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Estimation of accurate soil physical and hydraulic properties are of prime importance for the management of water resources in agriculture-dominant regions. This study introduces a simplified framework for estimating soil physical and hydraulic properties crucial for managing agricultural water resources. The developed framework optimizes soil properties for the Regional Hydrological Extremes Assessment System (RHEAS) to enhance the performance of its core hydrological model, Variable Infiltration Capacity (VIC). These soil properties were optimized using six years (2015–2021) of satellite soil moisture observations from NASA’s Soil Moisture Active Passive (SMAP) mission with a modified Shuffled Complex Evolution (SCE-UA) optimization algorithm. A total of three most sensitive soil properties that control model soil moisture simulations, such as Ksat (Saturated hydraulic conductivity), expt (exponent parameter in Campbell’s equation for hydraulic conductivity), and bd (Bulk density) were optimized for the Lower Mekong River (LMR) basin. To better assess the impact of optimized soil properties, streamflow simulation as well as agricultural drought severity assessment, were estimated using the RHEAS framework’s VIC Routing module and Soil Moisture Deficit Index (SMDI) module, respectively. The streamflow simulation involved four approaches: an initial open-loop setup, one optimized with SMAP soil moisture data (SMAP), another optimized with actual streamflow data (Runoff), and a final one combining the previous two datasets (SMAP_Runoff). Switching from the initial setup to the SMAP-optimized model increased the Nash-Sutcliffe Efficiency (NSE) by 56.4 % and upgrading from the streamflow-optimized to the combined data model raised the NSE by 21.9 %. This showcases the benefits of optimizing soil properties for more accurate simulations. Furthermore, the optimized model accurately represented the severity and extent of historical agricultural droughts, aligning with regional drought reports of LMR basin. This framework offers a valuable tool for hydrological modeling and drought management, particularly in data-scarce and agriculture-intensive regions, informing agricultural water resource management, irrigation decision-making, and food security initiatives within the LMR basin and beyond.

An analysis of a Level-2 (L2) soil moisture record extending from 1 May 2021 to 1 January 2024 derived from Spire, Inc.’s Global Navigation Satellite System Reflectometry (GNSS-R) observatories is presented. The product's sensitivity to large scale soil moisture variability is demonstrated using an example of a 2022 flood in Pakistan. Product consistency among the constellation's multiple satellites is also investigated; no clear evidence of intersatellite biases is observed. Further comparisons are performed with soil moisture datasets from the Soil Moisture Active Passive (SMAP) and Cyclone Global Navigation Satellite System (CYGNSS) missions, from the European Center for Medium-Range Weather Forecasts Reanalysis v5 (ERA5), and from in situ International Soil Moisture Network (ISMN) sites. Although an overall product correlation with SMAP soil moisture of approximately 85$\%$ is determined, per-pixel correlations vary significantly and per-pixel root-mean-square errors (RMSE) can range from 0.02 to 0.09 (cm$^{3}$/cm$^{3}$) depending on land class. The importance of applying the product's quality flags is also demonstrated. The influence of other calibration effects and inland water body contamination on these results is also discussed.

The NASA ISRO synthetic aperture radar (NISAR) mission scheduled for launch in 2024 will provide global L-band radar observations that can be applied to estimate land surface soil moisture. The mission's soil moisture product will be provided at 200-m resolution with a global revisit frequency of 12 days (or 6 days when considering both ascending and descending observations). A time-series ratio algorithm for soil moisture retrieval has been applied to NISAR simulated datasets from airborne UAVSAR measurements in the SMAPVEX12 field campaign. Soil moisture retrieval performance using the algorithm is encouraging, with a correlation coefficient between retrievals and in situ observations greater than 0.7 and an unbiased root-mean-squared Error (RMSE) of 0.05 ${{{\bm{m}}}^3}/{{{\bm{m}}}^3}$. The results suggest that the time-series ratio algorithm will provide soil moisture products that meet an accuracy goal of 0.06 ${{{\bm{m}}}^3}/{{{\bm{m}}}^3}$ unbiased RMSE.

To address the recent increase in wildfire severity and incidence, as well as the subsequent financial and physical costs, forest managers and wildland firefighting agencies rely on remotely sensed products for better decision-making and mitigation efforts. To address the remote sensing needs of these agencies, which include high spatial resolution, immunity to atmospheric and solar illumination effects, and day/night capabilities, the use of synthetic aperture radar (SAR) is under investigation for application in current and upcoming systems for all phases of a wildfire. Focusing on the active phase, a method for monitoring wildfire activity is presented based on changes in the radar vegetation index (RVI). L-band backscatter measurements from NASA/JPL’s UAVSAR instrument are used to obtain RVI images on multiple dates during the 2020 Bobcat (located in Southern CA, USA) and Hennessey (located in Northern CA, USA) fires and the 2021 Caldor (located in the Sierra Nevada region of CA, USA) fire. Changes in the RVI between measurement dates of a single fire are then compared to indicators of fire activity such as ancillary GIS-based burn extent perimeters and the Landsat 8-based difference normalized burn ratio (dNBR). An RVI-based wildfire “burn” detector/index is then developed by thresholding the RVI change. A combination of the receiver operating characteristic (ROC) curves and F1 scores for this detector are used to derive change detection thresholds at varying spatial resolutions. Six repeat-track UAVSAR lines over the 2020 fires are used to determine appropriate threshold values, and the performance is subsequently investigated for the 2021 Caldor fire. The results show good performance for the Bobcat and Hennessey fires at 100 m resolution, with optimum probability of detections of 67.89% and 71.98%, F1 scores of 0.6865 and 0.7309, and Matthews correlation coefficients of 0.5863 and 0.6207, respectively, with an overall increase in performance for all metrics as spatial resolution becomes coarser. The results for pixels identified as “burned” compare well with other fire indicators such as soil burn severity, known progression maps, and post-fire agency publications. Good performance is also observed for the Caldor fire where the percentage of pixels identified as burned within the known fire perimeters ranges from 37.87% at ~5 m resolution to 88.02% at 500 m resolution, with a general increase in performance as spatial resolution increases. All detections for Caldor show dense collections of burned pixels within the known perimeters, while pixels identified as burned that lie outside of the know perimeters have a sparse spatial distribution similar to noise that decreases as spatial resolution is degraded. The Caldor results also align well with other fire indicators such as soil burn severity and vegetation disturbance.