Latitude: 07° 49' 38" S (-7.827200) Longitude: 38° 07' 20" O (-38.1222) Altitude: 1123 Fuso Horário: UTC -3 Tipo: ANEMOMÉTRICA

A cidade de Brasília possui clima tropical de altitude, característico de planaltos e serras. A região apresenta regime de precipitação bem definido. A estação chuvosa tem início em outubro e estende-se até março. A estação seca acontece no restante do ano, desde abril até setembro. A estação chuvosa é causada pelo forte aquecimento da atmosfera na Amazônia, que favorece o desenvolvimento de um sistema quase estacionário em altos níveis, a Alta da Bolívia. Como resultado dessa circulação, observa-se nos baixos níveis uma região de baixa pressão, chamada de Baixa do Chaco. Essa região de baixa pressão e a convergência de ar são os principais fatores que provocam a precipitação neste período, responsável por 70% do total médio anual. O total médio anual de precipitação em Brasília é aproximadamente 1700mm. A umidade relativa do ar, no período de seca, cai de valores superiores a 70% para 20%. Coincidindo com o período mais quente, nos meses de agosto e setembro, a umidade relativa do ar pode chegar a 12%, valor típico de deserto. A temperatura média atinge valores em torno de 22ºC sendo que o pico anual ocorre nos meses de primavera. Os meses de junho e julho são os que apresentam as temperaturas mais baixas do ano, em média 18ºC. Tipos de solo encontrados: Latossolo Vermelho-Escuro, Latossolo Vermelho-Amarelo, Solos Aluviais, Cambissolos. Latitude: 15° 36' 03" S (-15,60083) Longitude: 47° 42' 47" O (-47,71306) Altitude: 1023 Fuso Horário: UTC -3

Sentinel-2 image mosaic of Brazilian Cerrado biome with 10m of spatial resolution. The mosaic was prepared in support of TerraClass project. The false color composition is based on the MSI bands 8, 4 and 3 assigned to RGB channels. The temporal composition encompasses 04-months of images, starting in june 2022 and ending in September 2022, 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 14000 Sentinel-2 scenes and was generated based on an existing data cube of Sentinel-2 images.

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 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 Sentinel-2 data cube. The input datacube was Landsat-8 - OLI - Cube Stack 16 days - v001, which was deprecated. The classification was made using 922 samples (Pasture: 258; Agriculture: 242; Natural Vegetation: 422). 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. 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 <a href="https://www.mdpi.com/2072-4292/12/24/4033" target="_blank">Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products.</a> 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).

CBERS-4A/WFI Level-2 Digital Number product. Level 2 products have radiometric correction and geometric correction using satellite ephemeris and attitude data (system correction).

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).