| Soil moisture is a key parameter in regulating the hydrothermal energy exchange between land and atmosphere, as well as an important part of the hydrologic cycle of the ecological system on ground surface and an important material source for terrestrial plants and soil organisms. Soil moisture is also an important input parameter of many models, such as climate model, hydrological model, ecological model, land surface model etc. The change of soil moisture will affect the hydrothermal process, then change the surface parameters and impact the climate. Therefore the quantitative monitoring of accurate soil moisture value is of great significance to agricultural production, global change strategy, ecological and environmental protection and many other fields.Different types of remote sensing platforms are currently used to infer soil moisture at different spatial and temporal scales, each with its specific characteristics and limitations. Compared to thermal infrared and active radar data, passive microwave data gives more accurate inversion of ground surface soil moisture. The passive microwave data, being sensitive to soil moisture information and having a repeat cycle of1to3days, can capture the temporal variation information of soil moisture, although its relatively low spatial resolution (25km-40km) makes it generally suitable for large-scale research. Average soil moisture information of large-scale grid can be obtained from a variety of active and passive microwave remote sensor platform with much ease. While the satellite-borne visible light and thermal infrared data can achieve a medium to high level of resolution (100m-lkm), it’s likely to be affected by weather conditions and has a less-than-ideal sensitivity to soil moisture. To overcome these issues, some of the current researches incorporate passive microwave data and optical data, with the purpose of improving both temporal and spatial resolution while achieving reliable soil moisture information.Choosing Mongolia and the Qinghai-Tibet Plateau region as the study area, this paper improves the algorithm of decomposing large-scale soil moisture via passive microwave radiometer AMSR-E soil moisture data and MODIS surface temperature, NDVI and albedo products.The four main study points of this paper are shown as follows:I. Retrieving soil moisture from passive microwave radiometer AMSR-EThe author calculates the25km AMSR-E soil moisture data from passive microwave radiometer AMSR-E data through an improved version of the Qp model. Based on Jackson’s single channel algorithm, the author uses37GHz V polarized brightness temperature to calculate the surface temperature, and get vegetation optical thickness from the experience relationship of vegetation water content and NDVI, in order to eliminate the influence of vegetation on the microwave signal. Finally, based on the dual-channel algorithm developed from Qp model, this paper eliminates the impact of surface roughness and obtains the25km soil moisture directly.II. Decomposing large-scale soil moisture utilizing space spectrum methodThis paper uses the method of space spectrum downscaling, which is improved from M. Montopoli’s algorithm, to decompose AMSR-E soil moisture data. By breaking down the gray-scale changes function of the soil moisture data into a series of superimposed periodic function using the Fourier transform, the author converts the soil moisture data to frequency domain images of the amplitude and phase through statistical conversion on periodic functions. Due to the fixed relationship between the soil moisture and the spatial frequency of the power spectral density in different spatial resolution images, the exponent relationship of the power spectral density and spatial frequency can be created.III. Analyzation of the empirical relationship of soil moisture parameters based on the evapotranspiration theoryThe paper analyzes the long sequence response mechanism between soil moisture and optical remote sensing parameters, such as surface temperature, vegetation index and surface albedo. This method is based on the triangle Ts-NDVI feature space learning from the evapotranspiration theory and statistical method of multiple linear regression analysis, from which the author gets the empirical relationship between soil moisture and the remote sensing optical factors above. IV. The method of space spectrum downscaling via high-resolution phase information with its application and field verificationThe author retrieves high-resolution phase information from MODIS data via the relationship between AMSR-E soil moisture and MODIS surface temperature, NDVI and albedo, and then substitution to replace soil moisture data the low-resolution phase information. Then by the Fourier inverse transform, spatial resolution better than the resolution of passive microwave data can be obtained by downscaling soil moisture data.By verifying the results with the CEOP ground data in Mongolia and Qinghai-Tibet Plateau study area, it shows that the high-resolution soil moisture data obtained using the downscaling method introduced in this paper matches the measured data in good agreement. Thus the high accuracy and credibility of the method is confirmed. |
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