Global accurate evapotranspiration (ET) maps are crucial to monitor the water balance on the Earth’s surface. For this purpose, Walker et al. (2019) developed a methodology to estimate ET (ET_WV), considering that the soil properties and moisture (SM) are important limiting factors. In this work ET_WV was assessed in different land covers, land uses and soil properties using FLUXNET data and climate reanalysis datasets available in Google Earth Engine (GEE). GEE application programming interface (API), a powerful programming tool for non-experienced user, was used here to derived global ET_WV maps. ET_WV, computed with in situ FLUXNET data resulted in an overall ubRMSE of about 1.58 mm d⁻¹. The ET_WV estimates with GEE datasets yielded RMSE at least 56% smaller than those published by the available weather datasets. So, ET_WV methodology showed to be suitable for deriving global ET maps from different sources of data.

A procedure for soil moisture (SM) estimation over flat lands in the Argentinian Pampas region, using the water balance equation that considers SM to be the result of water inflows and outflows to the soil system, is presented. In recent years, remotely sensed data with Synthetic Aperture Radar (SAR) and radiometer sensors have been used to develop different methodologies to obtain SM maps. Thus, a variety of methodologies with different levels of complexity are available nowadays. These models require soil information such as soil physical properties and mineral composition, not readily available in Argentina and many other remote areas of the world. The procedure presented in this paper takes into account water input and output processes of the soil system and represents them with different hydro-environmental variables and SAR data. The water balance equation was solved with Multiple Linear Regression (MLR), Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Network (ANN) statistical models, fed with readily available data over Comisión Nacional de Actividades Espaciales (CONAE) core site located in Cordoba province, Argentina. The resulting models were obtained with precipitation (PP), air temperature ( $T_{\mathrm{a}}$) and relative humidity (RH) observations and with SAR data from the Sentinel-1A satellite mission. The accuracy of the model estimates represents 10 $\mathrm{\%}$ of the observed measured values of SM and is in line with state of the art algorithms. Results suggest that any model can be used with similar precision, since they show similar errors, although the MLR method allows analyzing and quantifying the errors introduced by the variables.

Water surface elevation (WSE) estimation is crucial for understanding hydrological processes and managing water resources. Remote sensing observations play a vital role in consistently estimating WSE over large areas. In this context, the new Surface Water and Ocean Topography (SWOT) mission equipped with Synthetic Aperture Radar (SAR) technology, offers the opportunity to monitor WSE and other crucial parameters with high spatial resolutions. The Paraná River stands out because it is the second-longest river system in South America. Hence, the performance of the SWOT product was evaluated using in situ WSE observations in this area. Specifically, SWOT estimates were compared with WSE measurements registered by four gage stations (Paraná, Colastiné, Leyes, and Santa Fe) for 01/2024-04/2024. The results show that SWOT estimates agree well with observed WSE data, with uncertainties lower than 6% and a determination coefficient (R2) greater than 0.96. Moreover, the SWOT product accurately reproduces the temporal variation as in situ WSE measurements over the period and selected stations. These initial results are very promising, highlighting the potential of this new mission to accurately monitor WSE with high spatial resolution.