IGSNRR OpenIR
Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs
Zhu, Wanxue1,2; Sun, Zhigang1,2,3,4; Huang, Yaohuan2,5; Lai, Jianbin1; Li, Jing1; Zhang, Junqiang6,7; Yang, Bin7; Li, Binbin1; Li, Shiji1,2; Zhu, Kangying1,2; Li, Yang1,2; Liao, Xiaohan2,5,8
2019-10-01
Source PublicationREMOTE SENSING
Volume11Issue:20Pages:22
Corresponding AuthorSun, Zhigang(sun.zhigang@igsnrr.ac.cn)
AbstractLeaf area index (LAI) is a key biophysical parameter for monitoring crop growth status, predicting crop yield, and quantifying crop variability in agronomic applications. Mapping the LAI at the field scale using multispectral cameras onboard unmanned aerial vehicles (UAVs) is a promising precision-agriculture application with specific requirements: The LAI retrieval method should be (1) robust so that crop LAI can be estimated with similar accuracy and (2) easy to use so that it can be applied to the adjustment of field management practices. In this study, three UAV remote-sensing missions (UAVs with Micasense RedEdge-M and Cubert S185 cameras) were carried out over six experimental plots from 2018 to 2019 to investigate the performance of reflectance-based lookup tables (LUTs) and vegetation index (VI)-based LUTs generated from the PROSAIL model for wheat LAI retrieval. The effects of the central wavelengths and bandwidths for the VI calculations on the LAI retrieval were further examined. We found that the VI-LUT strategy was more robust and accurate than the reflectance-LUT strategy. The differences in the LAI retrieval accuracy among the four VI-LUTs were small, although the improved modified chlorophyll absorption ratio index-lookup table (MCARI2-LUT) and normalized difference vegetation index-lookup table (NDVI-LUT) performed slightly better. We also found that both of the central wavelengths and bandwidths of the VIs had effects on the LAI retrieval. The VI-LUTs with optimized central wavelengths (red = 612 nm, near-infrared (NIR) = 756 nm) and narrow bandwidths (similar to 4 nm) improved the wheat LAI retrieval accuracy (R-2 >= 0.75). The results of this study provide an alternative method for retrieving crop LAI, which is robust and easy use for precision-agriculture applications and may be helpful for designing UAV multispectral cameras for agricultural monitoring.
Keywordleaf area index unmanned aerial vehicle vegetation indices multispectral camera hyperspectral camera precision agriculture
DOI10.3390/rs11202456
WOS KeywordLEAF-AREA INDEX ; HYPERSPECTRAL VEGETATION INDEXES ; CANOPY CHLOROPHYLL CONTENT ; RADIATIVE-TRANSFER MODEL ; INVERSION ; PROSAIL ; CORN ; FORESTS ; POTATO ; IMAGES
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[31570472] ; National Natural Science Foundation of China[31870421] ; National Natural Science Foundation of China[41771388] ; Key Projects of the Chinese Academy of Sciences[KFZD-SW-319] ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences[KFJ-STS-ZDTP-049] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19040303] ; National Key Research and Development Program of China[2017YFC0503805]
Funding OrganizationNational Natural Science Foundation of China ; Key Projects of the Chinese Academy of Sciences ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Key Research and Development Program of China
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000498395800124
PublisherMDPI
Citation statistics
Cited Times:12[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/130218
Collection中国科学院地理科学与资源研究所
Corresponding AuthorSun, Zhigang
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Shandong Dongying Inst Geog Sci, Inst Geog Sci & Nat Resources Res, Dongying 257000, Peoples R China
4.Chinese Acad Sci, CAS Engn Lab Yellow River Delta Modern Agr, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
6.Chinese Acad Sci, Changchun Inst Opt Fine Mech & Phys, Changchun 130033, Jilin, Peoples R China
7.Yusense Informat Technol & Equipment Qingdao Ltd, Qingdao 266000, Shandong, Peoples R China
8.Chinese Acad Sci, Res Ctr UAV Applicat & Regulat, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Zhu, Wanxue,Sun, Zhigang,Huang, Yaohuan,et al. Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs[J]. REMOTE SENSING,2019,11(20):22.
APA Zhu, Wanxue.,Sun, Zhigang.,Huang, Yaohuan.,Lai, Jianbin.,Li, Jing.,...&Liao, Xiaohan.(2019).Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs.REMOTE SENSING,11(20),22.
MLA Zhu, Wanxue,et al."Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs".REMOTE SENSING 11.20(2019):22.
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
[Zhu, Wanxue]'s Articles
[Sun, Zhigang]'s Articles
[Huang, Yaohuan]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhu, Wanxue]'s Articles
[Sun, Zhigang]'s Articles
[Huang, Yaohuan]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhu, Wanxue]'s Articles
[Sun, Zhigang]'s Articles
[Huang, Yaohuan]'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.