{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T09:07:35Z","timestamp":1773911255111,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T00:00:00Z","timestamp":1683504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2021YFD1500100"],"award-info":[{"award-number":["2021YFD1500100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2021SYHZ0013"],"award-info":[{"award-number":["2021SYHZ0013"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jilin Province and the Chinese Academy of Sciences Science and Technology Cooperation High-tech Industrialization Special Fund Project","award":["2021YFD1500100"],"award-info":[{"award-number":["2021YFD1500100"]}]},{"name":"Jilin Province and the Chinese Academy of Sciences Science and Technology Cooperation High-tech Industrialization Special Fund Project","award":["2021SYHZ0013"],"award-info":[{"award-number":["2021SYHZ0013"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Agricultural drought significantly impacts agricultural production, highlighting the need for accurate monitoring. Accurate agricultural-drought-monitoring models are critical for timely warning and prevention. The random forest (RF) is a popular artificial intelligence method but has not been extensively studied for agricultural drought monitoring. Here, multi-source remote sensing data, including surface temperature, vegetation index, and soil moisture data, were used as independent variables; the 3-month standardized precipitation evapotranspiration index (SPEI_3) was used as the dependent variable. Soil texture and terrain data were used as auxiliary variables. The bias-corrected RF model was used to construct a random forest synthesized drought index (RFSDI). The drought-degree determination coefficients (R2) of the training and test sets reached 0.86 and 0.89, respectively. The RFSDI and SPEI_3 fit closely, with a correlation coefficient (R) above 0.92. The RFSDI accurately reflected typical drought years and effectively monitored agricultural drought in Northeast China (NEC). In the past 18 years, agricultural drought in NEC has generally decelerated. The degree and scope of drought impacts from 2003 to 2010 were greater than those from 2010 to 2020. Agricultural drought occurrence in NEC was associated with dominant climatic variables such as precipitation (PRE), surface temperature (Ts), relative humidity (RHU), and sunshine duration (SSD), alongside elevation and soil texture differences. The agricultural drought occurrence percentage at 50\u2013500 m elevations reached 94.91%, and the percentage of occurrence in loam and sandy soils reached 90.31%. Water and temperature changes were significantly correlated with the occurrence of agricultural drought. Additionally, NEC showed an alternating cycle of drought and waterlogging of about 10 years. These results have significant application potential for agricultural drought monitoring and drought prevention in NEC and demonstrate a new approach to comprehensively evaluating agricultural drought.<\/jats:p>","DOI":"10.3390\/rs15092477","type":"journal-article","created":{"date-parts":[[2023,5,9]],"date-time":"2023-05-09T01:06:28Z","timestamp":1683594388000},"page":"2477","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Construction and Assessment of a Drought-Monitoring Index Based on Multi-Source Data Using a Bias-Corrected Random Forest (BCRF) Model"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-8764-4173","authenticated-orcid":false,"given":"Yihao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Economics and Management, Northeast Agricultural University, Harbin 150030, China"},{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"given":"Linghua","family":"Meng","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"given":"Huanjun","family":"Liu","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6650-1022","authenticated-orcid":false,"given":"Chong","family":"Luo","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"given":"Yilin","family":"Bao","sequence":"additional","affiliation":[{"name":"College of Earth and Planetary Sciences, Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Beisong","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Agricultural, Jilin Agricultural University, Changchun 130102, China"}]},{"given":"Xinle","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Technology, Jilin Agricultural University, Changchun 130102, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"154550","DOI":"10.1016\/j.scitotenv.2022.154550","article-title":"Grassland productivity response to droughts in northern China monitored by satellite-based solar-induced chlorophyll fluorescence","volume":"830","author":"Wang","year":"2022","journal-title":"J. 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