一种新的光谱面积指数法反演冠层含水量

Xiao-po Zheng, H. Ren, Q. Qin, Lingjing Wu, Zhongling Gao, Yuejun Sun, Jianhua Wang, Xin Ye
{"title":"一种新的光谱面积指数法反演冠层含水量","authors":"Xiao-po Zheng, H. Ren, Q. Qin, Lingjing Wu, Zhongling Gao, Yuejun Sun, Jianhua Wang, Xin Ye","doi":"10.1109/IGARSS.2015.7326532","DOIUrl":null,"url":null,"abstract":"Canopy water content (CWC) is one of the most important biochemical properties of plants, which can be estimated from remote sensing data conveniently by using vegetation water indices. This paper started from the analysis of some existing indices and then proposed two novel indices to estimate CWC. First, the area under part of near infrared and shortwave infrared reflectance curve were calculated. Then two indices, Area-based Normalized Index (ABNI) and Area-Based Ratio Index (ABRI) were developed by using ratio method and normalization method, respectively. From the validation results, the new indices were found to exponentially correlate with CWC more significantly than some classical indices, and the determination coefficient (R2) and root mean square error (RMSE) of the new method were 0.89 and 0.04, which indicated that the novel indices provided a promising way to monitor CWC.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Retrieval of canopy water content using a new spectral area index method\",\"authors\":\"Xiao-po Zheng, H. Ren, Q. Qin, Lingjing Wu, Zhongling Gao, Yuejun Sun, Jianhua Wang, Xin Ye\",\"doi\":\"10.1109/IGARSS.2015.7326532\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Canopy water content (CWC) is one of the most important biochemical properties of plants, which can be estimated from remote sensing data conveniently by using vegetation water indices. This paper started from the analysis of some existing indices and then proposed two novel indices to estimate CWC. First, the area under part of near infrared and shortwave infrared reflectance curve were calculated. Then two indices, Area-based Normalized Index (ABNI) and Area-Based Ratio Index (ABRI) were developed by using ratio method and normalization method, respectively. From the validation results, the new indices were found to exponentially correlate with CWC more significantly than some classical indices, and the determination coefficient (R2) and root mean square error (RMSE) of the new method were 0.89 and 0.04, which indicated that the novel indices provided a promising way to monitor CWC.\",\"PeriodicalId\":125717,\"journal\":{\"name\":\"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IGARSS.2015.7326532\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IGARSS.2015.7326532","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

冠层含水量(CWC)是植物最重要的生化特性之一,利用植被水分指数可以方便地从遥感数据中估算出冠层含水量。本文从分析现有的一些指标入手,提出了两个新的CWC评价指标。首先,计算了近红外和短波红外部分反射曲线下的面积;然后分别采用比率法和归一化法建立了基于面积的归一化指数(ABNI)和基于面积的比率指数(ABRI)。验证结果表明,新指标与CWC的指数相关性高于一些经典指标,新方法的决定系数(R2)和均方根误差(RMSE)分别为0.89和0.04,为CWC的监测提供了一种有前景的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Retrieval of canopy water content using a new spectral area index method
Canopy water content (CWC) is one of the most important biochemical properties of plants, which can be estimated from remote sensing data conveniently by using vegetation water indices. This paper started from the analysis of some existing indices and then proposed two novel indices to estimate CWC. First, the area under part of near infrared and shortwave infrared reflectance curve were calculated. Then two indices, Area-based Normalized Index (ABNI) and Area-Based Ratio Index (ABRI) were developed by using ratio method and normalization method, respectively. From the validation results, the new indices were found to exponentially correlate with CWC more significantly than some classical indices, and the determination coefficient (R2) and root mean square error (RMSE) of the new method were 0.89 and 0.04, which indicated that the novel indices provided a promising way to monitor CWC.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Interferometric and polarimetric methods to determine SWE, fresh snow depth and the anisotropy of dry snow Usefulness assessment of polarimetric parameters for line extraction from agricultural areas DEM and DHM reconstruction in tropical forests: Tomographic results at P-band with three flight tracks Nationwide ground deformation monitoring by persistent scatterer interferometry MICAP (Microwave imager combined active and passive): A new instrument for Chinese ocean salinity satellite
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1