Estimation Of Soil Moisture Based On Multi-source Remote Sensing Data

Soil moisture is the amount of moisture in the soil.Sufficient soil moisture is the basic condition for growth of plant and agricultural production,while low soil moisture is a quantitative index of drought disaster.Especially,under the condition of global warming,soil moisture has increasingly attracted the extensive attention of all circles of the society and become the focus of oversea and domestic researchers.Huai River basin is the important agriculturel and grain production base.Geographic conditions are complex and weather conditions are volatile in Huai River basin,along with frequent disturbance of human activity,which leading to frequent drought and flood disasters.In consequence,taking Huai River basin as the research area in this article is typical.Moreover,traditional way of soil moisture monitoring which has the disadvantage of low efficiency,time-consuming and small range often result in of late-known and serious of drought disaster.Recently,rapid development of remote sensing technology provides a technical support for comprehensive and quick monitor of soil moisture.There are some limitations of monitoring by single remote sensing platform and sensor,and monitoring based on multi-source remote sensing data becomes a new research hotspot with the supplement of remote sensing data sources.Based on optical remote sensing data(MODIS data with 1 km resolution)with passive microwave remote sensing data(FY-3B and SMOS data with 25 km resolution),a new method of estimating soil moisture is put forward,and soil moisture with integrality and 1km resolution is obtained.Finally,research in this article can provide a new method and improve the integrality and exactness of soil moisture monitored by the means of remote sensing technology.Specific contents are as follows:(1)Basing on apparent thermal inertia model(ATI)wihch is applied in low vegetation covered area and vegetation supply water index(VSWI)which is applied in high vegetation covered area,comprehensive drought index(CDI)is developed which is suitable for different vegetation conditions.Soil moisture of 8 days in Huai River basin is inverted with the use of CDI based MODIS data,and then correlation analysis and verification is conducted with measured data.(2)The ascending,descending soil moisture of FY-3B and SMOS data are fused with the method of image fusion,and a more complete,fused daily soil moisture is got.Then the fused FY-3B and SMOS data are fused further,and the totally complete microwave soil moisture is obtained(MSM).(3)MSM data with 1 km resolution is obtained by MSM with 25 km resolution interpolation.Combining MSM data(1 km)with CDI data(1 km),6 regression models are built and then the best regression model is selected out.In order to get the complete soil moisture with 1km resolution of Huai River basin,the CDI data is calculated and supplied.If the pixel of CDI data is not null,the value of CDI is plug into the best regression model,,and then the dimensionless parameter is replaced by soil moisture.Otherwise the value of MSM is plug into the best regression model for calculation.Finally,the verification of calculation result is made with measured data.