Variations in chlorophyll-a concentration in response to hydrodynamics in a flow-through lake: Remote sensing and modeling studies
A convolution neural network (CNN) machine learning algorithm is established for Chlorophyll-a retrieval in Poyang Lake using in-situ Chlorophyll-a concentrations and concurrent satellite image data. For comparison, other machine learning methods, e.g., deep neural network, XGBoost, random forest, g...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-04-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X23002704 |
| _version_ | 1797866123653283840 |
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| author | Jiajun Xu Jiayi Pan Adam T. Devlin |
| author_facet | Jiajun Xu Jiayi Pan Adam T. Devlin |
| author_sort | Jiajun Xu |
| collection | DOAJ |
| description | A convolution neural network (CNN) machine learning algorithm is established for Chlorophyll-a retrieval in Poyang Lake using in-situ Chlorophyll-a concentrations and concurrent satellite image data. For comparison, other machine learning methods, e.g., deep neural network, XGBoost, random forest, gradient boost, decision tree, and linear regression, are also employed, together with two empirical models. Accuracy analysis validates the robustness of the CNN algorithm, with the R2 of 0.97 and the mean absolute errors of 1.74 μgL−1, and its performance is better than other machine learning and empirical algorithms. The Chl-a in Poyang Lake is derived from Sentinel-2 Multi-Spectral Instrument (MSI) images based on the robust CNN algorithm. As a follow-through type, Poyang Lake has abundant hydrodynamics affecting the Chl-a in the lake water. Thus, the Delft3D Flexible Mesh (FM) model is employed in Poyang Lake to simulate the active hydrodynamics with a high spatial resolution (∼80 m) under the realistic forcing in the period from 1 October 2020 to 15 March 2022. The simulation results reveal that the flow speed is a significant factor modulating the lake Chl-a with a high concentration in summer and a low concentration in winter; the flow speed and water storage exceed the temperature and solar radiance in modulating the Chl-a. The jacking effect of the Yangtze River can slow down the flow speed in the lake’s north channel, therefore, augmenting the Chl-a by ∼20%, favoring phytoplankton blooms in Poyang Lake. The active eddies may significantly affect the lake Chl-a; cyclonic eddies enhance the local Chl-a, and anticyclonic eddies weaken the Chl-a. A detailed analysis indicates that the Coriolis force plays a vital role in the momentum balance, which may cause the lift (restriction) of bottom nutrients in the cyclonic (anticyclonic) eddy area, increasing (decreasing) the surface nutrient supply. This study uses Poyang Lake as an example to show that such eddies conventionally appearing in a flow-through lake may dramatically affect the ecological environment in the lake water. |
| first_indexed | 2024-04-09T23:20:07Z |
| format | Article |
| id | doaj.art-d8b20a7238014442a2652b21c9163f04 |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-04-09T23:20:07Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-d8b20a7238014442a2652b21c9163f042023-03-22T04:36:23ZengElsevierEcological Indicators1470-160X2023-04-01148110128Variations in chlorophyll-a concentration in response to hydrodynamics in a flow-through lake: Remote sensing and modeling studiesJiajun Xu0Jiayi Pan1Adam T. Devlin2School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China; Key Lab of Poyang Lake Wetland and Watershed Research of Ministry of Education, Nanchang 330022, ChinaSchool of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China; Key Lab of Poyang Lake Wetland and Watershed Research of Ministry of Education, Nanchang 330022, China; Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong, China; Corresponding author at: School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China.Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong, China; Cooperative Institute for Marine and Atmospheric Research, School of Ocean and Earth Science and Technology, University of Hawaiʻi at Mānoa, Honolulu, HI 96822, United States; Department of Oceanography, University of Hawaiʻi at Mānoa, Honolulu, HI 96822, United StatesA convolution neural network (CNN) machine learning algorithm is established for Chlorophyll-a retrieval in Poyang Lake using in-situ Chlorophyll-a concentrations and concurrent satellite image data. For comparison, other machine learning methods, e.g., deep neural network, XGBoost, random forest, gradient boost, decision tree, and linear regression, are also employed, together with two empirical models. Accuracy analysis validates the robustness of the CNN algorithm, with the R2 of 0.97 and the mean absolute errors of 1.74 μgL−1, and its performance is better than other machine learning and empirical algorithms. The Chl-a in Poyang Lake is derived from Sentinel-2 Multi-Spectral Instrument (MSI) images based on the robust CNN algorithm. As a follow-through type, Poyang Lake has abundant hydrodynamics affecting the Chl-a in the lake water. Thus, the Delft3D Flexible Mesh (FM) model is employed in Poyang Lake to simulate the active hydrodynamics with a high spatial resolution (∼80 m) under the realistic forcing in the period from 1 October 2020 to 15 March 2022. The simulation results reveal that the flow speed is a significant factor modulating the lake Chl-a with a high concentration in summer and a low concentration in winter; the flow speed and water storage exceed the temperature and solar radiance in modulating the Chl-a. The jacking effect of the Yangtze River can slow down the flow speed in the lake’s north channel, therefore, augmenting the Chl-a by ∼20%, favoring phytoplankton blooms in Poyang Lake. The active eddies may significantly affect the lake Chl-a; cyclonic eddies enhance the local Chl-a, and anticyclonic eddies weaken the Chl-a. A detailed analysis indicates that the Coriolis force plays a vital role in the momentum balance, which may cause the lift (restriction) of bottom nutrients in the cyclonic (anticyclonic) eddy area, increasing (decreasing) the surface nutrient supply. This study uses Poyang Lake as an example to show that such eddies conventionally appearing in a flow-through lake may dramatically affect the ecological environment in the lake water.http://www.sciencedirect.com/science/article/pii/S1470160X23002704Poyang LakeChlorophyll-aConvolution Neural NetworkDelft3D FMEddies |
| spellingShingle | Jiajun Xu Jiayi Pan Adam T. Devlin Variations in chlorophyll-a concentration in response to hydrodynamics in a flow-through lake: Remote sensing and modeling studies Ecological Indicators Poyang Lake Chlorophyll-a Convolution Neural Network Delft3D FM Eddies |
| title | Variations in chlorophyll-a concentration in response to hydrodynamics in a flow-through lake: Remote sensing and modeling studies |
| title_full | Variations in chlorophyll-a concentration in response to hydrodynamics in a flow-through lake: Remote sensing and modeling studies |
| title_fullStr | Variations in chlorophyll-a concentration in response to hydrodynamics in a flow-through lake: Remote sensing and modeling studies |
| title_full_unstemmed | Variations in chlorophyll-a concentration in response to hydrodynamics in a flow-through lake: Remote sensing and modeling studies |
| title_short | Variations in chlorophyll-a concentration in response to hydrodynamics in a flow-through lake: Remote sensing and modeling studies |
| title_sort | variations in chlorophyll a concentration in response to hydrodynamics in a flow through lake remote sensing and modeling studies |
| topic | Poyang Lake Chlorophyll-a Convolution Neural Network Delft3D FM Eddies |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X23002704 |
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