基于AMSR-E与MODIS数据的新疆土壤水分协同反演与验证

CO-INVERSION AND VALIDATION OF LARGE-AREA SOIL MOISTURE BASED ON MODIS AND AMSR-E DATA

针对当前遥感在大范围土壤水分估算中面临的问题,提出将被动微波遥感数据与光学/热红外遥感数据在模型中协同反演陆表土壤水分的新方法:利用MODIS的光学与热红外波段反演土壤水分的基准值;利用AMSR-E传感器的X波段反演土壤水分的日变化量,然后集成二者建立土壤水分协同反演模型。以新疆为实验区,采用在典型地区获取的365个土壤水分实测值,对该模型进行了验证与精度分析。结果表明,协同反演模型的估算结果与地面实测值之间有着更好的相关性和较小的均方根误差,明显优于单一数据源或单一模型的反演结果。

In view of the fact that the current soil moisture retrieval from remotely sensed data is low in accuracy, a new integrated approach termed ＂ Co-inversion of land surface soil moisture by integrating optical, thermal infrared and passive microwave remote sensing data＂ was proposed. Specifically, the MODIS optical and thermal infrared bands are used to derive soil moisture benchmark, and the AMSR-E 10.7 GHz channel data to estimate daily variation of land surface soil moisture. Then the two are integrated, building up a co-inversion model for soil moisture retrieval over a large area. Xinjiang was cited as experiment zone. A total of 365 in-situ measured soil moisture values were collected from a typi- cal area and used to test the proposed inversion model. Verification analysis with the ground truthing data of the study area shows that the co-inversion of optical/thermal and microwave remotely sensed data displays higher correlation coefficient and smaller root mean square errors （RMSE） than any inversion using one single data source.