基于Sentinel-1/2改进极化指数和纹理特征的土壤含盐量反演模型

Synergistic Estimation of Soil Salinity Based on Sentinel -1 /2 ImprovedPolarization Combination Index and Texture Features

目前Sentinel-1/2协同反演植被土壤含盐量的研究大多是基于Sentinel-2光谱信息和Sentinel-1后向散射系数,没有考虑Sentinel-2光谱信息容易受土壤亮度等信息影响,Sentinel-1后向散射系数容易受土壤粗糙度和水分影响。为进一步提高Sentinel-1/2协同反演植被土壤含盐量的精度,用水云模型对雷达卫星后向散射系数进行校正,消除植被影响;然后协同Sentinel-2纹理特征,基于VIP、OOB、PCA 3种变量筛选和RF、ELM、Cubist 3种机器学习回归模型构建植被土壤含盐量反演模型。研究结果表明:经过水云模型去除植被影响后的雷达后向散射系数及其极化组合指数与土壤含盐量的相关性有一定程度的提高。不同变量选择方法与不同机器学习方法耦合模型在反演土壤含盐量中,OOB变量筛选方法与RF、ELM和Cubist 3种机器学习方法的耦合模型精度最佳,建模集和验证集的R2都在0.750以上,且验证集的RMSE和MAE均最小;其中OOB-Cubist耦合模型精度最高,且R_(v)^(2)/R_(c)^(2)为0.955,具有良好的鲁棒性。研究可为机器学习协同物理模型、光学卫星协同雷达卫星在土壤含盐量反演中的进一步应用提供思路。

Most of the current studies on Sentinel-1/2 synoptic inversion of vegetation soil salinity were based on Sentinel-2 spectral information and Sentinel-1 backscattering coefficients,without considering the two aspects that Sentinel-2 spectral information was susceptible to soil brightness and Sentinel-1 backscattering coefficients were susceptible to soil roughness and moisture.Therefore,in order to further improve the accuracy of Sentinel-1/2 synoptic inversion of vegetation soil salinity,the Sentinel-1 backscatter coefficients were corrected with a water cloud model to eliminate the influence of vegetation.Then,the corrected backscatter coefficients and Sentinel-2 texture features screened by VIP,OOB and PCA were used to construct soil salinity inversion models based on RF,ELM and Cubist.The results showed that the correlation between the radar backscatter coefficient and the soil salinity was improved to some extent after the removal of vegetation effects by the water cloud model.For the coupled models of different variable selection methods and different machine learning methods,OOB had the best performance in soil salinity inversion when being coupled with RF,ELM and Cubist,with R2 above 0.750 for both modeling and validation sets.And OOB-Cubist model had the highest accuracy and R_(v)^(2)/R_(c)^(2) was 0.955,which had good robustness.It provided some ideas for further applications of machine learning in collaboration with physical models and optical satellites in collaboration with radar satellites in soil salinity inversion.