Physical-based soil moisture retrieval method over bare agricultural areas by means of multi-sensor SAR data |
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| Authors: | Xiang Zhang Baozhang Chen Hui Zhao Tao Li Qianfu Chen |
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| Affiliation: | 1. Satellite Surveying and Mapping Application Center, National Administration of Surveying, Mapping and Geo-information, Beijing, China;2. School of environment science and spatial informatics, China University of Mining and Technology, Xuzhou, China;3. School of environment science and spatial informatics, China University of Mining and Technology, Xuzhou, China;4. Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing, China;5. National Geomatics Center of China, Beijing, China |
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| Abstract: | The main objective of this research is to develop, test and validate soil moisture retrieval method based on multi-source SAR (Synthetic Aperture Radar) data for bare agricultural areas. The Radardat-2, TerraSAR-X and Sentinel-1A SAR data were applied to retrieve soil moisture content in combination with the integral equation model (IEM) or calibrated integral equation model (CIEM). A straightforward inversion scheme was developed, which does not require the prior knowledge of surface roughness. The soil moisture content can be directly estimated using a look-up table (LUT) optimization method with multi-source SAR data as inputs. For validation purpose, in situ soil moisture content was measured during the period of SAR data acquisitions. The effectiveness and reliability of the soil moisture retrieval methods were evaluated based on the in situ measurements and cost function distribution graph. The experimental results indicate that the developed approach provided accurate soil moisture estimates with root mean square errors (RMSE) ranging from 0.047 cm3 cm?3 to 0.079 cm3 cm?3 over the experimental areas. The distribution graphs of the cost function demonstrate the uniqueness and convergence of the estimated results based on multi-source SAR data. Either IEM or CIEM was employed to estimate soil moisture content, more accurate results were obtained with Radarsat-2, TerraSAR-X and Sentinel-1A data as inputs. The experimental results preliminary illustrate that the multi-source SAR data are promising for soil moisture retrieval over bare agricultural areas. The novelty of the presented research can be summarized as two aspects. Firstly, the multi-sensor SAR with different incidence angle, different frequency and different polarization were combined to estimate soil moisture content by means of the physical-based methods. The combination of the multi-sensor SAR data can effectively solve the ill-posed problem of soil moisture retrieval using physical models. Secondly, the CIEM was utilized to establish the soil moisture retrieval model, which transforms the three unknown parameters to two unknown parameters. Furthermore, the convergence and uniqueness of the estimated soil moisture were validated through distribution graphs of the cost function. |
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