实测高光谱和HSI影像的区域土壤含水量遥感监测研究

基于典型研究区植被冠层实测高光谱数据和HSI高光谱影像数据,通过相关分析选择与不同深度土壤含水量响应敏感波段,建立两者的土壤含水量反演模型,并用实测高光谱土壤含水量反演模型校正HSI影像土壤含水量反演的模型。结果表明:土壤含水量响应敏感波段区域为450~650 nm和850~920 nm;两种土壤含水量反演模型对土壤深度为0~10 cm的土壤含水量估算效果最好,其中实测冠层高光谱土壤含水量反演模型精度高于HSI影像土壤含水量反演模型,判定系数(R~2)分别为0.659和0.557;经过校正的HSI影像土壤含水量反演模型精度有了较大的提高,判定系数(R~2)从0.557提升到0.719,均方根误差(RMSE)为0.043 5,较好地提高了区域尺度条件下土壤含水量监测精度,因此运用该方法进行土壤含水量遥感监测是可行的,为进一步提高区域尺度下土壤含水量定量遥感监测提供参考借鉴。

Study on Soil Moisture Monitoring Base on Measured Hyperspectral and HSI Data

Based on measured vegetation canopy hyperspectral data and HSI hyperspectral image data in a typical region, sensitive bands to soil moisture in different soil depths were selected by correlation analyses to establish the optimal inversion model of soil moisture by HSI image data, and the inversion model was calibrated by HSI inversion model based on the measured soil moisture. The results showed that: the sensitive bands to soil moisture were in 450 – 650 nm and in 850 – 920 nm; the two soil moisture inversion models showed that: the estimation effect for soil moisture in 0 – 10 cm depth was the best, and the accuracy of the inversion model based on HSI image data was higher than the inversion model based on the measured hyperspectral data, and the coefficients of determination (R2) were 0.659 and 0.557, respectively; the accuracy of soil moisture inversion model was improved better after calibration, the coefficient of determination (R2) raised to 0.719 from 0.557, and root mean square error (RMSE) was of 0.043 5, which indicated that the improvement of monitoring accuracy on soil moisture at the regional scale and proved this technique is feasible to monitor soil moisture, and provided helps to further improve soil moisture monitoring by remote sensing at regional scale.