Soil moisture (SM) space and time variability critically influences freshwater resources, agriculture, ecosystem dynamics, climate and land-atmosphere interactions, and it can also trigger hazards such as droughts, floods, landslides, and aggravate wildfires. Here, we present the first continental assessment of how SM varies at the local scales using [SMAP-HydroBlocks](https://waterai.earth/publication/2021_vergopolan_smaphydroblocks). This study maps the SM spatial variability, characterizes the landscape drivers, and quantifies how this variability persists across larger spatial scales. Results revealed striking SM spatial variability across the United States. However, this SM variability does not persist at coarser spatial scales resulting in extensive information loss. This information loss implicates inaccuracies when predicting non-linear SM-dependent hydrological, ecological, and biogeochemical processes using coarse-scale models and satellite estimates.
We introduce SMAP-HydroBlocks (SMAP-HB), a high-resolution satellite-based surface soil moisture dataset at an unprecedented 30-m resolution (2015–2019) across the conterminous United States. SMAP-HB was produced by using a scalable cluster-based merging scheme that combines high-resolution land surface modeling, radiative transfer modeling, machine learning, SMAP satellite microwave data, and in-situ observations. We evaluated the resulting dataset over 1,192 observational sites. Its largest benefit of SMAP-HB is the high spatial detail and improved representation of the soil moisture spatial variability and spatial accuracy with respect to SMAP products.
Soil Moisture (SM) is a direct measure of agricultural drought. While there are several global SM indices, none of them directly use SM observations in a near-real-time capacity and as an operational tool. This paper presents a near-real-time global …
Satellite missions measuring soil moisture from space continue to improve the availability of soil moisture information. However, the utility of these satellite products is limited by the large footprint of the microwave sensors. This study presents a merging framework that combines a hyper-resolution land surface model (LSM), a radiative transfer model (RTM), and a Bayesian scheme to merge and downscale coarse resolution remotely sensed hydrological variables to a 30-m spatial resolution. The framework is based on [HydroBlocks](https://waterai.earth/hydroblocks/), an LSM that solves the field-scale spatial heterogeneity of land surface processes through interacting hydrologic response units (HRUs). Our approach was demonstrated for soil moisture by coupling HydroBlocks with the Tau-Omega RTM used in the Soil Moisture Active Passive (SMAP) mission. The brightness temperature from the HydroBlocks-RTM and SMAP L3 were merged to obtain updated 30-m resolution soil moisture estimates.