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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 #412705
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: Vegetation crosstalk present in official SMAP surface soil moisture retrievals

Author
item Crow, Wade
item FELDMAN, A - Nasa Goddard Institute For Space Studies

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/7/2024
Publication Date: 11/13/2024
Citation: Crow, W.T., Feldman, A. 2024. Vegetation crosstalk present in official SMAP surface soil moisture retrievals. Remote Sensing of Environment. 316. https://doi.org/10.1016/j.rse.2024.114466.
DOI: https://doi.org/10.1016/j.rse.2024.114466

Interpretive Summary: Observations of microwave terrestrial emission can be inverted to provide satellite-based estimates of both soil moisture and vegetation water content. Both estimates have a variety of applications - including an improved understanding of how agricultural drought impacts ecosystem and agricultural productivity. However, the separation of these two signals - one soil-based and one vegetation-based - is challenging and often performed using only approximate methods. This paper uses a novel approach to evaluate how accurately state-of-the-art soil moisture retrieval algorithms perform such partitioning. Results suggests that spurious vegetation signals remain in existing soil moisture products. Such “crosstalk” between soil- and vegetation-based signals limits the value of satellite soil moisture products for key agricultural and ecohydrological applications. As such, this paper provides important context for ongoing efforts to develop better methods for isolating soil- and vegetation-based signals obtained from satellite remote sensing.

Technical Abstract: Successful surface soil moisture (SM) retrieval at large spatial scales has been enabled by microwave satellite measurements of Earth’s upwelling brightness temperature (TB). Nevertheless, the accurate correction for the impact of vegetation signals on TB remains a challenge for SM retrieval algorithms. In practice, such correction is often performed in a simplified manner. The Single Channel Algorithm (SCA) uses climatological normalized vegetation difference index values as a proxy for vegetation optical depth (t), resulting in SM retrievals that do not account for interannual t variability. NASA Soil Moisture Active/Passive (SMAP) mission SM algorithms are all based, to varying degrees, on the SCA. Therefore, the possibility exists that SMAP SM retrievals contain spurious crosstalk associated with the neglect of inter-annual t variability. Here, we utilize an instrumental variable analysis and SM retrievals derived from the Multi-Temporal Dual Channel Algorithm (MTDCA) – that better account for inter-annual t variability – as a benchmark to examine SMAP Level 3 SM retrievals for the presence of such crosstalk. Results suggest the presence of spurious vegetation signals in monthly climatological SMAP SM anomalies. Critically, the SMAP Dual Channel Algorithm (DCA), which serves as the current SMAP baseline SM algorithm, reduces - but does not eliminate - these signals. While such SM-vegetation crosstalk does not impact the overall precision of SMAP SM retrievals, it does suggest the need for caution when applying SMAP SM retrievals to science applications aimed at understanding SM coupling with the terrestrial biosphere.