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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #393622
Research Project: From Field to Watershed: Enhancing Water Quality and Management in Agroecosystems through Remote Sensing, Ground Measurements, and Integrative Modeling

Location: Hydrology and Remote Sensing Laboratory

Title: High-resolution soil moisture data reveal complex multi-scale spatial variability across the United States

Author
item VERGOPOLAN, N. - Princeton University
item SHEFFIELD, J. - University Of Southampton
item CHANEY, N. - Duke University
item PAN, M. - University Of California, San Diego
item BECK, H.E. - Princeton University
item FERGUSON, C.R - Albany State University
item TORRES-ROJAS, L. - Duke University
item EIGENBROD, F. - University Of Southampton
item Crow, Wade
item WOOD, E. - Princeton University

Submitted to: Geophysical Research Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/25/2022
Publication Date: 8/4/2022
Citation: Vergopolan, N., Sheffield, J., Chaney, N., Pan, M., Beck, H., Ferguson, C., Torres-Rojas, L., Eigenbrod, F., Crow, W.T., Wood, E. 2022. High-resolution soil moisture data reveal complex multi-scale spatial variability across the United States. Geophysical Research Letters. 49(15):e2022GL098586. https://doi.org/10.1029/2022GL098586.
DOI: https://doi.org/10.1029/2022GL098586

Interpretive Summary: Soil moisture is an important variable for a range of agricultural and water-resource applications including fertilizer application, crop yield monitoring and irrigation scheduling. However, efforts to monitor and utilize existing field-scale soil moisture data sets are inevitably complicated by the large amount of sub-field-scale variability typically observed in soil moisture fields. As a result, better tools are needed to understand the source of sub-field-scale soil moisture variability and how it can be linked with larger-scale local (i.e., county-level) and regional (i.e., state-level) variability. This paper presents a major step forward in these efforts by combining state-of-the-art modelling and remote sensing approaches to generate the first high-resolution (30-m) soil moisture dataset covering the entire contiguous United States. This dataset allows for the development of better models to describe the magnitude and dynamics of sub-field-scale soil moisture variability. Such models will, in turn, allow us to better monitor - and manage - the off-site flux of water, energy and nutrients at the field- and farm-scale.

Technical Abstract: Soil moisture (SM) spatiotemporal variability critically influences water resources, agriculture, and climate. However, besides site specific studies, little is known about how SM varies locally (1-100m scale). Consequently, quantifying the SM variability and its impact on the Earth system remains a long-standing challenge in hydrology. Here, we reveal the striking variability of local-scale SM across the United States using SMAP-HydroBlocks - a novel satellite-based surface SM dataset at 30-m resolution. Results show how the complex interplay of SM with landscape characteristics and hydroclimate is primarily driven by local variations in soil properties. This local-scale complexity yields a remarkable and unique multi-scale behavior at each location. However, very little of this complexity persists across spatial scales. Experiments reveal that up to 80% of the SM spatial information is lost at the 1-km resolution, with complete loss expected at the scale of current state-of-the-art monitoring and modeling systems (5-25km).