Using PROSEPCT and SVM for the Estimation of Chlorophyll Concentration

Abstract:

This study focused on estimating chlorophyll concentration of rice using PROSPECT and support vector machine. The study site is located in West Lake sewage irrigation area of Changchun, Jiliin Province. Reflectance spectrual of rice were measured by ASD3 spectrometer, chlorophyll contents of rice were recorded with a portable chlorophyll meter SPAD-502. Support vector machines and PROSPECT model were adopted to construct hyperspectral models for predicting chlorophyll content. The results indicate that: the hyperspectral prediction model of rice chlorophyll content yields a maximum correlation coefficient of 0.8563, and achieves a smallest RMSE of 9.5106; and the prediction accuracy based on the first derivative spectrum is higher than on the original spectrum. Research of this paper provides a theoretical basis for large scale dynamic prediction of rice chlorophyll content in sewage irrigated area.

• Periodical: Advanced Materials Research (Volumes 989-994)
• Pages: 2184-2187
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.989-994.2184
• Online since: July 2014
• Authors: Jie Lv*, Feng Li Deng, Zhen Guo Yan
• Keywords: Chlorophyll Concentration, Estimation, Hyperspectral Remote Sensing, Rice, Support Vector Machine (SVM)

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