IGSNRR OpenIR
Retrieval of leaf water content from remotely sensed data using a vegetation index model constructed with shortwave infrared reflectances
Zhang, Ziyang1,2; Tang, Bo-Hui1,2; Li, Zhao-Liang1,2,3
2019
Source PublicationINTERNATIONAL JOURNAL OF REMOTE SENSING
ISSN0143-1161
Volume40Issue:5-6Pages:2313-2323
Corresponding AuthorTang, Bo-Hui(tangbh@igsnrr.ac.cn)
AbstractThe timely and accurate estimation of leaf water content (LWC) is of great practical significance for monitoring the state of vegetation growth and forecasting crop yield. As the spectral capabilities of terrestrial spectroscopy instruments and high-spatial-resolution satellite sensors steadily increase, people pay more and more attentions to the application potential for the spectral characteristics of the plant in shortwave infrared (SWIR) domain. The major purpose of this article was to investigate the relationship between leaf spectral reflectance and LWC in SWIR. A normalized vegetation index model constructed with SWIR reflectances centred at 1725 and 2200nm yielded a high overall performance compared to the previous normalized difference water index (NDWI). The Leaf Optical Properties Experiment 1993 data set was introduced to validate the LWC estimated with the proposed model. The results showed that the accuracy of LWC estimation from this index can reach 0.0046gcm(-2), which is better than that from traditional NDWI with root mean square error equalled to 0.0104gcm(-2). The effects of band width and random noise on the LWC estimation from these two indices were also analysed and the results showed that LWC retrieved from both indices were insensitive to band width choices and random noise variations.
DOI10.1080/01431161.2018.1471553
WOS KeywordSPECTRAL REFLECTANCE ; LIQUID WATER ; THICKNESS ; LIGHT ; AREA
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41571353] ; Innovation Project of LREIS[O88RA801YA]
Funding OrganizationNational Natural Science Foundation of China ; Innovation Project of LREIS
WOS Research AreaRemote Sensing ; Imaging Science & Photographic Technology
WOS SubjectRemote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000464043900043
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/48052
Collection中国科学院地理科学与资源研究所
Corresponding AuthorTang, Bo-Hui
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Key Lab Agr Remote Sensing, Minist Agr, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Ziyang,Tang, Bo-Hui,Li, Zhao-Liang. Retrieval of leaf water content from remotely sensed data using a vegetation index model constructed with shortwave infrared reflectances[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2019,40(5-6):2313-2323.
APA Zhang, Ziyang,Tang, Bo-Hui,&Li, Zhao-Liang.(2019).Retrieval of leaf water content from remotely sensed data using a vegetation index model constructed with shortwave infrared reflectances.INTERNATIONAL JOURNAL OF REMOTE SENSING,40(5-6),2313-2323.
MLA Zhang, Ziyang,et al."Retrieval of leaf water content from remotely sensed data using a vegetation index model constructed with shortwave infrared reflectances".INTERNATIONAL JOURNAL OF REMOTE SENSING 40.5-6(2019):2313-2323.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zhang, Ziyang]'s Articles
[Tang, Bo-Hui]'s Articles
[Li, Zhao-Liang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhang, Ziyang]'s Articles
[Tang, Bo-Hui]'s Articles
[Li, Zhao-Liang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhang, Ziyang]'s Articles
[Tang, Bo-Hui]'s Articles
[Li, Zhao-Liang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.