stack). This experiment uses the time series of an agricultural calendar year, from September 2018 to August 2019, extracted from the CBERS-4/AWFI data cube. 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 applying the random forest algorithm. The classification quality assessment using 5-fold cross-validation (Wiens et al., 2008) of the training samples showed an overall accuracy of 97.0% and a Kappa index of 0.96. For more information see the paper "CBERS DATA CUBE: A POWERFUL TECHNOLOGY FOR MAPPING AND MONITORING BRAZILIAN BIOMES". To access this resource in GeoTIFF format it is necessary to have an access key provided by the BDC-OAuth service.

This land cover classification refers to a study area in Bahia state, in the Cerrado biome. For this map, the Landsat-8 monthly data cube was used, with a spatial resolution of 30 meters, using the best pixel composition function (Stack). This experiment uses the time series of an agricultural calendar year, from September 2018 to August 2019, extracted from the Sentinel-2 data cube. The classification was made using 922 samples (Pasture: 258; Agriculture: 242; Natural Vegetation: 422) and the following data cube bands: red, green, blue, and near-infrared (NIR) along with the NDVI and EVI indices. We trained a multi-layer perceptron for a deep learning classification network to classify the data cube using sits R package. Validation was done using good practice guidelines by Olofsson. The validation was done independently for each map using the PRODES Cerrado data of 2019. This data obtained overall accuracy (OA) 0.90. For more information see the paper "Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products". To access this resource in GeoTIFF format it is necessary to have an access key provided by the BDC-OAuth service.

This land cover classification refers to a study area in Bahia state, in the Cerrado biome. For this map, the CBERS-4/AWFI monthly data cube was used, with a spatial resolution of 64 meters, using the best pixel composition function (Stack). This experiment uses the time series of an agricultural calendar year, from September 2018 to August 2019, extracted from the CBERS-4/AWFI data cube. The classification was made using 922 samples (Pasture: 258; Agriculture: 242; Natural Vegetation: 422) and the following data cube bands: red, green, blue, and near-infrared (NIR) along with the NDVI and EVI indices. We trained a multi-layer perceptron for a deep learning classification network to classify the data cube using sits R package. Validation was done using good practice guidelines by Olofsson. The validation was done independently for each map using the PRODES Cerrado data of 2019. This data obtained overall accuracy (OA) 0.74. For more information see the paper "Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products". To access this resource in GeoTIFF format it is necessary to have an access key provided by the BDC-OAuth service.

This land cover classification refers to a study area in Mato Grosso state, in the Cerrado biome. For this map, the CBERS-4/AWFI monthly data cube was used, with a spatial resolution of 64 meters, using the best pixel composition function (Stack). This experiment uses the time series of an agricultural calendar year, from September 2018 to August 2019, extracted from the CBERS-4/AWFI data cube. 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. To access this resource in GeoTIFF format it is necessary to have an access key provided by the BDC-OAuth service.

This land cover classification refers to a study area in Goiás state, in the Cerrado biome. For this map, the CBERS-4/AWFI monthly data cube was used, with a spatial resolution of 64 meters, using the best pixel composition function (Stack). This experiment uses the time series of an agricultural calendar year, from September 2018 to August 2019, extracted from the CBERS-4/AWFI data cube. The classification was made using 701 samples (Annual Crop: 299; Natural: 202; Pasture: 200) and the following data cube bands: red, green, blue, and near-infrared (NIR) along with the NDVI and EVI indices. We trained a multi-layer perceptron for a deep learning classification network to classify the data cube using sits R package. To access this resource in GeoTIFF format it is necessary to have an access key provided by the BDC-OAuth service.