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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 #380923
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: Surface soil moisture retrievals under forest canopy for L-band SAR observations across a wide range of incidence angles by inverting a physical scattering model

Author
item KURUM, M. - Mississippi State University
item KIM, S. - Jet Propulsion Laboratory
item AKBAR, R. - University Of Southern California
item Cosh, Michael

Submitted to: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/30/2020
Publication Date: 12/31/2020
Citation: Kurum, M., Kim, S., Akbar, R., Cosh, M.H. 2020. Surface soil moisture retrievals under forest canopy for L-band SAR observations across a wide range of incidence angles by inverting a physical scattering model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 14:1741-1753. https://doi.org/10.1109/JSTARS.2020.3047883.
DOI: https://doi.org/10.1109/JSTARS.2020.3047883

Interpretive Summary: Surface soil moisture microwave remote sensing requires a mitigation of vegetation at the surface to provide a robust product. A model was tested using field measurements from Canada in 2012 as part of a larger field experiment which captured soil moisture and vegetation information across a variety of landscapes, including row crops, pastures, and forests. This study focused on retrieving soil moisture over the forested areas, where measurements were made. An inversion model was determined to be the most valuable method for estimating soil moisture versus other more traditional radiometric models. This result will help focus future model development for remote sensing algorithms over forested regions.

Technical Abstract: Surface soil moisture retrievals were performed by inverting physical scattering models for forests over 30 to 50 degree incidence angle range and 0.05 to 0.40 m^3/m^3 soil moisture range using L-band airborne SAR data during a 28-day period. The forward models implemented single-scattering of discrete elements of trees and were validated at site F2 within 1.5 dB rmse of observation for VV-pol, which was enabled by introducing the gaps between trees. The physical forward models were inverted using the time-series SAR data to retrieve soil moisture and soil surface roughness, which were validated with in-situ data at four sites F1, F2, F3, and F5. Retrievals using VV input over the full dynamic ranges of wetness are accurate to 0.044 m^3/m^3 un-biased rmse with correlations of 0.71 to 0.84, which is very encouraging for retrieval under forest canopy. The conditions of these results are: the vegetation water content varied from 7.3 to 25.6 kg/m^2 and the sensitivity of VV to soil moisture changes ranged from 0.64 to 2.27 dB/(0.1 m^3/m^3). When both HH and VV were used as inputs to retrieval, the performance did not improve because the benefit of the multi-channels was offset by the larger uncertainties in HH modeling. Due to the short duration of in-situ data, the efficacy of commonly-used relative change index retrieval is diminished. In comparison, the inversion of the scattering model is found to be an effective way to estimate forest soil moisture: it is capable of systematic correction of vegetation effect, and offers accurate retrieval of the dynamic ranges of soil moisture values in terms of un-biased rmse. The retrieval with the physical forward model provides a first step towards global application for the future NISAR satellite.