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).

The MODIS-Aqua Monthly Remote Sensing Reflectance (Rrs, unit sr-1) provides 8 spectral bands temporal resolution of one month and spatial resolution of 1 km over the Brazil oceanic waters and open ocean South Atlantic waters. This collection captures 7 visible, and 1 infrared channels using Level-1A images acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard the NASA's Aqua satellite. The Level-1A data were processed into Level-1B and GEO using the Data Processing Tools (modis_L1B) from SeaWiFS Data Analysis System (SeaDAS) software. The Level-1B and GEO data were applied in the atmospheric correction OC-SMART (Fan et al., 2021) by the National Institute for Space Research (INPE, Brazil) and the Laboratoire d'Océanologie et de Géosciences (LOG, France) generating the Level-2 data. The Level-2 data has been mosaicked to generate daily maps capturing the complete Brazilian ocean waters. The daily mosaic data were reprojected to geographical (lat/lon) coordinates using as reference the European Space Agency (ESA) Ocean Colour - Climate Change Initiative (OC-CCI) into Level-3 grid. Both, the mosaic and the reprojection were done using the Sentinel Application Platform (SNAP) on its version 10. Finally, the daily reprojected data were temporal merged to create the monthly Rrs products using the geometric mean.

Kd data generated based Semi-Analytical Algorithm developed by Maciel et al. (2020) (https://doi.org/10.1016/j.isprsjprs.2020.10.009). The underwater light field modeling is essential for the understanding of biogeochemical processes, such as photosynthesis, carbon fluxes, and sediment transports in inland waters. Water-column light attenuation can be quantified by the diffuse attenuation coefficient of the downwelling irradiance (Kd). This dataset represents the Kd estimate for a Sentinel-2/MSI time-series at Curuai Lake region - Lower Amazon floodplains. This time-series data was generated for 66 Sentinel-2/MSI scenes (08/2015 to 09/2019) during the research paper titled Mapping of diffuse attenuation coefficient in optically complex waters of amazon floodplain lakes. This product was funded by the Brazilian Development Bank (BNDES), Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).

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).

CBERS-4A/WPM - Level-4 Digital Number product. L4 product provides orthorectified images.

CBERS-4A/WFI image mosaic of Brazil Paraíba State with 55m of spatial resolution. The mosaic was prepared in order to demonstrate the technological capabilities of the Brazil Data Cube project tools. The false color composition is based on the WFI bands 16, 15 and 14 assigned to RGB channels. The temporal composition encompasses 03-months of images, starting in July 2020 and ending in September 2020, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 50 CBERS-4A scenes and was generated based on an existing CBERS-4A/WFI image collection.

CBERS-4A/WFI Level-4 Digital Number product. L4 product provides orthorectified images.

CBERS-4/MUX - Level-4 Surface Reflectance product over Brazil and part of South America. L4 SR product provides orthorectified surface reflectance images. This dataset is provided as Cloud Optimized GeoTIFF (COG).

AMAZONIA-1/WFI - Level-2 Digital Number product. Level 2 products have radiometric correction and geometric correction using satellite ephemeris and attitude data (system correction).

AMAZONIA-1/WFI - Level-4 Surface Reflectance product. L4 SR product provides orthorectified surface reflectance images. This dataset is provided as Cloud Optimized GeoTIFF (COG).