Entre os elementos do sistema climático que influenciam as atividades socioeconômicas, a precipitação apresenta papel fundamental em áreas tropicais. Regiões como África e América do Sul apresentam uma escassez considerável de observações desta variável, uma vez que a rede de estações meteorológicas não cobre sistematicamente todo o território. Neste contexto, produtos de múltiplos sensores, algoritmos e modelos climáticos são utilizados cada vez mais para análises dos elementos atmosféricos, do clima e da influência da precipitação nas características ambientais. Assim, há uma demanda crescente de validação de produtos orbitais e de simulações numéricas de forma a dar suporte aos estudos da distribuição espacial e temporal da precipitação. Este estudo tem como objetivo principal analisar os dados mensais de precipitação do produto Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) e verificar sua similaridade com os dados de estações meteorológicas do conjunto de informações do Centro de Previsão de Tempo e Estudos Climáticos (CPTEC) do Instituto Nacional de Pesquisas Espaciais (INPE) e do Instituto Nacional de Meteorologia (INMET) para o território brasileiro para os anos de 1981 a 2011. Para avaliar o CHIRPS, foram utilizados dados de 183 estações meteorológicas do INMET/CPTEC. Os resultados indicam que todas as regiões políticas do Brasil apresentaram correlação linear alta entre as informações do INMET/CPTEC e CHIRPS (95,4 %), ainda, quando consideramos toda a área do Brasil, a correlação linear média entre os dados é de 97%, significativa a p<0,05, teste t-student. Espacialmente, o noroeste do estado do Amazonas e o sudoeste do Pará apresentaram as maiores diferenças entre o conjunto de dados. As estimativas do CHIRPS ajustadas linearmente com os dados do INMET/CPTEC apresentaram uma concordância mais acentuada.

Deforestation in the Brazilian Amazon is related to the use of fire to remove natural vegetation and install crop cultures or pastures. In this study, we evaluated the relation between deforestation, land-use and land-cover (LULC) drivers and fire emissions in the Apyterewa Indigenous Land, Eastern Brazilian Amazon. In addition to the official Brazilian deforestation data, we used a geographic object-based image analysis (GEOBIA) approach to perform the LULC mapping in the Apyterewa Indigenous Land, and the Brazilian biomass burning emission model with fire radiative power (3BEM_FRP) to estimate emitted particulate matter with a diameter less than 2.5 µm (PM_2.5), a primary human health risk. The GEOBIA approach showed a remarkable advancement of deforestation, agreeing with the official deforestation data, and, consequently, the conversion of primary forests to agriculture within the Apyterewa Indigenous Land in the past three years (200 km²), which is clearly associated with an increase in the PM_2.5 emissions from fire. Between 2004 and 2016 the annual average emission of PM_2.5 was estimated to be 3594 ton year⁻¹, while the most recent interval of 2017–2019 had an average of 6258 ton year⁻¹. This represented an increase of 58% in the annual average of PM_2.5 associated with fires for the study period, contributing to respiratory health risks and the air quality crisis in Brazil in late 2019. These results expose an ongoing critical situation of intensifying forest degradation and potential forest collapse, including those due to a savannization forest-climate feedback, within “protected areas” in the Brazilian Amazon. To reverse this scenario, the implementation of sustainable agricultural practices and development of conservation policies to promote forest regrowth in degraded preserves are essential.

Quantifying forest fires remain a challenging task for the implementation of public policies aimed to mitigate climate change. In this paper, we propose a new method to provide an annual burned area map of Mato Grosso State located in the Brazilian Amazon region, taking advantage of the high spatial and temporal resolution sensors. The method consists of generating the vegetation, soil, and shade fraction images by applying the Linear Spectral Mixing Model (LSMM) to the Landsat-8 OLI (Operational Land Imager), PROBA-V (Project for On-Board Autonomy–Vegetation), and Suomi NPP-VIIRS (National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite) datasets. The shade fraction images highlight the burned areas, in which values are represented by low reflectance of ground targets, and the mapping was performed using an unsupervised classifier. Burned areas were evaluated in terms of land use and land cover classes over the Amazon, Cerrado and Pantanal biomes in the Mato Grosso State. Our results showed that most of the burned areas occurred in non-forested areas (66.57%) and old deforestation (21.54%). However, burned areas over forestlands (11.03%), causing forest degradation, reached more than double compared with burned areas identified in consolidated croplands (5.32%). The results obtained were validated using the Sentinel-2 data and compared with active fire data and existing global burned areas products, such as the MODIS (Moderate Resolution Imaging Spectroradiometer product) MCD64A1 and MCD45A1, and Fire CCI (ESA Climate Change Initiative) products. Although there is a good visual agreement among the analyzed products, the areas estimated were quite different. Our results presented correlation of 51% with Sentinel-2 and agreement of r² = 0.31, r² = 0.29, and r² = 0.43 with MCD64A1, MCD45A1, and Fire CCI products, respectively. However, considering the active fire data, it was achieved the better performance between active fire presence and burn mapping (92%). The proposed method provided a general perspective about the patterns of fire in various biomes of Mato Grosso State, Brazil, that are important for the environmental studies, specially related to fire severity, regeneration, and greenhouse gas emissions.

