遥感反演土壤水分指数的适用性研究

Applicability Research of Parameters in Soil Moisture Inversion

土壤水分是地表过程的核心变量之一,实时准确掌握土壤水分的空间分布信息有利于水资源管理、洪水预测以及全球气候变化研究,且对于农业发展及灌溉等都具有重要的意义。遥感反演土壤水分指数发展十分迅速,但对于不同指数的适用性还缺乏定量的认识。本文利用2012至2014年那曲地区MODIS数据计算多种遥感反演土壤水分指数,并与地面观测数据进行相关性分析,结果表明:在非冻融时期,ATI、SEE以及TVDI与土壤水分相关性较好,从相关系数来看,在不考虑植被覆盖条件和低植被覆盖条件下与土壤水分相关性较好的指数依次是ATI、SEE和TVDI;而在高植被覆盖条件下与土壤水分相关性较好的指数依次是SEE、TVDI、SWCTI和ATI;同时本文认为综合使用多种指数是实现土壤水分监测一种有效的途径,在不考虑植被覆盖和低植被覆盖条件下综合使用ATI、SEE组合,在高植被覆盖条件下综合使用SEE、ATI、NDVI组合可以更有效地监测土壤水分的时空分布。

Soil moisture is one of the core variables of the surface process, and accurately grasping the spatial distribution information of soil moisture in real time is not only beneficial to water resources management, flood forecasting and global climate change, but also can be important for agricultural development and irrigation management. At present research, remote sensing parameters for retrieving soil moisture have been well developed, however, the applicability of different parameters has not yet been quantitatively recognized. In this paper, the MODIS data from Naqu area are used to calculate a variety of remote sensing soil water parameters, and carry out the correlation analysis with ground observation data, the result shows that ATI, SEE and TVDI are well correlated with soil moisture during non-freezing period. From the correlation coefficient, the parameters related to soil moisture under arbitrary vegetation and low vegetation cover conditions are ATI, SEE and TVDI. The parameters related to soil moisture under high vegetation cover conditions are SEE, TVDI, SWCTI and ATI. At the same time, this paper believes that the comprehensive use of multiple parameters is an effective way to achieve soil moisture. Under the condition of not considering vegetation and low vegetation cover, the combination of ATI and SEE can be used, and under the condition of high vegetation coverage, the combination of SEE, ATI and NDVI can be used.