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Article

Joint Retrieval of Winter Wheat Leaf Area Index and Canopy Chlorophyll Density Using Hyperspectral Vegetation Indices

by 1,2,3, 2,4,*, 2,4, 2,3 and 5
1
China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
2
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
3
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China
4
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
5
Faculty of Science, University of Technology Sydney, Sydney, NSW 2007, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Kuniaki Uto, Nicola Falco and Mauro Dalla Mura
Remote Sens. 2021, 13(16), 3175; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163175
Received: 14 June 2021 / Revised: 4 August 2021 / Accepted: 6 August 2021 / Published: 11 August 2021
(This article belongs to the Special Issue Remote Sensing for Precision Agriculture)
Leaf area index (LAI) and canopy chlorophyll density (CCD) are key biophysical and biochemical parameters utilized in winter wheat growth monitoring. In this study, we would like to exploit the advantages of three canonical types of spectral vegetation indices: indices sensitive to LAI, indices sensitive to chlorophyll content, and indices suitable for both parameters. In addition, two methods for joint retrieval were proposed. The first method is to develop integration-based indices incorporating LAI-sensitive and CCD-sensitive indices. The second method is to create a transformed triangular vegetation index (TTVI2) based on the spectral and physiological characteristics of the parameters. PROSAIL, as a typical radiative transfer model embedded with physical laws, was used to build estimation models between the indices and the relevant parameters. Validation was conducted against a field-measured hyperspectral dataset for four distinct growth stages and pooled data. The results indicate that: (1) the performance of the integrated indices from the first method are various because of the component indices; (2) TTVI2 is an excellent predictor for joint retrieval, with the highest R2 values of 0.76 and 0.59, the RMSE of 0.93 m2/m2 and 104.66 μg/cm2, and the RRMSE (Relative RMSE) of 12.76% and 16.96% for LAI and CCD, respectively. View Full-Text
Keywords: hyperspectral vegetation index; leaf area index; canopy chlorophyll density; joint retrieval hyperspectral vegetation index; leaf area index; canopy chlorophyll density; joint retrieval
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MDPI and ACS Style

Xing, N.; Huang, W.; Ye, H.; Ren, Y.; Xie, Q. Joint Retrieval of Winter Wheat Leaf Area Index and Canopy Chlorophyll Density Using Hyperspectral Vegetation Indices. Remote Sens. 2021, 13, 3175. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163175

AMA Style

Xing N, Huang W, Ye H, Ren Y, Xie Q. Joint Retrieval of Winter Wheat Leaf Area Index and Canopy Chlorophyll Density Using Hyperspectral Vegetation Indices. Remote Sensing. 2021; 13(16):3175. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163175

Chicago/Turabian Style

Xing, Naichen, Wenjiang Huang, Huichun Ye, Yu Ren, and Qiaoyun Xie. 2021. "Joint Retrieval of Winter Wheat Leaf Area Index and Canopy Chlorophyll Density Using Hyperspectral Vegetation Indices" Remote Sensing 13, no. 16: 3175. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163175

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