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<p>Phytoplankton primary productivity (PP) is a key indicator of carbon fixation and ecosystem functioning in inland waters. Using high-resolution Sentinel-2 imagery, this study developed a regionalized Vertically Generalized Production Model (VGPM) optimized for highly turbid, shallow lakes to estimate modeled PP dynamics in Taihu Lake from 2019 to 2025. The multi-year mean daily PP in Taihu Lake ranged from 820.68 to 1 527.11 mg C/(m<sup>2</sup>·d), with a lake-wide average of ∼1 232.28 mg C/(m<sup>2</sup>∼d). Spatially, modeled PP decreased from the bays to the central basin and from west to east, with Zhushan and Meiliang bays maintaining the highest productivity (up to ∼1 667.93 mg C/(m<sup>2</sup>·d)). At the same time, the eastern region dominated by aquatic vegetation showed lower values (&lt;950 mg C/(m<sup>2</sup>·d)). Temporally, modeled PP exhibited a strong seasonal cycle, with higher values in summer and autumn and lower values in winter and spring, forming a bimodal pattern with peaks in May and September (&gt;1 800 mg C/(m<sup>2</sup>·d)). Moderate water temperature and sufficient light availability promoted productivity, whereas turbidity (light attenuation) limited phytoplankton growth. By integrating corrections for phytoplankton vertical distribution, algal bloom classification, and the removal of aquatic vegetation signals, the model’s performance was greatly improved. The Google Earth Engine (GEE)-based workflow enabled efficient long-term modeled PP monitoring and provides a transferable framework for quantifying carbon cycling, guiding eutrophic lake restoration, and assessing climate responses.</p>

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Regionalizing the vertically generalized production model (VGPM) for Sentinel-2 estimation of phytoplankton primary productivity in turbid inland waters

  • 期刊论文

  • 发表日期：

    2026年8月1日

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  • Ting Song,
  • Yanjuan Xu,
  • Hujun Zhang,
  • Bingchuan Zhu,
  • Ling Li,
  • Dongyang Cheng,
  • Ge Liu

摘要

Phytoplankton primary productivity (PP) is a key indicator of carbon fixation and ecosystem functioning in inland waters. Using high-resolution Sentinel-2 imagery, this study developed a regionalized Vertically Generalized Production Model (VGPM) optimized for highly turbid, shallow lakes to estimate modeled PP dynamics in Taihu Lake from 2019 to 2025. The multi-year mean daily PP in Taihu Lake ranged from 820.68 to 1 527.11 mg C/(m2·d), with a lake-wide average of ∼1 232.28 mg C/(m2∼d). Spatially, modeled PP decreased from the bays to the central basin and from west to east, with Zhushan and Meiliang bays maintaining the highest productivity (up to ∼1 667.93 mg C/(m2·d)). At the same time, the eastern region dominated by aquatic vegetation showed lower values (<950 mg C/(m2·d)). Temporally, modeled PP exhibited a strong seasonal cycle, with higher values in summer and autumn and lower values in winter and spring, forming a bimodal pattern with peaks in May and September (>1 800 mg C/(m2·d)). Moderate water temperature and sufficient light availability promoted productivity, whereas turbidity (light attenuation) limited phytoplankton growth. By integrating corrections for phytoplankton vertical distribution, algal bloom classification, and the removal of aquatic vegetation signals, the model’s performance was greatly improved. The Google Earth Engine (GEE)-based workflow enabled efficient long-term modeled PP monitoring and provides a transferable framework for quantifying carbon cycling, guiding eutrophic lake restoration, and assessing climate responses.