光谱学与光谱分析 |
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Inversion of LAI by Considering the Hotspot Effect for Different Geometrical Wheat |
ZHAO Juan1, 2, ZHANG Yao-hong1, HUANG Wen-jiang2*, JING Yuan-shu1, PENG Dai-liang2, WANG Li3, SONG Xiao-yu4 |
1. School of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China 2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China 3. Key Laboratory of Digital Earth Science, Chinese Academy of Sciences, Beijing 100094, China 4. Beijing Agriculture Information Technology Research Center, Beijing 100097, China |
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Abstract Aimed to deal with the limitation of canopy geometry to crop LAI inversion accuracy a new LAI inversion method for different geometrical winter wheat was proposed based on hotspot indices with field-measured experimental data. The present paper analyzed bidirectional reflectance characteristics of erective and loose varieties at red (680 nm) and NIR wavelengths(800 nm and 860 nm) and developed modified normalized difference between hotspot and dark-spot (MNDHD) and hotspot and dark-spot ratio index(HDRI) using hotspot and dark-spot index(HDS) and normalized difference between hotspot and dark-spot(NDHD) for reference. Combined indices were proposed in the form of the product between HDS, NDHD, MNDHD, HDRI and three ordinary vegetation indices NDVI, SR and EVI to inverse LAI for erective and loose wheat. The analysis results showed that LAI inversion accuracy of erective wheat Jing411 were 0.943 1 and 0.909 2 retrieved from the combined indices between NDVI and MNDHD and HDRI at 860 nm which were better than that of HDS and NDHD, the LAI inversion accuracy of loose wheat Zhongyou9507 were 0.964 8 and 0.895 6 retrieved from the combined indices between SR and HDRI and MNDHD at 800 nm which were also higher than that of HDS and NDHD. It was finally concluded that the combined indices between hotspot-signature indices and ordinary vegetation indices were feasible enough to inverse LAI for different crop geometrical wheat and multi-angle remote sensing data was much more advantageous than perpendicular observation data to extract crop structural parameters.
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Received: 2013-04-19
Accepted: 2013-06-28
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Corresponding Authors:
HUANG Wen-jiang
E-mail: yellowstar0618@163.com
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