The Amazon underwent a severe austral springtime drought attributed to the onset of El Niño in 2023 and the warmer North Atlantic, Indian, and North Pacific Oceans. The Amazon rivers, lakes, small streams, wetlands, and reservoirs quickly lowered their water level below historical records due to decreased rainfall and warmth in the region. Based on satellite imagery, this study presents the first estimate of the water loss extent in the 4.2 million km² of the Brazilian Amazon biome (∼62% of the Amazon total biome) in 2023. We estimated the loss of 3.3 million hectares of surface water relative to 2022, with an overall accuracy of 92%. The surface water losses were concentrated in the states of Amazonas (59.4%) and Pará (25.5), adding up to 2.8 million hectares. The warmer and drier climate in the region affected the main rivers in the Amazon. Among them, the Solimões, Negro, Purus, Acre, and Branco suffered extreme drops in their levels in some regions, resulting in a high negative impact on the aquatic biodiversity, yet estimated only in local areas. A total of 1.14 million hectares of surface water loss (i.e. 35%) was detected within protected areas territories, affecting extractivist, indegenous, African-Brazilian, and fishing and traditional communities. Proximity analysis revealed that 75% of the 2023 surface water loss was within 25 km of small towns, 48% and 65.8% at 50 km from indigenous villages and urban areas, respectively. Our results reinforce people’s vulnerability to climate change, anticipating a plausible adverse impact scenario in the Amazon region and urging solutions to adaptation and mitigation. Therefore, an integrated monitoring system based on climate and water dynamics from satellite and ground stations is necessary to improve understanding of the problem for timing response of climatic change negative impacts.

This study evaluated the adequacy of the One-Source Energy Balance (OSEB) and Surface Energy Balance Algorithm for Land (SEBAL) models to estimate evapotranspiration in grain growing areas with humid subtropical climate in Rio Grande do Sul, southern Brazil. The dataset was obtained from a micrometeorological station (Eddy Covariance) and MODIS (Moderate Resolution Imaging Spectroradiometer) products during 84 observation days between 2009 and 2011. The OSEB and SEBAL models were used to estimate the partition of net radiation (Rn) into the components latent heat flux (LE), sensible heat flux (H), and ground heat flux (G) estimated from the MODIS images while the experimental data measured in situ were used to compare the results. Analyses indicated that the Energy Balance (EB) components were estimated from the MODIS images satisfactorily despite the spatial resolution limitations. Furthermore, the SEBAL model estimated the EB components satisfactorily only for summer crops. There are uncertainties associated with determining the hot and cold pixels due to the region humid subtropical climate and the spatial resolution of the sensor used in the other periods. The OSEB model has the lowest errors and the most adequate partitioning of the EB components throughout the year, and therefore, it is the most suitable for the climatic conditions of Rio Grande do Sul.

The Amazon wetlands are the largest and most diverse freshwater ecosystem globally, characterized by various flooded vegetation and the Amazon River’s estuary. This critical ecosystem is vulnerable to land use changes, dam construction, mining, and climate change. While several studies have utilized remote sensing to map wetlands in this region, significant uncertainty remains, which limits the assessment of impacts and the conservation priorities for Amazon wetlands. This study aims to enhance wetland mapping by integrating existing maps, remote sensing data, expert knowledge, and cloud computing via Earth Engine. We developed a harmonized regional wetland classification system adaptable to individual countries, enabling us to train and build a random forest model to classify wetlands using a robust remote sensing dataset. In 2020, wetlands spanned 151.7 million hectares (Mha) or 22.0% of the study area, plus an additional 7.4 Mha in deforested zones. The four dominant wetland classes accounted for 98.5% of the total area: Forest Floodplain (89.0 Mha; 58.6%), Lowland Herbaceous Floodplain (29.6 Mha; 19.6%), Shrub Floodplain (16.7 Mha; 11.0%), and Open Water (14.1 Mha; 9.3%). The overall mapping accuracy was 82.2%. Of the total wetlands in 2020, 52.6% (i.e., 79.8 Mha) were protected in Indigenous Territories, Conservation Units, and Ramsar Sites. Threats to the mapped wetlands included 7.4 Mha of loss due to fires and deforestation, with an additional 800,000 ha lost from 2021 to 2024 due to agriculture, urban expansion, and gold mining. Notably, 21 Mha of wetlands were directly affected by both reduced precipitation and surface water in 2020. Our mapping efforts will help identify priorities for wetland protection and support informed decision-making by local governments and ancestral communities to implement conservation and management plans. As 47.4% of the mapped wetlands are unprotected and have some level of threats and pressure, there are also opportunities to expand protected areas and implement effective management and conservation practices.

Evapotranspiration is an important phenomenon to agriculture; therefore, this work aims at verifying the suitability of the SEBAL and METRIC models to estimate latent heat flux using remote sensing data from grain cultivation areas in the northwestern subtropical region of Rio Grande do Sul. This region stands out for grain production. The analyzed data set consisted of 84 dates distributed over a 3-year period of areas planted with soy, corn, oats, wheat, and vetch crops. The data estimated from the remote images were compared with the reference measurements acquired in a micrometeorological station using the Eddy Covariance technique. Both models presented satisfactory results. However, the LE estimated by the METRIC model had the lowest error for all 3 types of soil cover analyzed. The best performance of the METRIC model is attributed to the fact that it does not require extreme water condition, i.e. for LE equal to zero, to determine the hot pixel when estimating the sensible heat flux, unlike the SEBAL model.

O objetivo deste trabalho foi verificar o desempenho do índice TVDI (Temperature- Vegetation Dryness Index), obtido a partir de sensores espectrais de superfície, e compará-lo a dados de déficit hídrico determinado pelo balanço hídrico meteorológico, em lavoura de soja no noroeste do Estado do Rio Grande do Sul. O princípio de funcionamento do TVDI está ancorado na inclinação da reta de regressão linear entre o índice de vegetação e a temperatura de superfície, que representam o grau de deficiência da umidade do sistema solo/água/planta. Para o estudo foram utilizados sensores de superfície de índice de vegetação, no caso o NDVI (Normalized Difference Vegetation Index) e de temperatura radiométrica da superfície, conectados a dataloggers, registrando medidas a cada 15 minutos. O TVDI foi comparado frente a dados de déficit e excesso hídrico obtidos por balanço hídrico meteorológico diário. No período de maior déficit o TVDI apresentou os maiores valores, indicando a restrição hídrica, coerente com o armazenamento de água no solo. As temperaturas de superfície também foram altas neste período. O TVDI estimado a partir de sensores de superfície tem sensibilidade em representar a disponibilidade hídrica da cultura e permite acompanhar o desenvolvimento da soja durante a safra.