Visible-near infrared reflection spectroscopy has the advantages of fast and green, and has great technical advantages in the field detection of soil components, but no study on organic carbon (OC) in marine beach sediments has been found. According to the OC content and visible - near infrared reflection spectrum of sediment samples from Jiaozhou Bay beach in Qingdao, the spectral preprocessing was carried out by S-G derivative filter, and the sediment samples were divided by Kennard-Stone algorithm. On the basis of the whole band, the prediction model of sediment OC is established by using the least square support vector machine (LSSVM) algorithm. According to the evaluation results of the model, the modeling set R2=0.97, the prediction set R2=0.83, and the relative analysis error RPD=2.46, shows that the model has a good prediction effect.
The rapid prediction of carbon content by spectral method is helpful to grasp the carbon dynamic of sediments in intertidal zone in a timely manner. The spectral reflectivity of sediments with high carbon content and low carbon content often varies widely, limiting the accuracy of the model. When sediment samples are classified according to the carbon content and modeled separately, accurate prediction of sediment carbon is expected to be achieved. However, previous studies have not reported it. In our study, sediment samples were divided into low-carbon and high-carbon sample sets according to 3 g/kg carbon content, and divided into low-carbon, medium-carbon and high-carbon sample sets according to 2 g/kg and 4 g/kg carbon content, and then were pre-processed and PLS modeled separately. It is found that these classification can improve the modeling accuracy, but not improve the prediction accuracy. These results can provide a technical reference for the prediction of sediment carbon in intertidal zone.
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