Advanced

Eutrophication changes in fifty large lakes on the Yangtze Plain of China derived from MERIS and OLCI observations

Guan, Qi ; Feng, Lian ; Hou, Xuejiao ; Schurgers, Guy LU ; Zheng, Yi and Tang, Jing LU (2020) In Remote Sensing of Environment 246.
Abstract

The eutrophication problems in lakes on the Yangtze Plain of China have attracted global concern. However, a comprehensive assessment of the eutrophication status and its evolution is still lacking for these regional lakes, mostly because of technical difficulties and/or insufficient data to cover the large region. Our study attempts to fill this knowledge gap by using the entire archive of remote sensing images from two satellite ocean color missions (MEdium Resolution Imaging Spectrometer, or MERIS (2003−2011), and Ocean and Land Color Instrument, or OLCI (2017–2018)), together with in situ data on remote sensing reflectance and chlorophyll-a (Chla) concentrations across various lakes on the Yangtze Plain. A machine learning-based... (More)

The eutrophication problems in lakes on the Yangtze Plain of China have attracted global concern. However, a comprehensive assessment of the eutrophication status and its evolution is still lacking for these regional lakes, mostly because of technical difficulties and/or insufficient data to cover the large region. Our study attempts to fill this knowledge gap by using the entire archive of remote sensing images from two satellite ocean color missions (MEdium Resolution Imaging Spectrometer, or MERIS (2003−2011), and Ocean and Land Color Instrument, or OLCI (2017–2018)), together with in situ data on remote sensing reflectance and chlorophyll-a (Chla) concentrations across various lakes on the Yangtze Plain. A machine learning-based piecewise Chla algorithm was developed in this study, with special considerations to improve algorithm performance under lower Chla conditions. Remotely sensed Chla and algal bloom areas were then used to classify the eutrophication status of 50 large lakes on the Yangtze Plain, and the frequent satellite observations enabled us to estimate the probability of eutrophication occurrence (PEO) for each examined lake. The long-term mean Chla ranged from 17.58 mg m−3 to 43.86 mg m−3 on the Yangtze Plain, and severe floating algal blooms were found in 7 lakes. All 50 lakes had high climatological PEO values (50%) during the study period, indicating a generally high probability of eutrophication in lakes on the Yangtze Plain. However, 21 out of 51 lakes exhibited statistically significant (p < .05) decreasing trends in PEO during the observation period, suggesting an overall improvement in the water quality of lakes on the Yangtze Plain in recent years. The methods developed here are expected to contribute to real-time monitoring of drinking water safety for local regions, and the long-term results provide valuable baseline information for future lake conservation and restoration efforts.

(Less)
Please use this url to cite or link to this publication:
author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Algal bloom, Chlorophyll-a, Eutrophication, Lakes, MERIS, OLCI, Water quality, Yangtze Plain
in
Remote Sensing of Environment
volume
246
article number
111890
publisher
Elsevier
external identifiers
  • scopus:85084957944
ISSN
0034-4257
DOI
10.1016/j.rse.2020.111890
language
English
LU publication?
yes
id
0c142946-688c-48a7-b4bc-8f5fd7ef54e2
date added to LUP
2020-06-08 15:21:21
date last changed
2020-12-29 03:48:48
@article{0c142946-688c-48a7-b4bc-8f5fd7ef54e2,
  abstract     = {<p>The eutrophication problems in lakes on the Yangtze Plain of China have attracted global concern. However, a comprehensive assessment of the eutrophication status and its evolution is still lacking for these regional lakes, mostly because of technical difficulties and/or insufficient data to cover the large region. Our study attempts to fill this knowledge gap by using the entire archive of remote sensing images from two satellite ocean color missions (MEdium Resolution Imaging Spectrometer, or MERIS (2003−2011), and Ocean and Land Color Instrument, or OLCI (2017–2018)), together with in situ data on remote sensing reflectance and chlorophyll-a (Chla) concentrations across various lakes on the Yangtze Plain. A machine learning-based piecewise Chla algorithm was developed in this study, with special considerations to improve algorithm performance under lower Chla conditions. Remotely sensed Chla and algal bloom areas were then used to classify the eutrophication status of 50 large lakes on the Yangtze Plain, and the frequent satellite observations enabled us to estimate the probability of eutrophication occurrence (PEO) for each examined lake. The long-term mean Chla ranged from 17.58 mg m<sup>−3</sup> to 43.86 mg m<sup>−3</sup> on the Yangtze Plain, and severe floating algal blooms were found in 7 lakes. All 50 lakes had high climatological PEO values (50%) during the study period, indicating a generally high probability of eutrophication in lakes on the Yangtze Plain. However, 21 out of 51 lakes exhibited statistically significant (p &lt; .05) decreasing trends in PEO during the observation period, suggesting an overall improvement in the water quality of lakes on the Yangtze Plain in recent years. The methods developed here are expected to contribute to real-time monitoring of drinking water safety for local regions, and the long-term results provide valuable baseline information for future lake conservation and restoration efforts.</p>},
  author       = {Guan, Qi and Feng, Lian and Hou, Xuejiao and Schurgers, Guy and Zheng, Yi and Tang, Jing},
  issn         = {0034-4257},
  language     = {eng},
  publisher    = {Elsevier},
  series       = {Remote Sensing of Environment},
  title        = {Eutrophication changes in fifty large lakes on the Yangtze Plain of China derived from MERIS and OLCI observations},
  url          = {http://dx.doi.org/10.1016/j.rse.2020.111890},
  doi          = {10.1016/j.rse.2020.111890},
  volume       = {246},
  year         = {2020},
}