Although satellite-based soil moisture products are useful at regional and global scales, they do not meet the needs of users who require high resolution information for applications such as precision agriculture or catchment hydrologic modeling. The advent of uncrewed aerial systems (UAS) has opened new opportunities for bridging this need. We offer one of the very first validation studies on a dry-down event captured by a novel L-band radiometer onboard a Black Swift Technologies fixed-wing S2 UAS. A fallow cornfield at the Beltsville Agricultural Research Center (BARC) was selected as a study area to validate volumetric soil moisture estimates in mid-April. By leveraging a comparable radiative transfer model to that of SMAP and SMOS derived products, brightness temperature retrievals from this sensor are shown to successfully capture a week of dry-down post-storm event, as validated by HydraGO probe estimates (calibrated with gravimetric samples) and a nearby SCAN station. However, with a reported spatially averaged bias of -0.107 m3/m3 (compared to calibrated moisture values) and an ubRMSE of 0.028 m3/m3 potential concerns remain regarding the sensor calibration, vegetation and surface roughness corrections, and georeferencing. Nevertheless, given the potential of UAS for on-demand, high-resolution soil moisture retrievals, this collaborative effort provides critical feedback for informing future applications and improvements in the field of passive microwave remote sensing.

Droughts are projected to increase in intensity and frequency with the rise of global mean temperatures. However, not all drought indices equally capture the variety of influences that each hydrologic component has on the duration and magnitude of a period of water deficit. While such indices often agree with one another due to precipitation being the major input, heterogeneous responses caused by groundwater recharge, soil moisture memory, and vegetation dynamics may lead to a decoupling of identifiable drought conditions. As a semi-arid basin, the Limpopo River Basin (LRB) is a severely water-stressed region associated with unique climate patterns that regularly affect hydrological extremes. In this study, we find that vegetation indices show no significant long-term trends (S-statistic 9; p-value 0.779), opposing that of the modeled groundwater anomalies (S-statistic -57; p-value 0.05) in the growing season for a period of 18 years (2004–2022). Although the Mann-Kendall time series statistics for NDVI and drought indices are non-significant when basin-averaged, spatial heterogeneity further reveals that such a decoupling trend between vegetation and groundwater anomalies is indeed significant (p-value < 0.05) in colluvial, low-land aquifers to the southeast, while they remain more coupled in the central-west LRB, where more bedrock aquifers dominate. The conclusions of this study highlight the importance of ecological conditions with respect to water availability and suggest that water management must be informed by local vegetation species, especially in the face of depleting groundwater resources.

Mountain environments are at risk of water insecurity and ecosystemic disruption due to elevation dependent warming (EDW) and its impact on the cryosphere. Although past studies have investigated temperature and snow cover trends independently across the Andes, none have established their inter-relationship based on both remotely sensed and reanalysis datasets. This study leverages the available temperature and snow cover records from ERA5-Land (1950–2023) and a suite of remotely sensed data (primarily MODIS) from the past two decades (2003–2023) to highlight warming hotspots across the Cordillera and spatiotemporal relationships between temperature (air and surface) and snow cover by elevation band. Particular attention is given to the extratropical Southern Andes (25°S–38°S), whose snow-dominated mountain hydrology is uniquely vulnerable—confirmed by in-situ discharge gauges for sampled catchments in its eastern slopes. Results reveal the strong dependence of discharge on snow cover in the summertime, but more variability with respect to its relationship to year-round temperatures. Snow cover area appears to be especially sensitive to temperatures in the wintertime at higher elevations (3–5 km); however, the most significant trends in concurrent snow cover decrease and temperature increase emerge for the austral fall and spring (May and September – November). Trends with greater magnitudes for the modern period (2003–2022/3) compared to the historical climatology (1950–2000) demand greater attention with significant implications for future water management across the Andes.

Wildfire regimes in African savannas reflect interactions among surface heating, fuel availability, moisture constraints, and post-fire vegetation recovery. These processes are often analyzed jointly, making it difficult to distinguish near-term fire susceptibility from antecedent fuel–moisture controls and post-fire land-surface feedbacks. We examine these coupled processes in the Zambezi River Basin using monthly basin-aggregated satellite time series from January 2003 through November 2024 for burned area, land surface temperature (LST), precipitation, soil moisture, evapotranspiration (ET), and normalized difference vegetation index (NDVI). Random Forest regression, vector autoregression, impulse-response analysis, Granger predictive tests, and distributed lag regression are assigned non-overlapping inferential roles. Among the included predictors, surface soil moisture is the dominant contemporaneous land-surface signal in the Random Forest, while positive LST anomalies are associated with elevated burned-area anomalies at a one-month lag. Precipitation, soil moisture, NDVI, and ET predict burned area over seasonal lags through moisture-suppression and fuel-accumulation pathways. Burned-area history also improves prediction of subsequent NDVI and ET variability, indicating post-fire vegetation–water memory. The results support a three-component framework separating short-lag surface thermal state, antecedent hydrologic mediation, and post-fire vegetation–water response.