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Proceedings Paper

Analysis of optimal narrow band RVI for estimating foliar photosynthetic pigments based on PROSPECT model
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Paper Abstract

Remote sensing is an effective tool to estimate foliar pigments contents with the analysis of vegetation index. The crucial issue is how to choose the optimal bands-combination to conduct the vegetation index. In this study, RVI, a vegetation index computed by the reflectance of Red and NIR bands, has been used to estimate the contents of chlorophyll and carotenoid. The reflectance of the two bands forming the narrow band RVI was simulated by the PROSPECT model. The possible combinations of narrow band RVI were examined from 400 nm to 800 nm. The results showed that: At the leaf level, estimation of chlorophyll content can be identified in narrow band RVI. Ranges for these bands included: (1) 549-589nm, 616-636nm or 729-735nm combined with 434-454nm; (2) 663-688nm, 710-717nm, 719-728nm or 730- 739nm combined with 549-561nm; (3) 663-688nm combined with 569-615nm. However, no valid narrow-band RVI for the estimation of carotenoid content was successfully identified. Our results also showed that two rules should be followed when choosing optimal bands-combination: (1) the selected bands must have minimal interference from other biochemical constituents; (2) there should be distinct differences between the sensitivities of the bands selected for particular pigments.

Paper Details

Date Published: 8 October 2014
PDF: 10 pages
Proc. SPIE 9221, Remote Sensing and Modeling of Ecosystems for Sustainability XI, 922110 (8 October 2014); doi: 10.1117/12.2061281
Show Author Affiliations
Hong Wang, East China Normal Univ. (China)
Joint Lab. for Environmental Remote Sensing and Data Assimilation, ECNU and CEODE (China)
Runhe Shi, East China Normal Univ. (China)
Joint Lab. for Environmental Remote Sensing and Data Assimilation, ECNU and CEODE (China)
Pudong Liu, East China Normal Univ. (China)
Joint Lab. for Environmental Remote Sensing and Data Assimilation, ECNU and CEODE (China)
Mingliang Ma, East China Normal Univ. (China)
Joint Lab. for Environmental Remote Sensing and Data Assimilation, ECNU and CEODE (China)
Wei Gao, East China Normal Univ. (China)
Joint Lab. for Environmental Remote Sensing and Data Assimilation, ECNU and CEODE (China)
Colorado State Univ. (United States)


Published in SPIE Proceedings Vol. 9221:
Remote Sensing and Modeling of Ecosystems for Sustainability XI
Wei Gao; Ni-Bin Chang; Jinnian Wang, Editor(s)

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