| Title | Soil Moisture Retrieval in Agricultural Fields Using Adaptive Model-Based Polarimetric Decomposition of SAR Data |
| Authors | He, Lian Panciera, Rocco Tanase, Mihai A. Walker, Jeffrey P. Qin, Qiming |
| Affiliation | Peking Univ, Inst Remote Sensing & GIS, Beijing 100871, Peoples R China. Monash Univ, Dept Civil Engn, Melbourne, Vic 3800, Australia. Univ Melbourne, Cooperat Res Ctr Spatial Informat, Melbourne, Vic 3053, Australia. |
| Keywords | Agricultural fields polarimetric decomposition synthetic aperture radar (SAR) polarimetry soil moisture L-BAND SAR SCATTERING MODEL SURFACE-ROUGHNESS MICROWAVE-SCATTERING RADAR OBSERVATIONS COHERENCY MATRIX WHEAT FIELDS TIME-SERIES VEGETATION BACKSCATTERING |
| Issue Date | 2016 |
| Publisher | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING |
| Citation | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING.2016,54(8),4445-4460. |
| Abstract | The aim of this paper was to estimate soil moisture in agricultural crop fields from fully polarimetric L-band synthetic aperture radar (SAR) data through the polarimetric decomposition of the SAR coherency matrix. A nonnegative-eigenvalue-decomposition scheme, together with an adaptive volumescattering model, is extended to an adaptive model-based decomposition (MBD) (Adaptive MBD) model for soil moisture retrieval. The Adaptive MBD can ensure nonnegative decomposed scattering components and allows two parameters (i.e., the mean orientation angle and a degree of randomness) to be determined to characterize the volume scattering. Its performance was tested using airborne SAR data and coincident ground measurements collected over agricultural fields in southeastern Australia and compared with previous MBD methods (i.e., the Freeman three-component decomposition using the extended Bragg model, the Yamaguchi three-component decomposition, and an iterative generalized hybrid decomposition). The results obtained with the newly proposed decomposition scheme agreed well with expectations based on observed plant structure and biomass levels. The new method was superior in tracking soil moisture dynamics with respect to previous decomposition methods in our study area, with root-mean-square error of soil moisture estimations being 0.10 and 0.14 m(3)/m(3), respectively, for surface and double-bounce components. However, large variability in the achieved soil moisture accuracy was observed, depending on the presence of row structures in the underlying soil surface. |
| URI | http://hdl.handle.net/20.500.11897/437075 |
| ISSN | 0196-2892 |
| DOI | 10.1109/TGRS.2016.2542214 |
| Indexed | SCI(E) EI |
| Appears in Collections: | 地球与空间科学学院 |
