Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 225-230, 2013
https://doi.org/10.5194/isprsarchives-XL-1-W3-225-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
24 Sep 2013
Evaluation of Radar Backscattering Models IEM, OH, and Dubois using L and C-Bands SAR Data over different vegetation canopy covers and soil depths
S. Khabazan1, M. Motagh1, and M. Hosseini2 1University of Tehran, Remote Sensing Division, Surveying and Geomatics Engineering Department, College of Engineering, North Kargar, Tehran 1439957131 Iran
2Université de Sherbrooke, Centre d'applications et de recherches en télédétection (CARTEL), QC, Canada
Keywords: Soil moisture, Dubois model, Integral equation model (IEM), Oh model, Soil depth, NDVI, AIRSAR Abstract. Several algorithms have been proposed in the literature to invert radar measurements to estimate surface soil moisture. The objective of this paper is to compare the performance of the most common surface back scattering models including the theoretical integral equation model (IEM) of Fung et al. (1992), and the semi-empirical models of Oh et al. (1992, 1994, 2002 and2004) and Dubois et al. (1995). This analysis uses four AIRSAR data in L and C band together with in situ measurements (soil moisture and surface roughness) over bare soil and vegetation covers area and three different soil depths. The results show that Dubois model tend to over-estimate the radar response in both bands while IEM model and Oh model frequently over-estimate the radar response in L band but under-estimate them in C band. By evaluating of all models in different soil depths, the best results were obtained in 0–3 cm depths. For vegetation area poor correlation between models backscatter simulation and radar response was observed.
Conference paper (PDF, 822 KB)


Citation: Khabazan, S., Motagh, M., and Hosseini, M.: Evaluation of Radar Backscattering Models IEM, OH, and Dubois using L and C-Bands SAR Data over different vegetation canopy covers and soil depths, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 225-230, https://doi.org/10.5194/isprsarchives-XL-1-W3-225-2013, 2013.

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