The Pantanal biome—a tropical wetland area—has been suffering a prolonged drought that started in 2019 and peaked in 2020. This favored the occurrence of natural disasters and led to the 2020 Pantanal fire crisis. The purpose of this work was to map the burned area’s extent during this crisis in the Brazilian portion of the Pantanal biome using Sentinel-2 MSI images. The classification of the burned areas was performed using a machine learning algorithm (Random Forest) in the Google Earth Engine platform. Input variables in the algorithm were the percentiles 10, 25, 50, 75, and 90 of monthly (July to December) mosaics of the shade fraction, NDVI, and NBR images derived from Sentinel-2 MSI images. The results showed an overall accuracy of 95.9% and an estimate of 44,998 km² burned in the Brazilian portion of the Pantanal, which resulted in severe ecosystem destruction and biodiversity loss in this biome. The burned area estimated in this work was higher than those estimated by the MCD64A1 (35,837 km²), Fire_cci (36,017 km²), GABAM (14,307 km²), and MapBiomas Fogo (23,372 km²) burned area products, which presented lower accuracies. These differences can be explained by the distinct datasets and methods used to obtain those estimates. The proposed approach based on Sentinel-2 images can potentially refine the burned area’s estimation at a regional scale and, consequently, improve the estimate of trace gases and aerosols associated with biomass burning, where global biomass burning inventories are widely known for having biases at a regional scale. Our study brings to light the necessity of developing approaches that aim to improve data and theory about the impacts of fire in regions critically sensitive to climate change, such as the Pantanal, in order to improve Earth systems models that forecast wetland–atmosphere interactions, and the role of these fires on current and future climate change over these regions.

Fire occurrence is a major disturbance in the Brazilian Cerrado, which is driven by both natural and anthropogenic activities. Despite increasing efforts for monitoring the Cerrado, a biome-scale study for quantifying and understanding the variability of fire emissions is still needed. We aimed at characterizing and finding trends in Particulate Matter with diameter less than 2.5 µm (PM_2.5) fire emissions in the Brazilian Cerrado using the PREP-CHEM-SRC emissions preprocessing tool and Moderate Resolution Imaging Spectroradiometer (MODIS) active fires datasets for the 2002–2017 period. Our results showed that, on average, the Cerrado emitted 1.08 Tg year⁻¹ of PM_2.5 associated with fires, accounting for 25% and 15% of the PM_2.5 fire emissions in Brazil and South America, respectively. Most of the PM_2.5 fire emissions were concentrated in the end of the dry season (August, 0.224 Tg month⁻¹ and September, 0.386 Tg month⁻¹) and in the transitional month (October, 0.210 Tg month⁻¹). Annually, 66% of the total emissions occurred over the savanna land cover; however, active fires that were detected in the evergreen broadleaf land cover tended to emit more than active fires occurring in the savanna land cover. Spatially, each 0.1° grid cell emitted, on average, 0.5 Mg km⁻² year⁻¹ of PM_2.5 associated with fires, but the values can reach to 16.6 Mg km⁻² year⁻¹ in a single cell. Higher estimates of PM_2.5 emissions associated with fires were mostly concentrated in the northern region, which is the current agricultural expansion frontier in this biome. When considering the entire Cerrado, we found an annual decreasing trend representing -1.78% of the annual average PM_2.5 emitted from fires during the period analyzed, however, the grid cell analysis found annual trends representing ± 35% of the annual average PM_2.5 fire emissions.

The Amazon basin, the world’s largest river basin, is a key global climate regulator. Due to the lack of an extensive network of gauging stations, this basin remains poorly monitored, hindering the management of its water resources. Due to the vast extension of the Amazon basin, hydrological modeling is the only viable approach to monitor its current status. Here, we used the Soil and Water Assessment Tool (SWAT), a process-based and time-continuous eco-hydrological model, to simulate streamflow and hydrologic water balance in an Amazonian watershed where only a few gauging stations (the Jari River Basin) are available. SWAT inputs consisted of reanalysis data based on orbital remote sensing. The calibration and validation of the SWAT model indicated a good agreement according to Nash-Sutcliffe (NS, 0.85 and 0.89), Standard Deviation Ratio (RSR, 0.39 and 0.33), and Percent Bias (PBIAS, −9.5 and −0.6) values. Overall, the model satisfactorily simulated water flow and balance characteristics, such as evapotranspiration, surface runoff, and groundwater. The SWAT model is suitable for tropical river basin management and scenario simulations of environmental changes.