Article(id=1242116806857523471, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242116066525118632, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1242057600000, receivedDateStr=2009-05-12, revisedDate=1240502400000, revisedDateStr=2009-04-24, acceptedDate=null, acceptedDateStr=null, onlineDate=1246118400000, onlineDateStr=2009-06-28, pubDate=1246118400000, pubDateStr=2009-06-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1246118400000, onlineIssueDateStr=2009-06-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774073324165, creator=sys-migrate, updateTime=1774073324165, updator=sys-migrate, issue=Issue{id=1242116066525118632, tenantId=1146029695717560320, journalId=1146031591421210625, year='2009', volume='27', issue='0912', pageStart='3', pageEnd='126', issueExtLink='null', onlineDate='null', pubDate='1246118400000', pubDateStr='2009-06-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774073147655, creator='sys-migrate', updateTime=1774073147655, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=85, endPage=92, ext={EN=ArticleExt(id=1242116810523345192, articleId=1242116806857523471, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Research on Soil Moisture Remote Sensing Monitoring Based on EOS/MODIS Data, columnId=1242116810380743325, journalTitle=Science & Technology Review, columnName=Reviews, runingTitle=null, highlight=null, articleAbstract=Since the plan of EOS was carried out in 1991, the sensor of EOS/MODIS, with high temporal resolution, high spectrum resolution and normalized spatial resolution, has shown its advantages in the drought remote sensing monitoring. The studies on the soil moisture remote sensing monitoring based on EOS/MODIS data are reviewed in the following 5 aspects: ① The models based on the Vegetation Index, such as Simple Vegetation Index, Ratio Vegetation Index, Normalized Difference Vegetation Index, Enhanced Vegetation Index, Normalized Difference Water Index, Vegetation Condition Index, Average Vegetation Index etc. ② The models based on the infrared spectrum, such as Perpendicular Drought Index, Modified Perpendicular Drought Index, etc. ③ The models based on the Land Surface Temperature, such as Appearance Thermal Initial, Temperature Condition Index, Normalized Difference Temperature Index and Appearance Vegetation Drought Index, etc. ④ The models based on the combination of Vegetation Index and Land Surface Temperature. Such as Vegetation-Temperature Condition Index, Vegetation Index/Temperature Trapezoid, Temperature Vegetation Dryness Index, Crop Water Stress Index, etc. ⑤ The models based on the NDVI and soil background. Such as Surface Water Capacity Index, Modified Surface Water Capacity Index, etc. This paper focuses on the models with obvious physical meaning and with wide applications in practical work, such as Modified Perpendicular Drought Index (MPDI), Crop Water Stress Index (CWSI), Surface Water Capacity Index (SWCI) and Appearance Thermal Initial (ATI). The research directions of agricultural drought monitoring based on EOS/MODIS and FY-3 data are discussed. The cycle observation of "Soil-Crop-Atmosphere" and Multi-knowledge syntheses may achieve quantified monitoring. Some future research topics of remote sensing monitoring in soil moisture are proposed., authors=null, authorsList=null, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=o88b/+uqGx8rAcHQIzwHHA==, pdfFileSize=606755, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1242116809403466016, articleId=1242116806857523471, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=基于EOS/MODIS数据的土壤水分遥感, columnId=1242116809164390686, journalTitle=科技导报, columnName=综述文章, runingTitle=null, highlight=null, articleAbstract=美国国家航空航天局（NASA）自1991年开始实施对地观测系统（EOS）计划以来，EOS/MODIS传感器的高时间分辨率、高光谱分辨率、适中的空间分辨率等特点使其在干旱监测中具有突出的优势。纵观近年来国内外基于EOS/MODIS资料遥感监测土壤水分的理论、方法的发展和应用特点，可以分为5类：① 基于植被指数类的遥感干旱监测方法，如简单植被指数、比值植被指数、归一化植被指数、增强植被指数、归一化水分指数法、距平植被指数等；② 基于红外的遥感干旱监测方法，如垂直干旱指数法、修正的垂直干旱指数法等；③ 基于地表温度（LST）的遥感干旱监测方法，如热惯量法、条件温度指数、归一化差值温度指数、表观热惯量植被干旱指数等；④ 基于植被指数和温度的遥感干旱监测方法，如条件植被温度指数、植被温度梯形指数、温度植被干旱指数模型等；⑤ 基于植被与土壤的遥感监测方法，如地表含水量指数作物缺水指数法等。重点介绍了目前生物、物理意义较明显，比较成熟和广泛应用的基于可见光与热红外波段的改进型垂直干旱指数法（MPDI）、作物缺水指数法（CWSI）、地表含水量指数法（SWCI）、热惯量法（ATI）等；对各种遥感监测土壤水分方法的优缺点做出评价；指出利用高分辨率卫星资料（如EOS/MODIS和中国发射的风云三号极轨气象卫星250 m分辨率资料）进行农业干旱监测研究将成为主要的研究方向，“土壤-作物-大气”多圈层立体监测、多学科的融合将是解决提高旱情定量化监测精度、使之实用化和业务化的必由之路；最后，展望了遥感监测土壤水分的发展前景。, authors=张红卫^1,2,3,陈怀亮¹,申双和³, authorsList=张红卫;;陈怀亮;申双和, authorCompany=1. 河南省气象科学研究所2. 新乡市气象局3. 南京信息工程大学应用气象学院, correspAuthors=null, authorNote=null, correspAuthorsNote=张红卫, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=MfxjxsqDj4p1ghLb/pMv7w==, 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