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Prediction of Water Chlorophyll-a Content Based on Multi-scale Spectral Analysis
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: Paper number 131620105, 2013 Kansas City, Missouri, July 21 - July 24, 2013. (doi: http://dx.doi.org/10.13031/aim.20131620105) @2013
Authors: Yao Zhang, Lihua Zheng, Minzan Li, Hong Sun, Qin Zhang
Keywords: Water Body Multiscale Spectral Analysis Support Vector Machine Correlation coefficient analysis
Abstract. In this research, samples were collected from JiangSu Province of China. The visible and near infrared spectral reflectance of the water samples were measured. At the same time, the Chlorophyll-a content of water for each sample was measured in the laboratory. Then the spectral characteristics of the water samples were analyzed and the results showed that with chlorophyll-a concentration increasing, spectral transmittance decreased gradually. There was an apparent transmission valley at 676nm. Then multiscale spectral analysis aimed at predicting the chlorophyll-a content in water were conducted, which were consisted of all-wavebands prediction and specific sensitive wavebands prediction. For all-wavebands spectral analysis, this research adopted the SVM technology to establish the regression model based on the all-bands transmittance between 331~900nm. For the specific sensitive wavebands prediction, spectral correlation coefficient analysis was carried out to analyze the sensitive transmittance band of Chlorophyll-a in water. Comprehensive observation on the correlation coefficient curve, the significant correlation could be seen at around 365nm, 550nm and 856nm. It implies that water chlorophyll-a concentration linear forecasting model can be established by the sensitive wavebands selected above. After comparing, the results indicated that, 1) the accuracy of all-wavebands regression model reached to a quite high level, because there is no losing information from the spectra. 2) the sensitive wavebands regression model could also predict the chlorophyll-a content in water practically, and the model was more suitable for embedding in the detector and applying to the agricultural production.
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