首页|Remote estimation of phytoplankton primary production in clear to turbid waters by integrating a semi-analytical model with a machine learning algorithm

Remote estimation of phytoplankton primary production in clear to turbid waters by integrating a semi-analytical model with a machine learning algorithm

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  • NSTL
  • Elsevier
Remote estimation of phytoplankton primary production has long been recognized as an important method for investigating the responses of aquatic ecosystems to global climate change. The theory-based primary production model (TPM), one of the earlier proposed models, is potentially applicable to a variety of water bodies because of its semi-analytical nature. Its accuracy is highly dependent on whether the photophysiological response of phytoplankton is adequately parameterized, specifically the assimilation number (P-max(B)) and the light saturation parameter (Ek). The remote assignment of P-max(B) and Ek is acknowledged to be a challenging task, and the limited progress has impeded extensive use of the TPM. In this study, we proposed a machine learning algorithm, the enhanced random forest regression (ERFR), to retrieve P-max(B) and Ek from satellite observations. The ERFR were then integrated with the TPM (together termed as TPMERFR) to estimate daily depth-integrated primary production (IPP) in clear to turbid waters. The ERFR was trained and validated using in situ datasets from a broad range of trophic and biogeographic conditions, covering oceanic, coastal, and inland water bodies. Evaluations with independent in situ data and matchup data showed that the ERFR outperformed conventional empirical and semi-analytical algorithms, and it could better capture the variability of P-max(B) and Ek than look-up-table methods. The root mean square difference (RMSD) of the satellite-based IPP estimates from the TPMERFR remained within 0.27. In contrast, the benchmark models generally yielded IPP estimates with RMSDs of 0.27-0.62. The TPMERFR was then implemented to climatological satellite products (2010-2019) to reassess global IPP. Reasonable spatial distributions of IPP were preliminarily demonstrated, especially in polar, coastal, and inland waters. These results indicated the potential utility of the TPMERFR to generate seamless IPP distributions in clear to turbid waters.

Depth-integrated primary productionPhotosynthetic parametersSemi-analytical modelEnhanced random forest regressionOcean color remote sensingINHERENT OPTICAL-PROPERTIESOCEANIC PRIMARY PRODUCTIONSPECTRAL ABSORPTION-COEFFICIENTSFRACTIONATED PRIMARY PRODUCTIONCHLOROPHYLL-A CONCENTRATIONEAST CHINA SEAPHOTOSYNTHETIC PARAMETERSATMOSPHERIC CORRECTIONSATELLITE CHLOROPHYLLLAKE KASUMIGAURA

Li, Zhaoxin、Yang, Wei、Matsushita, Bunkei、Kondoh, Akihiko

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Chiba Univ

Univ Tsukuba

2022

10.1016/j.rse.2022.113027

Remote Sensing of Environment

Remote Sensing of Environment

EISCI
ISSN：0034-4257
年,卷(期)：2022.275
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