NASA’s Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error <0.04 m3/m3). The validation approach also includes other (“sparse network”) in situ SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products’ performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the L-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the ESA Soil Moisture Ocean Salinity mission, although there are differences in some regions. The high-resolution (3-km) SM retrieval product, generated by combining Copernicus Sentinel-1 data with SMAP observations, performs within expectations. Currently, however, there is limited availability of 3-km CVS data to support extensive validation at this spatial scale. The most recent (version 5) SMAP Level 4 SM data assimilation product providing surface and root-zone SM with complete spatio-temporal coverage at 9-km resolution also meets performance requirements. The SMAP SM validation program will continue throughout the mission life; future plans include expanding it to forested and high-latitude regions.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is a scientific mission that collects high spatio-temporal resolution (~70 m, 1-5 days average revisit time) thermal images since its launch on 29 June 2018. As a predecessor of future missions, one of the main objectives of ECOSTRESS is to retrieve and understand the spatio-temporal variations in terrestrial evapotranspiration (ET) and its responses to soil water availability. In the European ECOSTRESS Hub (EEH), by taking advantage of land surface temperature retrievals, we generated ECOSTRESS ET products over Europe and Africa using three structurally contrasting models, namely Surface Energy Balance System (SEBS) and Two Source Energy Balance (TSEB) parametric models, as well as the non-parametric Surface Temperature Initiated Closure (STIC) model. A comprehensive evaluation of the EEH ET products was conducted with respect to flux measurements from 19 eddy covariance sites over 6 different biomes with diverse aridity levels. Results revealed comparable performances of STIC and SEBS (RMSE of ~70 W m-2). However, the relatively complex TSEB model produced a higher RMSE of ~90 W m-2. Comparison between STIC ET estimate and the operational ECOSTRESS ET product from NASA PT-JPL model showed a difference in RMSE between the two ET products around 50 W m-2. Substantial overestimation (>80 W m-2) was noted in PT-JPL ET estimates over shrublands and savannas presumably due to the weak constraint of LST in the model. Overall, the EEH is promising to serve as a support to the Land Surface Temperature Monitoring (LSTM) mission.

Land evapotranspiration (ET) and lake evaporation are important water budget components, representing the main processes of energy and water exchange between the earth and the atmosphere, and thus can influence the regional-scale hydrological cycles. Based on several long-term and comprehensive land-atmosphere interaction measurements over the Tibetan Plateau, the total amounts of land ET and lake evaporation are estimated by a combination of satellite products and meteorological data, the results show that: (1) the total ET amount has an average annual value of 1.238±0.058×103 km3. The trends of annual ET amount show high variable in spatial distributions, with an increasing trend in the east plateau and a decreasing trend in the west plateau. (2) As for the lake surface, lake ice phenology are clearly presented by MODIS 8-day snow cover products, and they show large spatial variability in the duration of ice-free season. The estimated Bowen ratio and evaporation amounts show acceptable accuracies, and display opposite spatial distributions, with the latter being higher in the southern part than in the northern part. On the TP, a lake with a higher elevation, a smaller area and a larger latitude mostly corresponds to a shorter ice-free season (a lower total net radiation), a larger Bowen ratio and finally a lower evaporation amount. The multi-year average evaporation amounts are listed, with the total water evaporated from lake surface being approximately 29.4±1.2 km3 year-1 for the studied 75 lakes and 51.7±2.1 km3 year-1 for all Plateau lakes included. (3) To further explore the land/lake-atmosphere interaction processes in detail over data-limited regions of the TP and supported by the “Third Pole Environment (TPE) program, 16 comprehensive observation and research stations have been constructed over all kinds of landscapes and in different regions of the TP in 2021. These data have provided significance for future research on plateau- and regional-scale water budget, hydrological cycle and water resources management.

High spatiotemporal resolution rainfall is needed in predicting flash floods, local climate impact studies and agriculture management. Rainfall estimation techniques like satellites and the commercial microwave links (MWL) rainfall estimation have independently made significant advancements in high spatiotemporal resolution rainfall estimation. However, their combination for rainfall estimation has received little attention, while it could benefit many applications in ungauged areas. This study investigated the usability of the random forest (RF) algorithm trained with MWL rainfall and Meteosat Second Generation (MSG) based cloud top properties for estimating high spatiotemporal resolution rainfall in the sparsely gauged Kenyan Rift Valley. Our approach retrieved cloud top properties for use as predictor variables from rain areas estimated from the MSG data and estimated path average rainfall intensities from the MWL to serve as the target variable. We trained and validated the RF algorithm using parameters derived through optimal parameter tuning. The RF rainfall intensity estimates were compared with gauge, MWL, Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) and European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Multisensor Precipitation Estimate (MPE) to evaluate its rainfall intensities from point and spatial perspectives. The results can be described as good, considering they were achieved in near real-time, pointing towards a promising rainfall estimation alternative based on the RF algorithm applied to MWL and MSG data. The applicative benefits of this technique could be huge, considering that many ungauged areas have a growing MWL network and MSG and, in the future, Meteosat Third Generation coverage.