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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 #338693
Title: Combined radar-radiometer surface soil moisture and roughness estimation

Author
item AKBAR, R. - University Of Southern California
item Cosh, Michael
item ONEILL, P.E. - Goddard Space Flight Center
item ENTEKHABI, D. - Broad Institute Of Mit/harvard
item MOGHADDAM, M. - University Of Michigan

Submitted to: IEEE Transactions on Geoscience and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/1/2017
Publication Date: 7/1/2017
Citation: Akbar, R., Cosh, M.H., O'Neill, P., Entekhabi, D., Moghaddam, M. 2017. Combined radar-radiometer surface soil moisture and roughness estimation. IEEE Transactions on Geoscience and Remote Sensing. 55(7):4098-4110. https://doi.org/10.1109/TGRS.2017.2688403.
DOI: https://doi.org/10.1109/TGRS.2017.2688403

Interpretive Summary: Soil moisture remote sensing in the L-band has been limited by the resolution of the passive microwave instrumentation. With the integration of an active radar data product, the resolution could be increased to a management scale which is more useful to agriculture. An experiment was established in Beltsville, MD to provide a robust data set for the development of an algorithm for merging these two products. The results of this study can be applied to current and future remote sensing missions which will combine both active and passive radiometry for an improved resolution soil moisture product.

Technical Abstract: A robust physics-based combined radar-radiometer, or Active-Passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in combined radar-radiometer estimation surface roughness can be considered a free parameter. Extensive Monte-Carlo numerical simulations and assessment using field data have been performed to both evaluate the algorithm’s performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors (RMSE) range from 0.18 to 0.03 cm3/cm3 for two different land cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented.