基于雷达遥感的不同深度土壤含盐量反演模型

Inversion Model of Soil Salt Content in Different Depths Based on Radar Remote Sensing

为及时、有效地监测盐渍化土壤含盐量,以内蒙古河套灌区沙壕渠灌域为研究区,将Sentinel-1雷达影像作为数据源,同步采集不同深度土壤含盐量数据,通过组合两组雷达后向散射系数构建多种指数,并用灰度关联(Gray correlation degree,GCD)排除共线性强的指数,采用偏最小二乘回归(Partial least squares regression,PLSR)、分位数回归(Quantile regression,QR)和支持向量机(Support vector machine regression,SVM) 3种方法,构建0~10 cm、10~20 cm不同深度下的土壤含盐量反演模型。结果表明,在3种回归方法中,SVM回归模型的精度最高,模型建模集决定系数R2、验证集决定系数R2均在0.4以上,建模集均方根误差RMSEc、验证集均方根误差RMSEp均小于0.3%,分位数回归模型次之,偏最小二乘回归模型最差;在各反演深度下,0~10cm深度的反演精度均高于10~20 cm深度的反演精度,其中在0~10 cm深度下SVM反演模型效果优于其他模型,Rc2、RP2分别为0.568和0.686,RMSEc、RMSEp分别为0.201%和0.151%。本研究可为雷达遥感监测裸土期土壤盐渍化提供参考。

With the aim to monitor the salinization of soil salt content timely and effectively,taking Shahaoqu District of Hetao Irrigation Area as study area,the Sentinel-1 image as a data source,synchronous acquisition different depths of soil salinity data,by combining the two groups of radar backscatter coefficient to build a variety of indices,by using gray correlation degree(GCD) index to exclude the index with strong collinearity,and partial least squares regression(PLSR),quantile regression(QR) and support vector machine regression(SVM) were used to construct soil salinity inversion models at different depths of 0~10 cm and 10~20 cm.The results showed that among the three regression methods the accuracy of SVM regression model was the highest,the model modeling set determination coefficient Rc2 and the validation set determination coefficient Rp2 were all above 0.4,the modeling set root mean square error RMSEc and the validation set root mean square error RMSEp were all less than 0.3%,QR regression model was the next,PISR regression model was the worst.At each inversion depth,the inversion accuracy of 0~10 cm was higher than that of 10~20 cm,among which the SVM inversion model was better than other models at 0‖10 cm depth,Rc2 and Rp2 were 0.568 and 0.686,respectively,and RMSEc and RMSEp were 0.201% and 0.151%,respectively.The results could provide a reference for monitoring soil salinization in bare soil stage by radar remote sensing.