Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #382604
Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Rootzone soil moisture comparisons: AirMOSS, SMERGE and SMAP

Author
item TOBIN, K. - Texas A&M International Unviersity
item Crow, Wade
item BENNETT, M. - Texas A&M International Unviersity

Submitted to: Geoscience and Remote Sensing Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/26/2021
Publication Date: 6/15/2021
Citation: Tobin, K., Crow, W.T., Bennett, M. 2021. Rootzone soil moisture comparisons: AirMOSS, SMERGE and SMAP. Geoscience and Remote Sensing Letters. 19:1-5. https://doi.org/10.1109/LGRS.2021.3085432.
DOI: https://doi.org/10.1109/LGRS.2021.3085432

Interpretive Summary: Estimates of soil water availability in the crop root zone are of value for a range of agricultural applications – including drought monitoring, irrigation scheduling and the optimization of fertilizer applications. Soil moisture can be retrieved from microwave remote sensing observations; however, such retrievals are typically restricted to only a relatively thin surface layer. Multiple solutions have been proposed for this shortcoming – including the use of very low-frequency P-band observations. This paper provides the first direct cross-comparison of these solutions. Overall, results suggest that relatively low-cost methods based on the vertical extrapolation of existing surface soil moisture observations using a land surface model are as effective as methods based on the remote deployment of P-band sensors. These results will be used by NASA to prioritize the development of new technologies for the remote estimation of soil moisture for agricultural applications.

Technical Abstract: A long-standing goal in the terrestrial remote sensing community has been the development of a root-zone soil moisture (RZSM) product based on microwave radar or radiometry observations. For example, the National Aeronautics and Space Administration’s Airborne Microwave Observatory of Subcanopy and Surface (AirMOSS) mission between 2012 to 2015 served as an airborne testbed for the development of RZSM estimates acquired from a 420-440 MHz (P-band) radar. Results from the AirMOSS mission suggest that P-band backscatter can be inverted to provide a reliable estimate of 0- to 40-cm RZSM. In contrast, existing spaceborne estimates of soil moisture are based on higher-frequency microwave observations and recover only surface (i.e., upper 2- to 5-cm) estimates of soil moisture. However, such superficial estimates can be extrapolated into RZSM estimates via the application of data assimilation (DA) and data fusion (DF) techniques. Therefore, comparison between “indirect” RZSM products based on DF (Soil MERGE; SMERGE) and DA (Soil Moisture Active Passive Level 4; SMAP L4) techniques versus “direct” AirMOSS RZSM retrievals is warranted. Here, such comparisons are made at a 0.125-degree spatial resolution and summarized using bias, root mean square error, and unbiased root mean standard deviation metrics sampled at seven AirMOSS sites within the contiguous United States. Overall, direct AirMOSS RZSM does not outperform indirect SMERGE and SMAP L4 RZSM estimates derived from DF and DA approaches applied to C- and L-band remote sensing data. These results provide insight into the viability of achieving enhanced RZSM accuracy via deployment of a spaceborne P-band radar.