Retrieving Phytoplankton Size Class from the Absorption Coefficient and Chlorophyll A Concentration Based on Support Vector Machine

Abstract

The phytoplankton size class (PSC) plays an important role in biogeochemical processes in the ocean. In this study, a regional model of PSCs is proposed to retrieve vertical PSCs from the total minus water absorption coefficient (at-w(λ)) and Chlorophyll a concentration (Chla). The PSC model is developed by first reconstructing phytoplankton absorption and Chla from at-w(λ), and then extracting PSC from them using the support vector machine (SVM). In situ bio-optical data collected in the South China Sea from 2006 to 2013 were used to train the SVM. The proposed PSC model was subsequently validated using an independent PSC dataset from the Northeast South China Sea Cruise in 2015. The results indicate that the PSC model performed better than the three components model, with a value of r2 between 0.35 and 0.66, and the absolute percentage difference between 56% and 181%. On the whole, our PSC model shows a remarkable utility in terms of inferring vertical PSCs from the South China Sea.

Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms

“…However, more suitable algorithms were found and kernel-based extreme learning machine (KELM) generally performed the best (49 and 57 out of 100 repetitions for reflectance and first derivative spectra, respectively) for estimating the chlorophyll content when assessed using the ratio of performance to deviation (RPD) values. Although SVM's robustness has been reported in some studies [59][60][61], and it performed best in 20 and 37 of the repeats, it also showed the worst performance in 28 and 33 repetitions for the reflectance and first derivative spectra, respectively. These results strongly suggest that SVM is not a stable method.…”

Changes in Nutrient Concentrations in Shenzhen Bay Detected Using Landsat Imagery between 1988 and 2020

“…SVM theory is an approximation of the principle of structural risk minimization that involves the same processing complexity for high-and low-dimensional samples. Kernel functions are introduced to realize nonlinear mapping, thereby perfectly solving nonlinear processing problems and ensuring that SVMs are able to produce relatively good results when trained on a small number of samples [31]. The SVM model has been proven more suitable for modeling small sample data in previous research [13,31].…”

“…Kernel functions are introduced to realize nonlinear mapping, thereby perfectly solving nonlinear processing problems and ensuring that SVMs are able to produce relatively good results when trained on a small number of samples [31]. The SVM model has been proven more suitable for modeling small sample data in previous research [13,31]. In this paper, the optimum models for retrieving nutrient concentrations were determined by comparing model results from each ML method against empirical/in situ measurement data.…”

“…The data were divided into a training set (80%) and a validation set (20%). Because the amount of data matched by OLI was small, the SVM method that was more suitable for small dataset modeling was given priority [31]. The SVM model was trained on the Landsat-8 OLI data.…”

Retrieval of Chlorophyll-a Concentrations in the Coastal Waters of the Beibu Gulf in Guangxi Using a Gradient-Boosting Decision Tree Model

“…This method uses internal implicit networks and structures to determine the complex characteristics of input data and obtain explicit relationships among the output variables [12,13]. Several approaches, including the decision tree [12,14], BP neural network [15,16], support vector machine model (SVM) [17,18], and extreme machine learning approaches [19], have strong adaptability, fault tolerance, and organization of data.…”