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Evaluation of Red-Peak Algorithms for Chlorophyll Measurement in the Pearl River Estuary

Fenfen Liu,Shilin Tang

Published 2019 in IEEE Transactions on Geoscience and Remote Sensing

ABSTRACT

An algorithm of the red-peak envelop area (PEA) near 700 nm was evaluated using <italic>in situ</italic> 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 [<inline-formula> <tex-math notation="LaTeX">$R^{2}= 0.74$ </tex-math></inline-formula>, 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 (<inline-formula> <tex-math notation="LaTeX">$R^{2}= 0.7$ </tex-math></inline-formula>) 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 <inline-formula> <tex-math notation="LaTeX">$\text{m}^{-3}$ </tex-math></inline-formula>) were observed in the middle river channel upstream of the Pearl River estuary after strong precipitation events.

PUBLICATION RECORD

  • Publication year

    2019

  • Venue

    IEEE Transactions on Geoscience and Remote Sensing

  • Publication date

    2019-07-12

  • Fields of study

    Computer Science, Environmental Science

  • Identifiers
    DOI 10.1109/TGRS.2019.2923754
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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