ABSTRACT Various models have been proposed to estimate the degree of backscatter in Synthetic Aperture Radar (SAR) images. However, it is still necessary to calibrate these models based on the characteristics of different study areas and to propose new models to achieve the highest possible accuracy in estimating the backscattering coefficient ( ) SAR. In this study, three empirical models, including Champion, Sahebi and Zribi/Dechambre, were initially calibrated for two SAR datasets (i.e. The Airborne Synthetic Aperture Radar (AIRSAR) and Canadian Space Agency radar satellite (RADARSAT-1)) acquired over two bare soil study areas with various soil characteristics. The Zribi/Dechambre model was then modified by revising the roughness parameter to obtain higher accuracy in estimating over a larger range of incidence angles (θ). A new empirical model was also proposed by combining the four parameters of Soil Moisture (SM), standard deviation of surface height -root mean square- (rms), correlation length (l), and θ. To this end, the most appropriate form of the regression model was investigated and used for each of these parameters to obtain the highest correlation between the in-situ data and values. A comparison of the empirical models showed that the modified Zribi/Dechambre had the highest accuracy in predicting values with the Root Mean Square Errors (RMSE) of 1.20 dB and 1.59 dB over Oklahoma and Quebec, respectively. Furthermore, coefficients values of the new proposed model remained stable in the two datasets unlike the other investigated models. In this study, the effects of l on the accuracy of the new proposed model were also assessed. It was concluded that l had a considerable impact on the accuracy of the proposed model and including this parameter can improve the accuracy by up to 1 dB.

中文翻译：

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用于估计裸土表面参数的新经验反向散射模型

摘要 已经提出了各种模型来估计合成孔径雷达 (SAR) 图像中的反向散射程度。但是，仍然需要根据不同研究区域的特点对这些模型进行校准，并提出新的模型，以达到估计后向散射系数 ( ) SAR 的尽可能高的准确度。在这项研究中，三个经验模型，包括 Champion、Sahebi 和 Zribi/Dechambre，最初针对两个 SAR 数据集（即机载合成孔径雷达 (AIRSAR) 和加拿大航天局雷达卫星 (RADARSAT-1)）在两个裸机上获得校准。具有各种土壤特征的土壤研究区。然后通过修改粗糙度参数来修改 Zribi/Dechambre 模型，以在更大范围的入射角 (θ) 上获得更高的估计精度。结合土壤水分（SM）、地表高度标准差-均方根-（rms）、相关长度（l）和θ四个参数，提出了一种新的经验模型。为此，研究了最合适的回归模型形式，并将其用于这些参数中的每一个，以获得现场数据和值之间的最高相关性。经验模型的比较表明，修改后的 Zribi/Dechambre 在预测值方面具有最高的准确度，在俄克拉荷马州和魁北克的均方根误差 (RMSE) 分别为 1.20 dB 和 1.59 dB。此外，与其他研究模型不同，新提出的模型的系数值在两个数据集中保持稳定。在这项研究中，还评估了 l 对新提出的模型准确性的影响。