Soil moisture plays a critical role in improving the weather and climate forecast and understanding terrestrial ecosystem processes. It is a key hydrologic variable in agricultural drought monitoring, flood forecasting, and irrigation management as well. Satellite retrievals can provide unprecedented soil moisture information at the global scale; however, the products are generally provided at coarse resolutions (25–50 km²). This often hampers their use in regional or local studies. The National Aeronautics and Space Administration Soil Moisture Active Passive (SMAP) satellite mission was launched in January 2015 aiming to acquire soil moisture and freeze‐thaw states over the globe with 2 to 3 days revisit frequency. This work presents a new framework based on an ensemble learning method while using atmospheric and geophysical information derived from remote‐sensing and ground‐based observations to downscale the level 3 daily composite version (L3_SM_P) of SMAP radiometer soil moisture over the Continental United States at 1‐km spatial resolution. In the proposed method, a suite of remotely sensed and in situ data sets are used, including soil texture and topography data among other information. The downscaled product was validated against in situ soil moisture measurements collected from two high density validation sites and 300 sparse soil moisture networks throughout the Continental United States. On average, the unbiased Root Mean Square Error between the downscaled SMAP soil moisture data and in‐situ soil moisture observations adequately met the SMAP soil moisture retrieval accuracy requirement of 0.04 m³/m³. In addition, other statistical measures, that is, Pearson correlation coefficient and bias, showed satisfactory results.

This study investigates an extreme weather event that impacted the United Arab Emirates (UAE) in March 2016, using the Weather Research and Forecasting (WRF) model version 3.7.1 coupled with its hydrological modeling extension package (WRF-Hydro). Six-hourly forecasted forcing records at 0.5^∘ spatial resolution, obtained from the National Center for Environmental Prediction (NCEP) Global Forecast System (GFS), are used to drive the three nested downscaling domains of both standalone WRF and coupled WRF–WRF-Hydro configurations for the recent flood-triggering storm. Ground and satellite observations over the UAE are employed to validate the model results. The model performance was assessed using precipitation from the Global Precipitation Measurement (GPM) mission (30 min, 0.1^∘ product), soil moisture from the Advanced Microwave Scanning Radiometer 2 (AMSR2; daily, 0.1^∘ product) and the NOAA Soil Moisture Operational Products System (SMOPS; 6-hourly, 0.25^∘ product), and cloud fraction retrievals from the Moderate Resolution Imaging Spectroradiometer Atmosphere product (MODATM; daily, 5 km product). The Pearson correlation coefficient (PCC), relative bias (rBIAS), and root-mean-square error (RMSE) are used as performance measures. Results show reductions of 24 % and 13 % in RMSE and rBIAS measures, respectively, in precipitation forecasts from the coupled WRF–WRF-Hydro model configuration, when compared to standalone WRF. The coupled system also shows improvements in global radiation forecasts, with reductions of 45 % and 12 % for RMSE and rBIAS, respectively. Moreover, WRF-Hydro was able to simulate the spatial distribution of soil moisture reasonably well across the study domain when compared to AMSR2-derived soil moisture estimates, despite a noticeable dry and wet bias in areas where soil moisture is high and low. Temporal and spatial variabilities of simulated soil moisture compare well to estimates from the NOAA SMOPS product, which indicates the model's capability to simulate surface drainage. Finally, the coupled model showed a shallower planetary boundary layer (PBL) compared to the standalone WRF simulation, which is attributed to the effect of soil moisture feedback. The demonstrated improvement, at the local scale, implies that WRF-Hydro coupling may enhance hydrological and meteorological forecasts in hyper-arid environments.

