Abstract:Traditional drought monitoring indexes mainly consider a single factor and often cannot comprehensively reflect the drought situation. Based on remote sensing and land surface data Assimilation System (CLDAS) data of China Meteorological Administration, a daily scale integrated drought monitoring model was established by Gradient Boosting Machine (GBM), one of machine learning algorithm, with multiple influencing factors and drought index which can directly reflect drought degree as independent variables and comprehensive meteorological drought index (CI) as dependent variable. It was researched by taking Drought in North China from 2015 to 2018 as a case. The results show that the model monitoring results were significantly correlated with the CI calculated values of the observation stations. The coefficients of determination of the training and test sets were 0.945 and 0.655, respectively, and the root mean square error (RMSE) was 0.033 and 0.082, respectively. The integrated drought monitoring model had high accuracy. The consistency rate between the model monitoring and CI calculated values was above 65%, and the correlation coefficients with the standard precipitation evapotranspiration index (SPEI) and relative soil moisture(RSM) were 0.68 and 0.6, respectively, which could better reflect the meteorological drought and agricultural drought. Monitoring of typical drought condition shows that the integrated drought monitoring model can accurately identify the drought occurrence of drought , and represent the situation of comprehensive drought by considering various drought influencing factors.