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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 #318113

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: Coherent model of L-band radar scattering by soybean plants: model development, validation and retrieval

item HUANG, H. - University Of Washington
item KIM, S. - Jet Propulsion Laboratory
item TSANG, L. - University Of Washington
item XU, X. - Jet Propulsion Laboratory
item LIAO, T. - University Of Washington
item Jackson, Thomas
item YUEH, S. - Jet Propulsion Laboratory

Submitted to: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/1/2015
Publication Date: 2/1/2016
Citation: Huang, H., Kim, S., Tsang, L., Xu, X., Liao, T., Jackson, T.J., Yueh, S. 2016. Coherent model of L-band radar scattering by soybean plants: model development, validation and retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 9(1):272-284.

Interpretive Summary: Soil moisture retrieval for soybean fields was carried out using inversion of a semi-empirical scattering model with radar observations. Soybean data collected in the Soil Moisture Active Passive Validation Experiment 2012 was used to develop an improved algorithm for this landcover. The new algorithm improved the match-ups with data at soybean sites observed in the field experiment. It also improved the soil moisture retrieval accuracy. Improving these and expanding the number of landcover classes will improve the quality and reliability of satellite products. The results contribute to the Soil Moisture Active Passive satellite and will result in in a more robust product for agricultural hydrology.

Technical Abstract: An improved coherent branching model for L-band radar remote sensing of soybean is proposed by taking into account the correlated scattering among scatterers. The novel feature of the analytic coherent model consists of conditional probability functions to eliminate the overlapping effects of branches in the former branching models. Backscattering coefficients are considered for a variety of scenarios over the full growth cycle for vegetation water content (VWC) and the complete drydown conditions for soil moisture. The results of the coherent model show that HH scattering has a significant difference up to 3 dB from that of the independent scattering when VWC is low, 0.2 kg/m2. Forward model calculations are performed for the scattering from the vegetation volume for the full range of three axes for RMS height of bare soil, VWC and soil moisture using the coherent model. The soybean volume scattering including the double-bounce term is combined with the forward scattering model of bare soil from the numerical Maxwell solution that incorporates RMS height, soil permittivity and correlation length to form the forward model lookup table for the vegetated soil. The results are compared and validated with data from 13 soybean field sites collected as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12). Time-series retrieval of soil moisture is also applied to the soybean fields by inverting the forward model lookup table. During the retrieval, the VWC was optimized with physical constraints obtained from ground measurements. The accuracy of the retrieval was significantly improved by using the proposed coherent model: the root mean squared error (RMSE) of the soil moisture retrieval is improved from 0.09 to 0.05 cm3/cm3 and the correlation coefficient is increased from 0.66 to 0.92.