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Drought is a complex natural hazard. A remote sensingbased drought index, the integrated drought condition index (IDCI-RF) for monitoring agricultural drought, by integrating the droughtrelated information based on random forest (RF) regression technique was proposed. The optimal droughtrelated factors over different time periods were selected through correlation analyses between 17 remote sensing drought indices and the 3month standardized precipitationevapotranspiration index (SPEI-3).Based on the RF regression method, the IDCI-RF index which considered land cover data, climate classification information, digital elevation data and multisource droughtrelated factors comprehensively was established. The determination coefficients, RMSE and MAE values were calculated between the 3month SPEI and the IDCI which was derived from the RF, Cubist and Bagging model, respectively. Results showed that compared with other two ensemble methods, IDCI-RF produced higher correlation coefficient values with in situ variables and all the determination coefficients varied between 0.54 and 0.68. Additionally, regression analyses were performed between the IDCI-RF and the in situ reference data to further evaluate the capability of regional drought condition monitoring and analyses were performed in seven main provinces of the study area. Results showed that the IDCI-RF was agreed well with the SPEI-3 in different provinces, and all the determination coefficients were above 0.7. The yearly IDCI-RF variations in 21 representative meteorological sites were compared with that of the in situ drought indices to evaluate the temporal drought monitoring capability of this index. Results showed that the IDCI-RF exhibited consistent variations with the in situ reference data at the regional scales in most cases. The spatial changes in the IDCI-RF maps were also compared with the changes in the in situ reference data at the meteorological sites to assess the IDCI-RF performance in monitoring shortterm drought conditions. Results showed that the IDCI-RF maps basically showed a similar spatial pattern with the in situ reference data. The practical application of IDCI-RF demonstrated that it can provide accurate and detailed drought condition and IDCI-RF method can be effectively used for regional agricultural drought monitoring.