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

Ting SONG; Yanjuan XU; Hujun ZHANG; Bingchuan ZHU; Ling LI; Dongyang CHENG; Ge LIU

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

Updated：2026-07-29

- Regionalizing the vertically generalized production model (VGPM) for Sentinel-2 estimation of phytoplankton primary productivity in turbid inland waters
- Journal of Oceanology and Limnology Pages: 1-24(2026)
- Affiliations：
  
  1.Jiangsu Wuxi Environmental Monitoring Center, Wuxi 214121, China
  2.State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
  3.Suzhou Chenyang Hengrui Information Technology Co., Ltd., Suzhou 215000, China
- Author bio：
  
  liuge@iga.ac.cn
- Funds：
- DOI：
  CLC：
- Received：02 December 2025，
  
  Online First：29 July 2026，
- Accepted：
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SONG Ting,XU Yanjuan,ZHANG Hujun,et al.Regionalizing the vertically generalized production model (VGPM) for Sentinel-2 estimation of phytoplankton primary productivity in turbid inland waters[J].Journal of Oceanology and Limnology,
DOI：

SONG Ting,XU Yanjuan,ZHANG Hujun,et al.Regionalizing the vertically generalized production model (VGPM) for Sentinel-2 estimation of phytoplankton primary productivity in turbid inland waters[J].Journal of Oceanology and Limnology, DOI：.

摘要

Abstract

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/(m

·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.

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references

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Department of Zoology, University of Otago, PO Box 56, Dunedin 9054, New Zealand

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