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Researchers have developed many drought indices based on ground-based climate data and various remote sensing data. Ground-based drought indices are more accurate but limited in coverage; while the remote sensing drought indices cover larger areas but have poor accuracy. Applying data-driven models to fuse multi-source remote sensing data for reproducing composite drought index may help fill this gap and better monitor drought in terms of spatial resolution. Machine learning methods can effectively analyze the hierarchical and non-linear relationships between the independent and dependent variables, resulting in better performance compared with traditional linear regression models. In this study, seven drought impact factors from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor, Global Precipitation Measurement Mission (GPM), and Global Land Data Assimilation System (GLDAS) were used to reproduce the standard precipitation evapotranspiration index (SPEI) for Shandong province, China, from 2002 to 2020. Three machine learning methods, namely bias-corrected random forest (BRF), extreme gradient boosting (XGBoost), and support vector machines (SVM) were applied as regression models. Then, the best model was used to construct the spatial distribution of SPEI. The results show that the BRF outperforms XGBoost and SVM in SPEI estimation. The BRF model can effectively monitor drought conditions in areas without ground observation data. The BRF model provides comprehensive drought information by producing a spatial distribution of SPEI, which provides reliability for the BRF model to be applied in drought monitoring.<\/jats:p>","DOI":"10.3390\/rs14246398","type":"journal-article","created":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T08:41:41Z","timestamp":1671439301000},"page":"6398","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["Drought Monitoring and Performance Evaluation Based on Machine Learning Fusion of Multi-Source Remote Sensing Drought Factors"],"prefix":"10.3390","volume":"14","author":[{"given":"Yangyang","family":"Zhao","sequence":"first","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2894-9627","authenticated-orcid":false,"given":"Jiahua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Yun","family":"Bai","sequence":"additional","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}]},{"given":"Sha","family":"Zhang","sequence":"additional","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}]},{"given":"Shanshan","family":"Yang","sequence":"additional","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0201-0566","authenticated-orcid":false,"given":"Malak","family":"Henchiri","sequence":"additional","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"},{"name":"Laboratory of Eremology and Combating Desertification, Institut des Regions Arides (IRA), Medenine 4119, Tunisia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6133-1338","authenticated-orcid":false,"given":"Ayalkibet Mekonnen","family":"Seka","sequence":"additional","affiliation":[{"name":"Arba Minch Water Technology Institute, Water Resources Research Center, Arba Minch University, Arba Minch P.O. Box 21, Ethiopia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9131-5099","authenticated-orcid":false,"given":"Lkhagvadorj","family":"Nanzad","sequence":"additional","affiliation":[{"name":"National Remote Sensing Center, Information and Research Institute of Meteorology, Hydrology and Environment (IRIMHE), Ulaanbaatar 15160, Mongolia"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"33568","DOI":"10.1007\/s11356-019-06500-4","article-title":"Characterization of Drought Monitoring Events through Modis-and Trmm-Based Dsi and Tvdi over South Asia During 2001\u20132017","volume":"26","author":"Ali","year":"2019","journal-title":"Environ. Sci. Pollut. 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