基于多极化SAR数据的黑土区农田土壤水分反演研究OA

Farmland soil moisture is an important parameter to characterize surface drought.The accurate acquisition and long-term monitoring of soil moisture spatio-temporal distribution information are closely related to black land ecological protection and national food security.Microwave remote sensing is an effective means to monitor soil moisture.It can achieve remote access to the required information and is not affected by external weather.However,the scattering con-tribution of vegetation and soil surface roughness greatly reduces the accuracy of soil moisture estimation.In order to solve the above problems,this paper takes the friendship farm in Shuangyashan City of Heilongjiang Province as the re-search area,constructs the radar backscatter parametric model based on the multi polarization SAR data,and develops the soil moisture retrieval algorithm suitable for the farmland in the black soil area of Northeast China.Firstly,The Water Cloud Model suitable for crop growth period was selected as the forward model.The vegetation water content was estimated by vegetation index(GNDVI,NDVI,EVI),and then the contribution to vegetation backscattering coefficient was separated;At the same time,the combined roughness parameter Zg was introduced to weaken the influence of soil surface roughness on backscattering coefficient.Finally,the simulated backscatter coefficient and radar backscatter coeffi-cient were used to construct the cost function to achieve high-precision retrieval of soil moisture.The results showed that:(1)using GNDVI to establish vegetation water content estimation model had better effect,and the determination coefficient R2 reached 0.73 for corn and 0.83 for soybean;(2)The precision of soil moisture retrieval and surface roughness retrieval was high,and the root mean square errors of corn field were 0.016cm3/cm3 and 0.168cm,respective-ly;The root mean square errors of soybean fields were 0.031cm3/cm3 and 0.201cm,respectively.This study has impor-tant reference value for monitoring drought and flood disasters and field management in black soil region.