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Research Project: Understanding Water-Driven Ecohydrologic and Erosion Processes in the Semiarid Southwest to Improve Watershed Management

Location: Southwest Watershed Research Center

Title: Application of a change detection soil moisture retrieval algorithm to combined, semiconcurrent radiometer, and radar observations

Author
item OUELLETTE, J.D. - Naval Research Laboratory
item HIMANI, T. - Naval Research Laboratory
item LI, L. - Naval Research Laboratory
item COLLIANDER, S. - Jet Propulsion Laboratory
item TWAROG, E.M. - Naval Research Laboratory
item Goodrich, David - Dave
item Holifield Collins, Chandra
item Cosh, Michael
item WALKER, J.P. - Monash University

Submitted to: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/18/2022
Publication Date: 11/3/2022
Citation: Ouellette, J., Himani, T., Li, L., Colliander, S., Twarog, E., Goodrich, D.C., Holifield Collins, C.D., Cosh, M.H., Walker, J. 2022. Application of a change detection soil moisture retrieval algorithm to combined, semiconcurrent radiometer, and radar observations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 15:9716-9721. https://doi.org/10.1109/JSTARS.2022.3219259.
DOI: https://doi.org/10.1109/JSTARS.2022.3219259

Interpretive Summary: The state of soil moisture in a watershed or a field can affect everything from crop yields to flooding. Soil moisture is difficult to measure over large areas. The importance of soil moisture was a key factor in NASA developing a satellite to measure it. The quality of the remotely sensed satellite soil moisture is dependent on developing routines to transform what is sensed by the satellite into soil moisture. Ground validation of the remotely sensed soil measurements is critical to understand the quality and error characteristis of the soil moisture estimates. In this study, a new method to analyze the error characteristics of an existing soil moisture algorithm was developed and tested over the four ground validation networks in Yanco, New South Wales, Australia; Kenaston, Manitoba, Canada; Fredricksburg, Texas, USA; and in the USDA-Agricultural Research Service’s Walnut Gulch Experimental Watershed, Arizona, USA. The error performance of the new approach described in this study is comparable to the original satellite baseline algorithm. However, the methodology described in this study achieved reasonable results without the aid of ancillary information or the need for training data.

Technical Abstract: This paper extends the application of an existing change-detection-based, time-series soil moisture retrieval algorithm to non-concurrent active and passive measurements from the WindSat and Soil Moisture Active Passive sensor suites. A time-series of L-band radar backscatter observations was used to populate an under-determined matrix equation whose optimal solution is derived via a bounded linear least squares estimator, and whose bounds were derived from a time-series of radiometer brightness temperature observations. Surface soil moisture estimates are compared with in-situ measurement probes, which were treated as ground truth. Error statistics and time-series results for the validation sites are presented and conclusions derived therefrom. The overall RMSE and un-biased RMSE for the retrieval algorithm, taken across all reference pixels considered in the study, were 0.070 m3/m3 and 0.055 m3/m3 respectively.