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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 #419814
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: Soil moisture estimates using -band airborne SAR over forests replicating NISAR observations

Author
item KIM, S - National Aeronautics Space Administration (NASA) - Jet Propulsion Laboratory
item XU, X - Jet Propulsion Laboratory
item COLLIANDER, A - Jet Propulsion Laboratory
item Cosh, Michael
item Kraatz, Simon
item KELLY, V - Cary Institute Of Ecosystem Studies
item SIQUEIRA, P - University Of Massachusetts

Submitted to: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/15/2025
Publication Date: 3/17/2025
Citation: Kim, S., Xu, X., Colliander, A., Cosh, M.H., Kraatz, S.G., Kelly, V., Siqueira, P. 2025. Soil moisture estimates using -band airborne SAR over forests replicating NISAR observations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 18:7364-7373. https://doi.org/10.1109/JSTARS.2025.3544095.
DOI: https://doi.org/10.1109/JSTARS.2025.3544095

Interpretive Summary: L-band radar is a valuable high resolution remote sensing signal for estimating soil moisture across a variety of landscapes with low vegetation. To extend the value of these instruments, an experiment was conducted to determine the validity of this signal over forested landscapes in hope of producing a soil moisture estimate. This experiment was conducted in 2022 in the eastern United States and was accompanied by L-band radar on an aircraft to produce maps similar to what a satellite could produce. The results reveal the complexity of the surface will present challenges to interpretation for independent estimates of soil moisture and vegetation, which are confounding factors in the signal. This study will be of value to sensor developers.

Technical Abstract: Airborne SAR observations of soil moisture conditions are analyzed over deciduous and evergreen forest in the US Northeast during the 10-day spring and 14-day summer periods in 2022. During the summer, the dynamic range of HH is about 1 dB, deemed associated mostly with soil moisture changes. Larger changes in '''' is found between the two seasons, indicating the vegetation change, and also in spring when the attenuation by canopy is weak. In many sites, s0 correlates highly with in situ soil moisture, consistently between ascending and descending viewing geometry on flat terrain and, on slopes only when imaged at similar incidence angles. The consistency benefits NISAR’s retrieval by allowing more frequent consistent retrievals of soil moisture. The accuracy of soil moisture retrieval from 9 sites are 0.064 m3/m3 in unbiased rmse. The results are very encouraging as an independent test of the retrieval algorithm under the challenging conditions of surface slope or forest vegetation. Deficiencies in the retrieval algorithm is represented mostly as the bias error. In comparison, the temporal range of the retrieval is the most useful property for applications and matches well with in situ observations.