Satellite mapping reveals phytoplankton biomass's spatio-temporal dynamics and responses to environmental factors in a eutrophic inland lake

Lai Lai; Yuchao Zhang; Tao Han; Min Zhang; Zhen Cao; Zhaomin Liu; Qiduo Yang; Xi Chen

doi:10.1016/j.jenvman.2024.121134

Satellite mapping reveals phytoplankton biomass's spatio-temporal dynamics and responses to environmental factors in a eutrophic inland lake

J Environ Manage. 2024 Jun:360:121134. doi: 10.1016/j.jenvman.2024.121134. Epub 2024 May 14.

Authors

Lai Lai¹, Yuchao Zhang², Tao Han¹, Min Zhang¹, Zhen Cao¹, Zhaomin Liu¹, Qiduo Yang¹, Xi Chen³

Affiliations

¹ Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
² Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China; University of Chinese Academy of Sciences, Beijing, 100049, China. Electronic address: yczhang@niglas.ac.cn.
³ Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China; Nanjing University of Information Science and Technology, Nanjing, 210044, China.

PMID: 38749137
DOI: 10.1016/j.jenvman.2024.121134

Abstract

Chlorophyll a (Chla) concentration can be used as an indicator of algal biomass, and the accumulation of algal biomass in water column is essential for the emergence of surface blooms. By using Moderate Resolution Imaging Spectrometer (MODIS) data, a machine learning algorithm was previously developed to assess algal biomass within the euphotic depth (B_eu). Here, a long-term B_eu dataset of Lake Taihu from 2003 to 2020 was generated to examine its spatio-temporal dynamics, sensitivity to environmental factors, and variations in comparison to the surface algal bloom area. During this period, the daily B_eu (total B_eu within the whole lake) exhibited temporal fluctuations between 40 and 90 t Chla, with an annual average of 63.32 ± 5.23 t Chla. Notably, it reached its highest levels in 2007 (72.34 t Chla) and 2017 (73.57 t Chla). Moreover, it demonstrated a clear increasing trend of 0.197 t Chla/y from 2003 to 2007, followed by a slight decrease of 0.247 t Chla/y after 2017. Seasonal variation showed a bimodal annual cycle, characterized by a minor peak in March ∼ April and a major peak in July ∼ September. Spatially, the average pixel-based B_eu (total B_eu of a unit water column) ranged from 21.17 to 49.85 mg Chla, with high values predominantly distributed in the northwest region and low values in the central region. The sensitivity of B_eu to environmental factors varies depending on regions and time scales. Temperature has a significant impact on monthly variation (65.73%), while the level of nutrient concentrations influences annual variation (55.06%). Wind speed, temperature, and hydrodynamic conditions collectively influence the spatial distribution of B_eu throughout the entire lake. Algal bloom biomass can capture trend changes in two mutant years as well as bimodal phenological changes compared to surface algal bloom area. This study can provide a basis for scientific evaluation of water environment and a reference for monitoring algal biomass in other similar eutrophic lakes.

Keywords: Algal biomass; Eutrophic lakes; MODIS; Remote sensing; Spatio-temporal dynamics.

MeSH terms

Biomass*
Chlorophyll A / analysis
Environmental Monitoring / methods
Eutrophication*
Lakes*
Phytoplankton*
Satellite Imagery
Seasons

Substances

Chlorophyll A