微波遥感是土壤水分监测的重要手段,但微波遥感土壤水分产品的空间分辨率较低,难以满足区域尺度的应用需求.使用地理加权回归模型,以 1 km MODIS 产品的遥感地表温度(LST)和归一化植被指数(NDVI)作为辅助数据,将空间分辨率为 9 km的 SMAP被动微波土壤水分数据降尺度为 1 km,利用吉林省地面实测土壤水分数据,对降尺度后的 SMAP数据进行了精度验证.结果表明,该降尺度方法在吉林省适用性较好,降尺度结果与 SMAP数据在空间分布上保持了较高的一致性,小幅度提高了 SMAP 数据的精度,显著提高了 SMAP数据的空间细节和纹理特征.
Microwave Soil Moisture Downscaling Study of Jilin Province Based on Geographically Weighted Regression
At present,microwave remote sensing is an essential method for soil moisture monitoring.But the spatial resolution of soil moisture products from microwave remote sensing is so low that it is difficult to meet the application needs of regional scale.With the aim of downscaling SMAP passive microwave soil moisture from spatial resolution of 9 km to 1 km,remote sensing land surface temperature and normalized difference vegetation index of MODIS products were used as auxiliary data by a downscaling method based on geographically weighted regression model in Jilin Province.The downscaled SMAP passive microwave soil moisture data were compared with the in situ soil moisture data at ground moisture stations.The results show that the downscaling method is suitable for Jilin Province.The downscaling results retain a high consistency with the original soil moisture data,improve the accuracy of SMAP data slightly,and signifi-cantly improve the spatial details and texture characteristics of soil moisture.
万方数据
维普
