Inversion Of Soil Moisture Using Modis Data

Soil moisture is an important indicator for monitoring soil degradation. It has devastating impacts on agriculture, water resources, economy and environment. It plays an important role on the water and energy exchange at the land surface/atmosphere interface. So it is significant to do research on how to monitor soil moisture by remote sensing, which can provide information on large area quickly and easily.After analyze all kinds of information about soil moisture reversion and compare different quantitative remote sensing models, we think it is reasonable to use apparent thermal inertia(ATI) to retrieve soil moisture of bare soil or area with sparse canopies and temperature vegetation dryness index(TVDI) or Ts/NDVI for area with fractional vegetation cover.We use TVDI to monitor soil moisture in Xinjiang municipality in Augusts September and October, based on the MODIS products MOD11A2 and MOD13A2. Validation of the retrieved information by the field data has shown that TVDI is closely correlated to soil moisture. Then we use TVDI to monitor soil moisture in China by zones. The indicator can reflect the soil moisture status in the zone, but the information of different zones can't compare with each other, because different zones have different dry-wet edges which relative to soil moisture. We try to improve the TVDI and use the Ts/NDVI slope to monitor soil moisture in China, which can get the real soil moisture, not the relative data. So we can analyze the spatial and temporal variations of soil moisture. Due to the lack of field data of soil moisture, we can't get the quantitative relationship between the Ts/NDVI slope and soil moisture. Though we try to use the empirical relationship which was made by different remote sensing data, the result is not good. So we need do more research on the relationship between the Ts/NDVI slope and soil moisture using MODIS data.We initially propose to define the dry-wet edges using the middle ranges of NDVI, based on the features of Ts-NDVI space. A least square linear regression is applied to define the edge parameters.We initially analyze the spatial distribution and temporal evolution of soil moisture in Xinjiang municipality by remote sensing, and the result is fit to the annual precipitation and annual average relative humidity.