Accurate information on the global distribution and the three-dimensional (3D) structure of Earth’s forests is needed to assess forest biomass stocks and to project the future of the terrestrial Carbon sink. In spite of its importance, the 3D structure of forests continues to be the most crucial information gap in the observational archive. The Global Ecosystem Dynamics Investigation (GEDI) Light Detection and Ranging (LiDAR) sensor is providing an unprecedented near-global sampling of tropical and temperate forest structural properties. The integration of GEDI measurements with spatially-contiguous observations from polar orbiting optical satellite data therefore provides a unique opportunity to produce wall-to-wall maps of forests’ 3D structure. Here, we utilized Visible Infrared Imaging Radiometer Suite (VIIRS) annual metrics data to extrapolate GEDI-derived forest structure attributes into 1-km resolution contiguous maps of tree height (TH), canopy fraction cover (CFC), plant area index (PAI), and foliage height diversity (FHD) for the conterminous US (CONUS). The maps were validated using an independent subset of GEDI data. Validation results for TH (r² = 0.8; RMSE = 3.35 m), CFC (r² = 0.79; RMSE = 0.09), PAI (r² = 0.76; RMSE = 0.41), and FHD (r² = 0.83; RMSE = 0.25) demonstrated the robustness of VIIRS data for extrapolating GEDI measurements across the nation or even over larger areas. The methodology developed through this study may allow multi-decadal monitoring of changes in multiple forest structural attributes using consistent satellite observations acquired by orbiting and forthcoming VIIRS instruments.

Data assimilation is the application of Bayes' theorem to condition the states of a dynamical systems model on observations. Any real‐world application of Bayes' theorem is approximate, and therefore, we cannot expect that data assimilation will preserve all of the information available from models and observations. We outline a framework for measuring information in models, observations, and evaluation data in a way that allows us to quantify information loss during (necessarily imperfect) data assimilation. This facilitates quantitative analysis of trade‐offs between improving (usually expensive) remote sensing observing systems versus improving data assimilation design and implementation. We demonstrate this methodology on a previously published application of the ensemble Kalman filter used to assimilate remote sensing soil moisture retrievals from Advanced Microwave Scattering Radiometer for Earth (AMSR‐E) into the Noah land surface model.

Land surface temperature (LST) is an important input to the Atmosphere–Land Exchange Inverse (ALEXI) model to derive the Evaporative Stress Index (ESI) for drought monitoring. Currently, LST inputs to the ALEXI model come from the Geostationary Operational Environmental Satellite (GOES) and Moderate Resolution Imaging Spectroradiometer (MODIS) products, but clouds affect them. While passive microwave (e.g., AMSR-E and AMSR-2) sensors can penetrate non-rainy clouds and observe the Earth’s surface, but usually with a coarse spatial resolution, how to utilize multiple instruments’ advantages is an important methodology in remote sensing. In this study, we developed a new five-channel algorithm to derive LST from the microwave AMSR-E and AMSR-2 measurements and calibrate to the MODIS and GOES LST products. A machine learning method is implemented to further improve its performance. The MODIS and GOES LST products still show better performance than the AMSR-E and AMSR-2 LSTs when evaluated against the ground observations. Therefore, microwave LSTs are only used to fill the gaps due to clouds in the MODIS and GOES LST products. A gap filling method is further applied to fill the remaining gaps in the merged LSTs and downscale to the same spatial resolution as the MODIS and GOES products. With the daily integrated LST at the same spatial resolution as the MODIS and GOES products and available under nearly all sky conditions, the drought index, like the ESI, can be updated on daily basis. The initial implementation results demonstrate that the daily drought map can catch the fast changes of drought conditions and capture the signals of flash drought, and make flash drought monitoring become possible. It is expected that a drought map that is available on daily basis will benefit future drought monitoring.

Soil moisture (SM) data from Soil Moisture Operational Product System (SMOPS) have been available and used by users since 2013, and the latest version (3.0) has been operationally released since 2017. The version 3.0 provides a combination of currently all available daily global microwave SM retrievals including observations of ASCATA, ASCATB, SMAP, SMOS, and AMSR2 from 1 April 2015 to present. This study intercompares Noah model skills with benefits of assimilating the SMOPS blended (hereafter, SMOPS) and the five individual satellite SM retrievals. Results show that SMOPS SM product presents a significant advantage in data availability in comparison with the individual SM retrievals. Significant differences in data availability, climatology, and dynamic range of SM values between the bias‐corrected SMOPS and individual SM data lead to remarkable distinctions in Noah model SM simulations. Significant improvements of assimilating individual and blended satellite SM retrievals on model SM simulations versus the open loop in both surface and root zone soil layers are evident with reducing the Soil Climate Analysis Network measurements‐based root mean square errors and raising the correlations with respect to the enhanced vegetation index. Compared to the individual SM assimilations, model SM estimations with benefits of assimilating the SMOPS data provide the more remarkable improvements in surface soil layer.