Potential of X-Band TerraSAR-X and COSMO-SkyMed SAR Data for the Assessment of Physical Soil Parameters

Abstract

Abstract:The aim of this paper is to analyze the potential of X-band SAR measurements (COSMO-SkyMed and TerraSAR-X) made over bare soils for the estimation of soil moisture and surface geometry parameters at a semi-arid site in Tunisia (North Africa). Radar signals acquired with different configurations (HH and VV polarizations, incidence angles of 26° and 36°) are statistically compared with ground measurements (soil moisture and roughness parameters). The radar measurements are found to be highly sensitive to the various soil parameters of interest. A linear relationship is determined for the radar signals as a function of volumetric soil moisture, and a logarithmic correlation is observed between the radar signals and three surface roughness parameters: the root mean square height (Hrms), the parameter Zs = Hrms 2 /l (where l is the correlation length) and the parameter Zg = Hrms × (Hrms/l) α (where α is the power of the surface height correlation function). The highest dynamic sensitivity is observed for Zg at high incidence angles. Finally, the performance of different physical and semi-empirical backscattering models (IEM, Baghdadi-calibrated IEM and Dubois models) is compared with SAR measurements. The results provide an indication of the limits of validity of the IEM and Dubois models, for various radar configurations and roughness conditions. Considerable improvements in OPEN ACCESSRemote Sens. 2015, 7 748 the IEM model performance are observed using the Baghdadi-calibrated version of this model.

Semi-Empirical Calibration of the Integral Equation Model for Co-Polarized L-Band Backscattering

“…Given the robustness of the IEM semi-empirical calibration proposed in Baghdadi et al [10,14] for C-and X-bands SAR data as demonstrated in (e.g., [16,18,20]), the objective is to extend the same approach to L-band in order to improve the SAR backscatter prediction. The prediction error on the radar backscattering coefficients based on a 10-fold cross-validation was estimated for each polarization in order to validate the predictive performance of the calibrated version of the IEM (dataset was randomly divided into 90% training and 10% validation data elements and this procedure was repeated 10 times).…”

“…In our database, the roughness parameter rms has been estimated with an accuracy ranging between 5% and 10%, and the L-values with an accuracy ranging between 10% and 20% [20,21]. Low uncertainties on the roughness parameters measurements are obtained for high length and low sampling interval of the roughness profiles.…”

“…Most studies reported discrepancies between modeled backscatters by IEM and observed backscatters by SAR sensors at L-, C-, and X-bands [10][11][12][13][14][15][16][17][18][19][20]. These discrepancies could be related to the inaccuracy of the roughness measurements, which introduces significant errors into the modeled radar signal [21,22], and to the model itself [10][11][12][13][14][15].…”

Potential of Sentinel-1 Images for Estimating the Soil Roughness over Bare Agricultural Soils

“…The study site consists mainly of agricultural fields (cereals) on flat landscape. Soil texture measurements showed a clay percentage between 2.4% and 53.1% and sand percentage between 4.4% and 84.3% [29].…”

“…The Tunisian study site is situated in the Kairouan plain, in central Tunisia Figure 1). The climate in this region is semi-arid Mediterranean, with an average rainfall of approximately 300 mm/year, characterized by a rainy season lasting from October to May, with the two rainiest months being October and March [29]. The study site consists mainly of agricultural fields (cereals) on flat landscape.…”