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An algorithm of the red-peak envelop area (PEA) near 700 nm was evaluated using in situ data during nine cruises in the Pearl River estuary and compared with other algorithms using the reflectance peak (RP) near 700 nm, including the fluorescence line height (FLH), maximum chlorophyll index (MCI), and MCI2 algorithms. Of all algorithms, the PEA algorithm presented the most accurate performance [ $R^{2}= 0.74$ , root-mean-square error RMSE = 0.12] and provided a more rational spatial distribution of phytoplankton blooms when both Sentinel 3 Ocean and Land Color Instrument (OLCI) and Hyperion data were used because the PEA integrates information from both the moving peak and the asymmetric curve on each side of the peak due to the high correlation relationship ($R^{2}= 0.7$) of chlorophyll and the ratio of the peak area between the left and right halves. Moreover, compared with other algorithms, the PEA algorithm developed using the Hyperion (higher spectral resolution) and OLCI band settings presented similar retrieval accuracies. These results demonstrated that the PEA algorithm is less dependent on the band settings, and the spectral band settings of OLCI from 650 to 750 nm are reasonable and can be used to detect phytoplankton blooms if the PEA algorithm is applied. The OLCI PEA algorithm was applied to determine the variations in phytoplankton blooms under the influences of strong precipitation events. The most obvious increases in chlorophyll concentration (from 20 to 30 mg $\text{m}^{-3}$) were observed in the middle river channel upstream of the Pearl River estuary after strong precipitation events. [ABSTRACT FROM AUTHOR]

Ocean remote sensing is the only technique capable of quantitatively detecting long-term and global variability of marine primary production. However, because the spectral curves of light derived from satellite sensors have a limited capability to identify the physiological state of phytoplankton, the direct estimation of photoadaptive variables, such as daily maximum carbon fixation in a water column (P{opt^{b}}), from the spectra of satellite sensors is difficult. This difficulty results in most of the errors in ocean color-based primary productivity models. In this paper, algorithms were developed to estimate $P_{{{\rm opt}}}^{{\rm b}}$ using statistical models known as support vector machines (SVM). The algorithms were run using three different inputs: sea surface temperature (SST); both SST and sea surface chlorophyll a concentration; and SST, sea surface chlorophyll a concentration and photosynthetically active radiation (PAR) together. The results indicate that the algorithm using the three inputs (SST, sea surface chlorophyll a concentration, and PAR) had the best performance. The algorithms based on the SVM had better results than the general statistical regression method. Using the new $P_{{{\rm opt}}}^{{\rm b}}$ algorithm, the performances of the previous primary production models, including the vertically generalized production model and the statistical model developed in our earlier research, were improved. In contrast to the previous algorithm, the global primary productivity estimated using the new $P_{{{\rm opt}}}^{{\rm b}}$ algorithm showed regional changes, with higher primary productivity in most open ocean areas and lower primary productivity in coastal waters. [ABSTRACT FROM PUBLISHER]