SMAP-HydroBlocks: Hyper-resolution satellite-based surface soil moisture over the continental United States
SMAP-HydroBlocks is a hyper-resolution satellite-based surface soil moisture product at 3-hourly 30-m resolution over the continental United States (2015-2019). This dataset combines microwave satellite remote sensing, hyper-resolution land surface model, radiative transfer modeling, machine learning, and in-situ observations to obtain hydrologically consistent soil moisture estimates of the top 5-cm of the soil.
Our approach is built upon HydroBlocks, a hyper-resolution land surface model that leverages the repeating spatial patterns over the landscape by implementing a hierarchical clustering algorithm to define its mesh (Chaney et al., 2020). HydroBlocks cluster the fine-scale drivers of the landscape spatial heterogeneity (e.g., 30-m land cover, soil properties, topography data) into complex tiles/clusters of similar hydrologic behavior. In this way, by simulating hydrological processes with clusters instead of regular grids, HydroBlocks yields an effective 30-m spatial resolution while leveraging the complex physics of land surface models and reducing the system's dimensionality and computational requirements.
To develop the SMAP-HydroBlocks dataset, we coupled the HydroBlocks model with a Tau-Omega Radiative Transfer Model (HydroBlocks-RTM) to simulate the soil surface brightness temperature, and we merged it with the NASA's Soil Moisture Active-Passive (SMAP) L3 Enhanced 9-km brightness temperature product (SMAP L3E). For merging cluster-based model and grid-based satellite data we developed a cluster-based spatial Bayesian scheme (Vergopolan et al., 2020). We parameterized this merging scheme by regionalizing relationships extracted from satellite, models, and in-situ soil moisture observations using machine learning (Vergopolan et al., accepted). With the fused brightness temperature, the inverse HydroBlocks-RTM model was applied to retrieve the SMAP-HydroBlocks (SMAP-HB) soil moisture estimates.
More details and updates available at: https://waterai.earth/smaphb/. For illustration, SMAP-HB long-term and annual climatology at 30-m resolution is shown in the Soil Moisture Visualization tab. Data is best shown in chrome and firefox browsers; otherwise, spatial resolution may be degraded.
- Data over the Continental United States is available for download at https://zenodo.org/record/5206725
- Subsets of the SMAP-HB dataset at 30-m 3-hr resolution (2015–2019) can also be made available on request from Noemi Vergopolan (email@example.com).
Please cite the following paper when using the dataset in any publication:
- Vergopolan, N., Chaney, N. W., Beck, H. E., Pan, M., Sheffield, J., Chan, S., & Wood, E. F. (2020). Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates. Remote Sensing of Environment, 242, 111740. https://doi.org/10.1016/j.rse.2020.111740