Abrupt onset and swift intensification characterize flash droughts. Global surface soil moisture (θ_RS) from NASA's Soil Moisture Active Passive (SMAP) satellite can facilitate a near‐real‐time assessment of emerging flash droughts at a 36‐km footprint. However, a robust flash drought monitoring using θ_RS must account for the (a) short observation record of SMAP, (b) nonlinear geophysical controls over θ_RS dynamics, and (c) emergent meteorological drivers of flash droughts. We propose a new method for near‐real‐time characterization of droughts using Soil Moisture Stress (SMS, drought stress) and Relative Rate of Drydown (RRD, drought stress intensification rate)—developed using SMAP θ_RS (March 2015–May 2021), footprint‐scale seasonal soil water retention parameters and land‐atmospheric coupling strength. SMS and RRD are nonlinearly combined to develop Flash Drought Stress Index (FDSI) to characterize emerging flash droughts (FDSI ≥ 0.71 for moderate to high RRD and SMS). Globally, FDSI shows a high correlation with concurrent meteorological anomalies. A mechanistic evaluation of flash droughts is presented for the Northern Great Plains, Central South Africa, and Eastern Australia using FDSI, SMS, and RRD. About 5.6% of the earth's landmass experienced flash droughts of varying intensity and duration during 2015–2021 (FDSI ≥ 0.71 for >30 consecutive days), majorly in global drylands. FDSI shows high skill in forecasting vegetation health with a lead of 0–2 weeks, with exceptions in irrigated croplands and mixed forests. With readily available parameters, low data latency, and no dependence on model simulations, we provide a robust tool for global near‐real‐time flash drought monitoring using SMAP.

Key Points: Soil moisture physical controls evolve with spatial scale and wetness condition Soil was found the most dominant physical control for most wetness condition Entropy method could be a good tool to soil moisture numerical analysis

In this new era of remote‐sensing based hydrology, a major unanswered question is how to incorporate the impact of land‐surface based heterogeneity on soil moisture dynamics at remote sensing scales. The answer to this question is complicated since (1) soil moisture dynamics that vary with support, extent, and spacing scales are dependent on land‐surface based heterogeneity and (2) land‐surface based heterogeneity itself is scale‐specific and varies with hydroclimates. Land‐surface factors such as soil, vegetation, and topography affect soil moisture dynamics by redistributing the available soil moisture on the ground. In this study, we determined the contribution of these biophysical factors to redistribution of near‐surface soil moisture across a range of remote sensing scales varying from an (airborne) remote sensor footprint (1.6 km) to a (satellite) footprint scale (25.6 km). Two‐dimensional nondecimated wavelet transform was used to extract the support scale information from the spatial signals of the land‐surface and soil moisture variables. The study was conducted in three hydroclimates: humid (Iowa), subhumid (Oklahoma), and semiarid (Arizona). The dominance of soil on soil moisture dynamics typically decreased from airborne to satellite footprint scales whereas the influence of topography and vegetation increased with increasing support scale for all three hydroclimates. The distinct effect of hydroclimate was identifiable in the soil attributes dominating the soil moisture dynamics. The near‐surface soil moisture dynamics in Arizona (semiarid) can be attributed more to the clay content which is an effective limiting parameter for evaporation whereas in Oklahoma (humid), sand content (limiting parameter for drainage) was the dominant soil attribute. The findings from this study can provide a deeper understanding of the impact of heterogeneity on soil moisture dynamics and the potential improvement of hydrological models operating at footprints' scales.

Understanding the global soil moisture (SM) dynamics and its governing controls beyond the Darcy Scale is critical for various hydrological, meteorological, agricultural, and environmental applications. In this study, we parameterize the pathways of global surface SM (θ_RS) drydowns using seasonal observation from Soil Moisture Active Passive (SMAP) satellite (between 2015 and 2019) at 36 km resolution. We develop a new data‐driven nonparametric approach to identify the canonical forms of θ_RS drydown, followed by a nonlinear least squares parameterization of the seasonal drydown pathways at each SMAP footprint. The derived parameters provide the effective soil water retention parameters (SWRP_eff), land‐atmospheric coupling strength and soil hydrologic regimes for SMAP footprint. Depending on the footprint heterogeneity, climate, and season, the characteristics curves comprising different drydown phases are discovered at SMAP footprints. Drydown curves respond to the within‐footprint changes in the meteorological drivers, land‐surface characteristics, and the soil‐vegetative and atmospheric dynamics. Drydown parameters display high interseasonal variability, especially in the grasslands, croplands, and savannah landscapes due to significant changes in the landscape characteristics and moisture patterns at the subgrid‐scale. Soil texture exerts influence on the soil water retention and drydown parameters only when the footprint mean θ_RS is low, specifically in arid and sparsely vegetated regions. The interseasonal variability of the SWRP_eff is primarily driven by the landuse and climate of the SMAP footprint. A global understanding of the SM drydown features at SMAP footprint provides a significant step toward a scale‐specific, effective soil hydrologic parameterization for various applications.