1. 华东师范大学地理信息科学教育部重点实验室，上海 200241 2. 华东师范大学环境遥感与数据同化联合实验室，上海 200241 3. 华东师范大学、美国科罗拉多州立大学中美新能源与环境联合研究院，上海 200062 4. Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins 80532, USA
Dual NDVI Ratio Vegetation Index: A Kind of Vegetation Index Assessing Leaf Carotenoid Content Based on Leaf Optical Properties Model
WANG Hong1, 2, SHI Run-he1, 2, 3*, LIU Pu-dong1, 2, GAO Wei1, 2, 3, 4
1. Key Laboratory of Geographical Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China 2. Joint Laboratory for Environmental Remote Sensing and Data Assimilation, East China Normal University, Shanghai 200241, China 3. Joint Research Institute for New Energy and the Environment, East China Normal University and Colorado State University, Shanghai 200062, China 4. Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins 80532, USA
Abstract：With characteristics of rapidness, non-destructiveness and high precision in detecting plant leaves, hyperspectral technology is promising in assessing the contents of leaf pigments and other biochemical components. Because the spectral absorption features of carotenoid and chlorophyll are overlapped in visible light region and that foliar carotenoid content is far lower than chlorophyll content, studies about constructing vegetation indices (VIs) for carotenoid is rare at home and abroad though carotenoid is one of the most important photosynthetic pigments. Hyperspectral data has abundant spectral information, so this paper proposed a multiple spectral indices collaborative algorithm to construct VIs on the basis of band-combination traversal and correlation analysis. Through a large number of simulated leaf reflectance spectra under different biochemical components contents run on PROSPECT model, a radiative transfer model, we successfully constructed a new kind of stable vegetation index (VI) for assessing carotenoid content at leaf level: RVIDNDVI. Our results indicate that RVIDNDVI is composed of two parts: (1)Narrow band NDVI constructed with 532 and 405 nm is high correlated with both carotenoid content and chlorophyll content while narrow band NDVI constructed with 548 and 498 nm is highly correlated with carotenoid content. The influence of chlorophyll content on RVIDNDVI can be eliminated with the ratio combination of these two indices. (2) The influence of mesophyll structure parameter can be weakened by subtracting the reflectance at 916 nm, which has strong correlation with mesophyll structure parameter. RVIDNDVIonly has high sensitivity to carotenoid content (the correlation coefficient is -0.94) at leaf level and R2 of its exponential fit is 0.834 4. The estimation of RVIDNDVIto carotenoid content can be verified with the validations of both simulated data and measured data.
Key words：Carotenoid content;Multiple spectral indices collaborative algorithm;Vegetation index;PROSPECT model;RVIDNDVI
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