关键词: 机器学习, 多源遥感, 干旱, 标准化降水蒸散指数, 山东省

Abstract: In this study, Shandong province was taken as the research area, and three machine learning methods, namely Bias corrected Random Forest(BRF), Support Vector Regression(SVR) and Cubist model were selected to integrate multiple impact factors to simulate the standardized precipitation evapotranspiration index on a three-month time scale(SPEI-3), so as to provide a method for accurate monitoring of drought in Shandong province. SPEI-3 values of 23 stations from 2001 to 2017 were taken as dependent variables. Multi-source remote sensing data including precipitation, surface temperature, evapotranspiration, potential evapotranspiration, normalized difference vegetation index and soil moisture were taken as independent variables. Independent variables and dependent variables constituted 80% of the data set as training set and 20% as test set. According to the BRF model, the simulated values of each site in the study area and the relative importance of each impact factor were obtained. The spatial distribution diagram of SPEI-3 was drawn and verify them. The results showed that the multiple factor simulation was more effective than the single factor. The R2 of the simulated value and observed value in the BRF model’s test set reached 0.856, and the RMSE of the root mean square error was 0.359. The BRF model can well simulate the SPEI-3’s values of the sites. Simulated and observed values for most sites are consistent with the drought trend, and reflect the number of months of different drought conditions is basically the same. The spatial distribution of SPEI-3 simulated by the BRF model is basically consistent with the drought degree of observed SPEI-3 at the site, and SPEI-3 spatial distribution of raster data outside the sites can also reflect the drought situation more accurately. According to the BRF model, the drought situation in Shandong province can be monitored more accurately at the site and spatial scale.

Key words: Machine learning, Multi-source remote sensing, Drought, Standardized precipitation evapotranspiration index, Shandong province