渭干河—库车河三角洲绿洲棉田土壤盐分估算及遥感反演

基于研究区的野外采样数据与Landsat 8遥感影像提取的增强型植被指数,构建渭干河—库车河三角洲绿洲棉田土壤盐分估算模型,并对土壤盐分的空间分布格局进行预测。结果表明:(1)由土壤含盐量与增强型归一化植被指数(ENDVI)构建的线性回归模型(y=-56.494x+22.687)拟合效果最好(R2=0. 886,RMSE=0. 907)。(2)通过选取的82个采样点,依据最佳遥感反演模型,预测出研究区土壤含盐量在9.33～26.99 g·kg-1之间变化,平均值为17.42 g·kg-1,标准差为2.30 g·kg-1,预测结果与土壤盐分的实测值较为一致。(3)利用地统计分析方法制作研究区棉田土壤盐分的空间分布图,分析可知土壤盐分从绿洲内部向外围呈逐渐增加的趋势。

Estimation and inversion modeling of salinity of cotton field soil using remote sensing in the Delta Oasis of Weigan and Kuqa Rivers

Based on data from the field sampling and the enhanced vegetation index from Landsat 8 remote sensing images, we tried to construct the salinity inversion estimation model of cotton field soil in the Delta Oasis of Weigan and Kuqa Rivers. Also, the model was used to predict the spatial distribution pattern of soil salinity in the region. The results showed that: (1) By using soil measured salinity and Enhanced Normalized Difference Vegetation Index (ENDVI), a linear model, y=-56.494x+22.687, was constructed with a R² of 0.886 and RMSE of 0.907. (2) The predicted salinity by the inversion model based on the remote sensing data varied from 9.33 to 26.99 g·kg^-1 with an average value of 17.42 g·kg^-1 and a standard deviation of 2.30 g·kg^-1. Compared the data from the selected 82 sampling points with the predicted results, the predicted results of soil salinity were consistent with the measured results. (3) A spatial distribution map of soil salinity of cotton field in the study area was developed by using the method of geo-statistical analysis. The analysis demonstrated that soil salinity was rising gradually from the interior to the periphery of the oasis.