As the largest river basin on Earth, the Amazon is of major importance to the world's climate and water resources. Over the past decades, advances in satellite‐based remote sensing (RS) have brought our understanding of its terrestrial water cycle and the associated hydrological processes to a new era. Here, we review major studies and the various techniques using satellite RS in the Amazon. We show how RS played a major role in supporting new research and key findings regarding the Amazon water cycle, and how the region became a laboratory for groundbreaking investigations of new satellite retrievals and analyses. At the basin‐scale, the understanding of several hydrological processes was only possible with the advent of RS observations, such as the characterization of "rainfall hotspots" in the Andes‐Amazon transition, evapotranspiration rates, and variations of surface waters and groundwater storage. These results strongly contribute to the recent advances of hydrological models and to our new understanding of the Amazon water budget and aquatic environments. In the context of upcoming hydrology‐oriented satellite missions, which will offer the opportunity for new synergies and new observations with finer space‐time resolution, this review aims to guide future research agenda toward integrated monitoring and understanding of the Amazon water from space. Integrated multidisciplinary studies, fostered by international collaborations, set up future directions to tackle the great challenges the Amazon is currently facing, from climate change to increased anthropogenic pressure.

Evapotranspiration (ET) is a key process linking surface and atmospheric energy budgets, yet its drivers and patterns across wetlandscapes are poorly understood worldwide. Here we assess the ET dynamics in 12 wetland complexes across South America, revealing major differences under temperate, tropical, and equatorial climates. While net radiation is a dominant driver of ET seasonality in most environments, flooding also contributes strongly to ET in tropical and equatorial wetlands, especially in meeting the evaporative demand. Moreover, significant water losses through wetlands and ET differences between wetlands and uplands occur in temperate, more water-limited environments and in highly flooded areas such as the Pantanal, where slow river flood propagation drives the ET dynamics. Finally, floodplain forests produce the greatest ET in all environments except the Amazon River floodplains, where upland forests sustain high rates year round. Our findings highlight the unique hydrological functioning and ecosystem services provided by wetlands on a continental scale.

Climate change is expected to increase the frequency and intensity of flooding, emphasizing the need to better understand these events. Satellite observations provide essential information, and the recent launch of SWOT offers new opportunities to investigate flood dynamics. Here, we use SWOT observations to detect significant hydrologic changes during an unprecedented 2024 flood in southern Brazil. In the Jacuí and Guaíba Rivers, water slopes increased dramatically–up to 11 times (from 0.82 to 9.59 cm/km) and 21 times (0.31–6.58 cm/km) compared to stable conditions. SWOT‐derived surface water elevations captured 99% of water level variability compared to in situ observations. Additionally, flooding in Patos Lagoon was intensified by NE–SW wind forcing, causing water levels to rise by up to 40 cm, worsening the flood event. SWOT proves a unique opportunity to understand extreme flooding events, providing new support for flood risk management in the context of climate change.

Evapotranspiration ( $ET$ ) provides a strong connection between surface energy and hydrological cycles. Advancements in remote sensing techniques have increased our understanding of energy and terrestrial water balances as well as the interaction between surface and atmosphere over large areas. In this study, we computed surface energy fluxes using the Surface Energy Balance Algorithm for Land (SEBAL) algorithm and a simplified adaptation of the CIMEC (Calibration using Inverse Modeling at Extreme Conditions) process for automated endmember selection. Our main purpose was to assess and compare the accuracy of the automated calibration of the SEBAL algorithm using two different sources of meteorological input data (ground measurements from an eddy covariance flux tower and reanalysis data from Modern-Era Reanalysis for Research and Applications version 2 (MERRA-2)) to estimate the dry season partitioning of surface energy and water fluxes in a transitional area between tropical rainforest and savanna. The area is located in Brazil and is subject to deforestation and cropland expansion. The SEBAL estimates were validated using eddy covariance measurements (2004 to 2006) from the Large-Scale Biosphere-Atmosphere Experiment in the Amazon (LBA) at the Bananal Javaés (JAV) site. Results indicated a high accuracy for daily ET, using both ground measurements and MERRA-2 reanalysis, suggesting a low sensitivity to meteorological inputs. For daily ET estimates, we found a root mean square error (RMSE) of 0.35 mm day⁻¹ for both observed and reanalysis meteorology using accurate quantiles for endmembers selection, yielding an error lower than 9% (RMSE compared to the average daily ET). Overall, the ET rates in forest areas were 4.2 mm day⁻¹, while in grassland/pasture and agricultural areas we found average rates between 2.0 and 3.2 mm day⁻¹, with significant changes in energy partitioning according to land cover. Thus, results are promising for the use of reanalysis data to estimate regional scale patterns of sensible heat (H) and latent heat (LE) fluxes, especially in areas subject to deforestation.

In 2023, an unprecedented drought and heat wave severely affected Amazon waters, leading to high mortality of fishes and river dolphins. Five of 10 lakes monitored had exceptionally high daytime water temperatures (over 37°C), with one large lake reaching up to 41°C in the entire approximately 2-meter-deep water column and up to 13°C of diel variation. Modeling showed that high solar radiation, reduced water depth and wind speed, and turbid waters were the main drivers of the high temperatures. This extreme heating of Amazon waters follows a long-term increase of 0.6°C/decade revealed by satellite estimates across the region’s lakes between 1990 and 2023. With ongoing climate change, temperatures that approach or exceed thermal tolerances for aquatic life are likely to become more common in tropical aquatic systems.

Suspended sediments (SSs) contribute to the maintenance of several ecosystems. However, intense soil erosion can lead to environmental, social, and economic impacts. South America (SA) has very high erosion and sediment transport rates. Here, we present a detailed description of the spatio‐temporal dynamics of natural SS flows in SA using the continental sediment model MGB‐SED AS. We evaluate the model with daily in situ data from 595 stations, information from regional studies and a global model. The model performance analysis showed that, in general, there was a better agreement between simulated and observed data than with the information found in regional studies and of the global model. The use of the hydrodynamic propagation method has allowed a better representation of sediment flows in rivers and floodplains. Based in the calibrated model results, SA delivers 1.00 × 10⁹ t/year of SS to the oceans, in which the Amazon (4.36 × 10⁸ t/year), Orinoco (1.37 × 10⁸ t/year), La Plata (1.11 × 10⁸ t/year), and Magdalena (3.26 × 10⁷) rivers are the main suppliers. The floodplains play an essential role, retaining about 12% (2.40 × 10⁷ t/year) of the SS loads reaching the rivers. In this study, data sets related to SS flows in SA were generated and can be used to support other large‐scale researches or policymakers and stakeholders for adequate management of continental land use.