Journal
REMOTE SENSING
Volume 8, Issue 1, Pages -Publisher
MDPI
DOI: 10.3390/rs8010007
Keywords
soil moisture; TerraSAR-X; Radarsat-2; SVR; Dubois model
Categories
Funding
- National Science Foundation of China [41271116]
- Natural Science Foundation [BK20130174]
- Ordinary University Graduate Student Scientific Research Innovation Projects of Jiangsu Province [KYLX_1393]
- Priority Academic Program Development of Jiangsu higher education institutions (PAPD)
- Special Fund for Public Projects of National Administration of Surveying, Mapping, and Geoinformation of China [201412016]
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The potential use of TerraSAR-X and Radarsat-2 data for soil moisture retrieval over bare agricultural areas was investigated using both empirical and semi-empirical approaches. For the empirical approach, the Support Vector Regression (SVR) model was used with two cases: (1) using only one C-band or X-band image; and (2) using a pair of C-band and X-band images jointly. For the semi-empirical approach, the modified Dubois model based on C-band and X-band SAR data was developed to estimate soil moisture content. The experiments were implemented over two bare agricultural areas, and in-situ measurements were carried out to assess the methods. The results showed that the TerraSAR-X and Radarsat-2 are suitable remote sensing tools for the estimation of surface soil moisture, with an accuracy of about 3 vol % (root mean square error, RMSE) over bare agricultural areas. Compared with the results obtained by Radarsat-2 data, TerraSAR-X data gives a slight improvement in estimating soil moisture. The accuracy of the soil moisture estimation was improved further when the two bands SAR data were used (RMSE of about 2.2 vol %) instead of only one. Moreover, the modified Dubois model showed comparable accuracy to the empirical model independent of the surface roughness.
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