Improving the quality of remotely sensed precipitation product from GPM satellites by using a spatial random forest
Satellite remote-sensing precipitation products are currently the main source for obtaining large-scale and continuous precipitation observations.However,currently available satellite remote-sensing precipitation products have coarse spatial resolution and suffer from certain systematic biases.Thus,this paper aims to downscale the precipitation data and remove its inherent systematic biases.This paper proposes a two-stage Spatial Random Forest(SRF)method(SRF-SRF)by fully considering the influence of high-resolution environmental variables(including topography,NDVI,surface temperature,latitude,and longitude)on the precipitation and the spatial correlation of neighboring remotely sensed precipitation(stations).Taking the Global Precipitation Measurement Mission(GPM)monthly precipitation data of Sichuan Province from 2015-2019 as an example,its quality is enhanced with the help of SRF-SRF.The calculation results are compared with those of seven existing methods,including Geo-Weighted Regression(GWR),Back-Propagation Neural Network(BPNN),Random Forest(RF),Kriging interpolation of station precipitation(Kriging),Geographic Difference Analysis correction after downscaling by SRF(SRF-GDA),SRF correction after downscaling by bilinear interpolation(Bi-SRF),and annual precipitation downscaled by SRF.Subsequently,the results are scaled by month and corrected using SRF(SRFDis).Experimental analysis shows the following:(1)At the monthly scale,compared with the original GPM,the mean absolute error(MAE)of SRF-SRF is reduced by 19.51％,and the medium error(RMSE)is reduced by 16.35％.The accuracy is better than those of other methods.At the seasonal scale,SRF-SRF has the smallest error in winter and the largest error in summer,but its calculation accuracy is better than those of other methods.At the annual scale,the four SRF-based methods(including SRF-SRF,SRF-GDA,Bi-SRF,and SRFdis)outperform GWR,BPNN,and RF.The accuracy of SRF-SRF is higher than that of Bi-SRF and SRF-GDA.(2)The spatial-distribution continuity of SRF-SRF precipitation products is better,and the local precipitation details are significantly improved.(3)The spatial correlation of precipitation plays an important role in the improvement in GPM precipitation quality.(4)SRF-SRF based on the monthly scale is better than SRFdis based on the annual scale.This finding indicates that NDVI can be used for precipitation-quality enhancement at the monthly scale in Sichuan province.This paper proposes a two-stage satellite precipitation product-quality enhancement method that considers spatial correlation.The method takes into account the spatial autocorrelation between precipitation and combines downscaling and calibration while integrating environmental factors.Accordingly,the spatial resolution and accuracy of precipitation products improve.Experimental results show that the new method outperforms the other seven classical methods and is more applicable to the quality improvement of precipitation products in complex terrain.
remote sensingprecipitationdownscalingpoint and surface fusionrandom forestGPMmachine learning
NETL
NSTL
万方数据
