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.

The Mediterranean region is one of the climate hotspots where the climate change impacts are both pronounced and documented. The HyMeX (Hydrometeorological Mediterranean eXperiment) aims to improve our understanding of the water cycle from the meteorological to climate scales. However, monitoring the water cycle with Earth observations (EO) is still a challenge: EO products are multiple, and their utility is degraded by large uncertainties and incoherences among the products. Over the Mediterranean region, these difficulties are exacerbated by the coastal/mountainous regions and the small size of the hydrological basins. Therefore, merging/integration techniques have been developed to reduce these issues. We introduce here an improved methodology that closes not only the terrestrial but also the atmospheric and ocean budgets. The new scheme allows us to impose a spatial and temporal multi-scale budget closure constraint. A new approach is also proposed to downscale the results from the basin to pixel scales (at the resolution of 0.25^∘). The provided Mediterranean WC budget is, for the first time, based mostly on observations such as the GRACE water storage or the netflow at the Gibraltar Strait. The integrated dataset is in better agreement with in situ measurements, and we are now able to estimate the Bosporus Strait annual mean netflow.

Over the last 25 years, the Arctic sea ice has seen its extent decline dramatically. Passive microwave observations, with their ability to penetrate clouds and their independency to sunlight, have been used to provide sea ice concentration (SIC) measurements since the 1970s. The Copernicus Imaging Microwave Radiometer (CIMR) is a high priority candidate mission within the European Copernicus Expansion program, with a special focus on the observation of the polar regions. It will observe at 6.9 and 10.65 GHz with 15 km spatial resolution, and at 18.7 and 36.5 GHz with 5 km spatial resolution. SIC algorithms are based on empirical methods, using the difference in radiometric signatures between the ocean and sea ice. Up to now, the existing algorithms have been limited in the number of channels they use. In this study, we proposed a new SIC algorithm called Ice Concentration REtrieval from the Analysis of Microwaves (IceCREAM). It can accommodate a large range of channels, and it is based on the optimal estimation. Linear relationships between the satellite measurements and the SIC are derived from the Round Robin Data Package of the sea ice Climate Change Initiative. The 6 and 10 GHz channels are very sensitive to the sea ice presence, whereas the 18 and 36 GHz channels have a better spatial resolution. A data fusion method is proposed to combine these two estimations. Therefore, IceCREAM will provide SIC estimates with the good accuracy of the 6+10GHz combination, and the high spatial resolution of the 18+36GHz combination.

The total water storage change (TWSC) over land is a major component of the global water cycle, with a large influence on the climate variability, sea level budget and water resource availability for human life. Its first estimates at a large scale were made available with GRACE (Gravity Recovery and Climate Experiment) observations for the 2002–2016 period, followed since 2018 by the launch of the GRACE-FO (Follow-On) mission. In this paper, using an approach based on the water mass conservation rule, we propose to merge satellite-based observations of precipitation and evapotranspiration with in situ river discharge measurements to estimate TWSC over longer time periods (typically from 1980 to 2016), compatible with climate studies. We performed this task over five major Asian basins, subject to both large climate variability and strong anthropogenic pressure for water resources and for which long-term records of in situ discharge measurements are available. Our Satellite Water Cycle (SAWC) reconstruction provides TWSC estimates very coherent in terms of seasonal and interannual variations with independent sources of information such as (1) TWSC GRACE-derived observations (over the 2002–2015 period), (2) ISBA-CTRIP (Interactions between Soil, Biosphere and Atmosphere CNRM – Centre National de Recherches Météorologiques – Total Runoff Integrating Pathways) model simulations (1980–2015) and (3) the multi-satellite inundation extent (1993–2007). This analysis shows the advantages of the use of multiple satellite-derived datasets along with in situ data to perform a hydrologically coherent reconstruction of a missing water component estimate. It provides a new critical source of information for the long-term monitoring of TWSC and to better understand its critical role in the global and terrestrial water cycle.

The three infrared atmospheric sounding interferometers (IASIs), launched in 2006, 2012, and 2018, are key instruments to weather forecasting, and most meteorological centres assimilate IASI nadir radiance data into atmospheric models to feed their forecasts. The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) recently released a reprocessed homogeneous radiance record for the whole IASI observation period, from which 13 years (2008–2020) of temperature profiles can be obtained. In this work, atmospheric temperatures at different altitudes are retrieved from IASI radiances measured in the carbon dioxide absorption bands (654–800 and 2250–2400 cm⁻¹) by selecting the channels that are the most sensitive to the temperature at different altitudes. We rely on an artificial neural network (ANN) to retrieve atmospheric temperatures from a selected set of IASI radiances. We trained the ANN with IASI radiances as input and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5) as output. The retrieved temperatures were validated with ERA5, with in situ radiosonde temperatures from the Analysed RadioSoundings Archive (ARSA) network and with EUMETSAT temperatures retrieved from IASI radiances using a different method. Between 750 and 7 hPa, where IASI is most sensitive to temperature, a good agreement is observed between the three datasets: the differences between IASI on one hand and ERA5, ARSA, or EUMETSAT on the other hand are usually less than 0.5 K at these altitudes. At 2 hPa, as the IASI sensitivity decreases, we found differences up to 2 K between IASI and the three validation datasets. We then computed atmospheric temperature linear trends from atmospheric temperatures between 750 and 2 hPa. We found that in the past 13 years, there is a general warming trend of the troposphere that is more important at the poles and at mid-latitudes (0.5 K/decade at mid-latitudes, 1 K/decade at the North Pole). The stratosphere is globally cooling on average, except at the South Pole as a result of the ozone layer recovery and a sudden stratospheric warming in 2019. The cooling is most pronounced in the equatorial upper stratosphere (−1 K/decade). This work shows that ANN can be a powerful and simple tool to retrieve IASI temperatures at different altitudes in the upper troposphere and in the stratosphere, allowing us to construct a homogeneous and consistent temperature data record adapted to trend analysis.