Retrieval And Downscaling Analysis Of Soil Moisture Based On FY-3B Microwave Data

As a key element in the water circle,soil moisture is the most directly reflect the status of soil moisture and reveal the drought and flood parameters,in areas such as agriculture,hydrology,meteorology,ecology,much attention has been paid to accurately obtain the soil humidity of the continuous increase of time and space weather forecast accuracy,improving the capacity of drought monitoring and early warning,strengthening water resources management ability and so on has important application value and practical significance.However,due to the spatial heterogeneity of soil moisture and the scarcity of soil moisture measurement stations,the traditional soil moisture observation data are scarce and their spatial representation is limited.At present,most of the satellite soil moisture products have low resolution,so using microwave remote sensing data to retrieve soil moisture and conduct downscaling analysis is an effective way to improve the application of soil moisture information.This study is based on the microwave band(X-band,Ka-band)data of FY-3B satellite from 2013 to 2015 and the measured data of the site.The Land Parameter Retrieving Model(LPRM)based on microwave radiation transmission mechanism is used to compare and optimize the Model Parameter solutions.Two methods of scaling down were introduced,including Discrete Cosine Transformation-Penalized Partial Least Square(DCT-PLS),Vegetation Temperature Condition Index(VTCI)and Soil Evaporative Efficiency(SEE).The inversion,interpolation,downscaling and accuracy evaluation of soil moisture data based on FY-3B satellite were studied.The main contents and conclusions of the study are as follows:(1)Select representative sites worldwide data,first of all,FY-3B soil moisture official product and LPRM inversion accuracy is verified and analysis,found the product accuracy and NDVI(Normalized Difference Vegetation Index)value is closely related to the four sites in area of low Vegetation coverage,NDVI value is small,FY-3B official product precision and LPRM inversion,FY-3B official product Root Mean square Error(RMSE)and Correlation Coefficient(R)of scheme 1 are 0.066 and 0.725,those of scheme 2 are 0.071 and 0.669,and those of scheme 3 are 0.049 and 0.858,respectively.However,in the four sites with high vegetation coverage,the high vegetation optical thickness has an impact on both the FY-3B official products and the LPRM algorithm products,but the impact on the FY-3B official products is more significant.The correlation coefficient between the products and the measured values of the site is negative,while the LPRM algorithm still maintains a good accuracy of soil moisture inversion.The parameter scheme of LPRM algorithm needs to be optimized for different regions.By comparing the inversion results of three different parameter schemes with the observation data of selected stations around the world,it is found that the inversion of parameter scheme 3 is the best,followed by the inversion of parameter scheme 1 and the inversion of parameter scheme 2.Finally,the most accurate parameter scheme was selected for soil moisture inversion in China region.(2)Both the soil moisture inversion results of FY-3B LPRM in China region based on the preferred scheme and the official products of FY-3B have the problem of low coverage rate.Therefore,DCT-PLS spatial interpolation algorithm is used to interpolate the inversion results,and the result of higher coverage rate is obtained.Based on ERA5-LAND data,the overall accuracy of FY-3B LPRM results was the best,followed by AMSR2 LPRM results,and FY-3B official products were the worst,with correlation coefficients of 0.611,0.588,0.387,and RMSE of 0.134,0.239,0.176,respectively.Both FY-3B LPRM results and FY-3B official products underestimated soil moisture,while AMSR2 LPRM products overestimated soil moisture.In terms of the spatial distribution of soil moisture data,it generally presents the characteristics of high in the east and low in the west,which is in line with the overall climate change law in China.In different regions,FY-3B LPRM products have better performance in eastern and central regions and southwest regions,with RMSE of 0.131 and 0.060 respectively.FY3B official products have better performance in northwest and northeast regions,with RMSE of 0.097 and 0.064 respectively.(3)Based on the spatial downscaling method of VTCI and SEE parameters,and taking full advantage of the high spatial resolution of MODIS NDVI and Land Surface Temperature(LST),the downscaling analysis of soil moisture in China was realized,and the spatial resolution of soil moisture in FY-3B LPRM was improved from 0.25° x0.25° to 0.01° x 0.01°.Use of NAQU site observation data of regional downscaling results are analyzed,the results show that two kinds of downscaling parameters of soil moisture downscaling results can well match the site observation value trend,the overall error,SEE parameters downscaling results than VTCI downscaling results and the correlation coefficient can reach more than 0.4.From the perspective of the spatial distribution of stations,the stations close to each other have similar characteristics in the distribution of error indexes.The areas with higher R and RMSE are located in the northwest part of the study area,and have a certain relationship with vegetation index.