{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T15:41:17Z","timestamp":1771947677801,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2020,7,17]],"date-time":"2020-07-17T00:00:00Z","timestamp":1594944000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41630859"],"award-info":[{"award-number":["41630859"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Droughts are one of the costliest natural disasters. Reliable drought monitoring and prediction are valuable for drought relief management. This study monitors and predicts droughts in Xinjiang, an arid area in China, based on the three drought indicators, i.e., the Standardized Precipitation Index (SPI), the Standardized Soil Moisture Index (SSMI) and the Multivariate Standardized Drought Index (MSDI). Results indicate that although these three indicators could capture severe historical drought events in the study area, the spatial coverage, persistence and severity of the droughts would vary regarding different indicators. The MSDI could best describe the overall drought conditions by incorporating the characteristics of the SPI and SSMI. For the drought prediction, the predictive skill of all indicators gradually decayed with the increasing lead time. Specifically, the SPI only showed the predictive skill at a 1-month lead time, the MSDI performed best in capturing droughts at 1- to 2-month lead times and the SSMI was accurate up to a 3-month lead time owing to its high persistence. These findings might provide scientific support for the local drought management.<\/jats:p>","DOI":"10.3390\/rs12142298","type":"journal-article","created":{"date-parts":[[2020,7,20]],"date-time":"2020-07-20T10:59:38Z","timestamp":1595242778000},"page":"2298","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Monitoring and Predicting Drought Based on Multiple Indicators in an Arid Area, China"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1587-6913","authenticated-orcid":false,"given":"Yunqian","family":"Wang","sequence":"first","affiliation":[{"name":"School of Geography and Tourism, Qufu Normal University, Rizhao 276826, China"},{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"Department of Geography, Ghent University, 9000 Ghent, Belgium"},{"name":"Sino-Belgian Joint Laboratory of Geo-information, Urumqi 830011, China"}]},{"given":"Jing","family":"Yang","sequence":"additional","affiliation":[{"name":"National Institute of Water and Atmospheric Research, Christchurch 8000, New Zealand"}]},{"given":"Yaning","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"}]},{"given":"Zhicheng","family":"Su","sequence":"additional","affiliation":[{"name":"China Institute of Water Resources and Hydropower Research, Beijing 100038, China"}]},{"given":"Baofu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geography and Tourism, Qufu Normal University, Rizhao 276826, China"}]},{"given":"Hao","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Geography and Tourism, Qufu Normal University, Rizhao 276826, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8902-3855","authenticated-orcid":false,"given":"Philippe","family":"De Maeyer","sequence":"additional","affiliation":[{"name":"Department of Geography, Ghent University, 9000 Ghent, Belgium"},{"name":"Sino-Belgian Joint Laboratory of Geo-information, 9000 Ghent, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2669","DOI":"10.5194\/hess-18-2669-2014","article-title":"Global meteorological drought\u2013Part 2: Seasonal forecasts","volume":"18","author":"Dutra","year":"2014","journal-title":"Hydrol. 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