{"title":"利用多元线性回归和 GLDAS 产品评估 SMAP 和 CYGNSS 土壤湿度在干旱预测中的作用","authors":"Komi Edokossi, Shuanggen Jin, Andrés Calabia, Iñigo Molina, Usman Mazhar","doi":"10.14358/pers.23-00075r2","DOIUrl":null,"url":null,"abstract":"Drought is a devastating natural hazard and exerts profound effects on both the environment and society. Predicting drought occurrences is significant in aiding decision-making and implementing effective mitigation strategies. In regions characterized by limited data availability, such\n as Southern Africa, the use of satellite remote sensing data promises an excellent opportunity for achieving this predictive goal. In this article, we assess the effectiveness of Soil Moisture Active Passive (SMAP) and Cyclone Global Navigation Satellite System (CYGNSS) soil moisture data\n in predicting drought conditions using multiple linear regression???predicted data and Global Land Data Assimilation System (GLDAS) soil moisture data.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Evaluation of SMAP and CYGNSS Soil Moistures in Drought Prediction Using Multiple Linear Regression and GLDAS Product\",\"authors\":\"Komi Edokossi, Shuanggen Jin, Andrés Calabia, Iñigo Molina, Usman Mazhar\",\"doi\":\"10.14358/pers.23-00075r2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Drought is a devastating natural hazard and exerts profound effects on both the environment and society. Predicting drought occurrences is significant in aiding decision-making and implementing effective mitigation strategies. In regions characterized by limited data availability, such\\n as Southern Africa, the use of satellite remote sensing data promises an excellent opportunity for achieving this predictive goal. In this article, we assess the effectiveness of Soil Moisture Active Passive (SMAP) and Cyclone Global Navigation Satellite System (CYGNSS) soil moisture data\\n in predicting drought conditions using multiple linear regression???predicted data and Global Land Data Assimilation System (GLDAS) soil moisture data.\",\"PeriodicalId\":211256,\"journal\":{\"name\":\"Photogrammetric Engineering & Remote Sensing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Photogrammetric Engineering & Remote Sensing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14358/pers.23-00075r2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Photogrammetric Engineering & Remote Sensing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14358/pers.23-00075r2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Evaluation of SMAP and CYGNSS Soil Moistures in Drought Prediction Using Multiple Linear Regression and GLDAS Product
Drought is a devastating natural hazard and exerts profound effects on both the environment and society. Predicting drought occurrences is significant in aiding decision-making and implementing effective mitigation strategies. In regions characterized by limited data availability, such
as Southern Africa, the use of satellite remote sensing data promises an excellent opportunity for achieving this predictive goal. In this article, we assess the effectiveness of Soil Moisture Active Passive (SMAP) and Cyclone Global Navigation Satellite System (CYGNSS) soil moisture data
in predicting drought conditions using multiple linear regression???predicted data and Global Land Data Assimilation System (GLDAS) soil moisture data.
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