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An Effective Model to Retrieve Soil Moisture from L- and C-Band SAR Data

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Abstract

This study investigated an appropriate method for soil moisture retrieval from radar images and coincident ground measurements acquired over bare soil and sparsely vegetated regions. The adopted approach based on a single scattering integral equation method (IEM) was developed to establish the relationship between backscatter coefficient and surface soil parameters including volumetric soil moisture content and surface roughness. The performance of IEM in 0–7.6 cm is better than that in 0–20 cm. Moreover, IEM can simulate correctly the backscatter coefficients only for the root mean square (RMS) height s < 1.5 cm at C-band and s < 2.5 cm at L-band by using an exponential correlation function and for s > 1.5 cm at C-band and s > 2.5 cm at L-band by using Gaussian function. However, due to the difficulties involved in the parameterization of soil surface roughness, the estimated accuracy is not satisfactory for the inversion of IEM. This paper used a combined roughness parameter and Fresnel reflection coefficient to develop an empirical model. Simulations were performed to support experimental results and to highlight soil moisture content and surface roughness effects in different polarizations. Results showed that a good agreement was found between the IEM simulations and the SAR measurements over a wide range of soil moisture and surface roughness characteristics. The model had a significant operational advantage in soil moisture retrieval. The correlation coefficients were 77.03 % at L-band and 81.45 % at C-band with the RMSEs of 0.515 and 0.4996 dB, respectively. Additionally, this work offered insight into the required application accuracy of soil moisture retrieval at a large area of arid regions.

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Acknowledgments

This work is supported by Beijing Project: Beijing Flash Flood Disaster Monitoring and Risk Assessment. The authors would like to thank every member who had made contribution to the manuscript. Liangliang Tao designed the study, developed the methodology, and wrote the manuscript. Jing Li formulated the original problem and provided direction and guidance. Liangliang Tao, Qingkong Cai, Xi Chen and Yunfei Zhang performed the experiment, collected the data, and performed the data analysis.

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Authors and Affiliations

  1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China

    Liangliang Tao, Jing Li, Xi Chen & Yunfei Zhang

  2. Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing, 100875, China

    Liangliang Tao, Jing Li, Xi Chen & Yunfei Zhang

  3. Institute of Civil Engineering, Henan Institute of Engineering, Zhengzhou, 451191, China

    Qingkong Cai

Authors
  1. Liangliang Tao
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  2. Jing Li
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  3. Xi Chen
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  4. Qingkong Cai
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  5. Yunfei Zhang
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Correspondence to Jing Li.

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Tao, L., Li, J., Chen, X. et al. An Effective Model to Retrieve Soil Moisture from L- and C-Band SAR Data. J Indian Soc Remote Sens 45, 621–629 (2017). https://doi.org/10.1007/s12524-016-0626-x

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  • DOI: https://doi.org/10.1007/s12524-016-0626-x

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