Inversion Method Of Surface Soil Moisture In North China Plain Based On Multi-source Image Fusion

North China Plain is an important animal husbandry production and commodity grain base in China.Agricultural water consumption accounts for more than 60 % of total water consumption.Soil moisture can directly reflect the dry and wet state of soil.By using microwave remote sensing technology,regional soil moisture can be accurately and real-time obtained,which can effectively improve the level of agricultural drought monitoring.It is of great significance for the implementation of precision irrigation and agricultural modernization in North China Plain.Taking Daxing District of Beijing as a typical study area,this paper uses Sentinel-1,Sentinel-2 and Landsat 8 image data to divide the study into bare soil area and vegetation cover area(spring maize,summer maize,low-dwarf crops and woodland)by supervised classification.Inversion of surface soil moisture based on water cloud model and RBF neural network model in bare soil area and vegetation cover area,and compared with the soil moisture accuracy of the linear model based on VV polarization and VH polarization.The main conclusions are as follows:(1)Based on Landsat 8 images,the land use types in Daxing District is supervised Classification results show that: the overall classification accuracy of maximum likelihood,neural network and support vector machine is greater than 90.53%,and the Kappa coefficient is greater than 0.87.The overall classification accuracy of parallelepiped,minimum distance and Mahalanobis distance is between 65.38%-75.12%,and Kappa coefficient is between0.57-0.68.At the same time,the effect map and the field survey results are compared,and the maximum likelihood is finally selected to supervised classification of the study area.(2)Evaluation of the effect of removing vegetation cover.Study on removal of vegetation cover by optical image,For VV polarization or VH polarization,the effect of bringing the vegetation water content calculated by NDVI into the water cloud model to remove the influence of vegetation layer is better than that of NDWI;Bringing the vegetation water content calculated by Landsat 8’s index NDVI into the water cloud model to remove the effect of vegetation layer is better than Sentinel-2’s index NDVI;After VV and VH polarization penetrate the vegetation layer,VH polarization decays more.(3)Establishment of soil moisture inversion model in vegetation coverage area.The influence of vegetation cover is removed by the water cloud model,by removing the VV polarization and VH polarization backscatter coefficient,the polarization difference between VV polarization and VH polarization,the normalized vegetation index NDVI,the radar incident angle combined with RBF neural network Constructing a soil moisture retrieval model in vegetation covered area.Through verification analysis,the correlation between the simulated value and the measured value of the model was good(r =0.855,RMSE = 0.024 cm~3/cm~3).Compared with the VV polarization linear regression model,the correlation(r)increased by 0.103,and the root mean square error(RMSE)decreased by 0.034 cm~3/cm~3.Compared with the VH polarization linear regression model,the correlation(r)increased by0.13,and the root mean square error(RMSE)decreased by 0.01 cm~3/cm~3.(4)Establishment of soil moisture inversion model in bare soil area.The soil moisture inversion model in bare soil area was constructed by VV polarization backscattering coefficient,VH polarization backscattering coefficient,polarization difference between VV polarization and VH polarization,radar incident angle combined with RBF neural network.Through verification analysis,the correlation between the simulated value and the measured value of the model was good(r =0.796,RMSE = 0.029 cm~3/cm~3).Compared with the VV polarization linear regression model,the correlation(r)increased by 0.044,and the root mean square error(RMSE)decreased by 0.029 cm~3/cm~3.Compared with the VH polarization linear regression model,the correlation(r)increased by 0.071,and the root mean square error(RMSE)decreased by 0.005 cm~3/cm~3.