8 November 2014 Inversion of a radiative transfer model for estimation of rice chlorophyll content using support vector machine
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Accurate retrieval of crop chlorophyll content is of great importance for crop growth monitoring, crop stress situations, and the crop yield estimation. This study focused on retrieval of rice chlorophyll content from data through radiative transfer model inversion. A field campaign was carried out in September 2009 in the farmland of ChangChun, Jinlin province, China. A different set of 10 sites of the same species were used in 2009 for validation of methodologies. Reflectance of rice was collected using ASD field spectrometer for the solar reflective wavelengths (350-2500 nm), chlorophyll content of rice was measured by SPAD-502 chlorophyll meter. Each sample sites was recorded with a Global Position System (GPS).Firstly, the PROSPECT radiative transfer model was inverted using support vector machine in order to link rice spectrum and the corresponding chlorophyll content. Secondly, genetic algorithms were adopted to select parameters of support vector machine, then support vector machine was trained the training data set, in order to establish leaf chlorophyll content estimation model. Thirdly, a validation data set was established based on hyperspectral data, and the leaf chlorophyll content estimation model was applied to the validation data set to estimate leaf chlorophyll content of rice in the research area. Finally, the outcome of the inversion was evaluated using the calculated R2 and RMSE values with the field measurements. The results of the study highlight the significance of support vector machine in estimating leaf chlorophyll content of rice. Future research will concentrated on the view of the definition of satellite images and the selection of the best measurement configuration for accurate estimation of rice characteristics.
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Jie Lv, Jie Lv, Zhenguo Yan, Zhenguo Yan, Jingyi Wei, Jingyi Wei, "Inversion of a radiative transfer model for estimation of rice chlorophyll content using support vector machine", Proc. SPIE 9260, Land Surface Remote Sensing II, 926006 (8 November 2014); doi: 10.1117/12.2068874; https://doi.org/10.1117/12.2068874

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