基于Sentinel卫星及无人机多光谱的滨海冬小麦种植区土壤盐分反演研究——以黄三角垦利区为例

奚雪,赵庚星,高鹏,崔昆,李涛

Inversion of Soil Salinity in Coastal Winter Wheat Growing Area Based on Sentinel Satellite and Unmanned Aerial Vehicle Multi-Spectrum— A Case Study in Kenli District of the Yellow River Delta

XI Xue,ZHAO GengXing,GAO Peng,CUI Kun,LI Tao

表6 基于卫星影像光谱指数的土壤盐分估测模型

Table 6 Soil salinity estimation model based on spectral index of satellite image

建模方法 Modeling approach 估测模型 Estimating model 建模精度 Modeling accuracy 验证精度 Verification accuracy R2 RMSE R2 RMSE 逐步回归Stepwise regression Y=-97.012×NDVI+22.298×RVI-13.905×SI-8.489 0.509 1.190 0.434 0.874 偏最小二乘法Partial least squares Y=-57.4889×NDVI+13.3418×RVI+21.9667×SI-9.0323 0.555 0.940 0.414 0.964 BP神经网络The BP neural network — — — 0.602 0.900 支持向量机Support vector machine — 0.612 1.166 0.438 0.432