Spatiotemporal variation in biomass abundance of different algal species in Lake Hulun using machine learning and Sentinel-3 images
Abstract Climate change and human activities affect the biomass of different algal and the succession of dominant species. In the past, phytoplankton phyla inversion has been focused on oceanic and continental shelf waters, while phytoplankton phyla inversion in inland lakes and reservoirs is still...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-87338-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832585792806977536 |
|---|---|
| author | Zhaojiang Yan Chong Fang Kaishan Song Xiangyu Wang Zhidan Wen Yingxin Shang Hui Tao Yunfeng Lyu |
| author_facet | Zhaojiang Yan Chong Fang Kaishan Song Xiangyu Wang Zhidan Wen Yingxin Shang Hui Tao Yunfeng Lyu |
| author_sort | Zhaojiang Yan |
| collection | DOAJ |
| description | Abstract Climate change and human activities affect the biomass of different algal and the succession of dominant species. In the past, phytoplankton phyla inversion has been focused on oceanic and continental shelf waters, while phytoplankton phyla inversion in inland lakes and reservoirs is still in the initial and exploratory stage, and the research results are relatively few. Especially for mid-to-high latitude lakes, the research is even more blank. Therefore, this study proposes a machine learning method based on OLCI/Sentinel-3 satellite imagery to retrieve algal biomass abundance. Remote sensing models were developed to estimate the biomass abundance of three major algal groups: Cyanophyta, Chlorophyta, and Bacillariophyta. This study compared and evaluated 6 commonly used machine learning models, including extreme gradient boosting (XGBoost), support vector regression (SVR), backpropagation neural network (BP), gradient boosting decision tree (GBDT), random forest (RF), and categorical boosting (CatBoost). The results indicated that XGBoost exhibited the highest accuracy (R2 = 0.92, RMSE = 1.78%, MAPE = 9.96%) in estimating Cyanophyta’s biomass abundance. The RF model demonstrated the highest accuracy for estimating Chlorophyta’s biomass abundance (R2 = 0.72, RMSE = 6.57%, MAPE = 50.8%), while the GBDT model exhibited the highest accuracy for estimating Bacillariophyta’s biomass abundance (R2 = 0.9, RMSE = 4.66%, MAPE = 47.87%). The models were subsequently applied to all cloud-free OLCI images from Hulun Lake during the ice-free periods from 2016 to 2023, producing spatiotemporal distribution maps of the different phytoplankton biomass abundance. Cyanophyta dominated the biomass abundance (44.62 ± 3.47%), followed by Bacillariophyta (36.35 ± 2.68%), and Chlorophyta had the lowest proportion (10.42 ± 1.08%). Together, these three algae groups constituted 91.4 ± 1.55% of all phytoplankton in Hulun Lake. Significant annual variations in the biomass abundance of Cyanophyta and Bacillariophyta were observed, whereas those of Chlorophyta remained stable. Additionally, this study examined the effects of climatic factors and water quality parameters on the biomass abundance of algae. The findings suggest that temperature, wind speed, and atmospheric pressure are critical factors influencing the biomass abundance of the different algae groups. This study not only fills the gaps in the related field, but also provides a new method for monitoring algae, as well as a strong support for realizing the goals of sustainable management of water resources and ecological protection. |
| format | Article |
| id | doaj-art-9878fe149e0f4e91960ee8298241d400 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9878fe149e0f4e91960ee8298241d4002025-01-26T12:29:41ZengNature PortfolioScientific Reports2045-23222025-01-0115111510.1038/s41598-025-87338-4Spatiotemporal variation in biomass abundance of different algal species in Lake Hulun using machine learning and Sentinel-3 imagesZhaojiang Yan0Chong Fang1Kaishan Song2Xiangyu Wang3Zhidan Wen4Yingxin Shang5Hui Tao6Yunfeng Lyu7School of Geographic Science, Changchun Normal UniversityState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of SciencesState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of SciencesSchool of Geographic Science, Changchun Normal UniversityState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of SciencesState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of SciencesState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of SciencesSchool of Geographic Science, Changchun Normal UniversityAbstract Climate change and human activities affect the biomass of different algal and the succession of dominant species. In the past, phytoplankton phyla inversion has been focused on oceanic and continental shelf waters, while phytoplankton phyla inversion in inland lakes and reservoirs is still in the initial and exploratory stage, and the research results are relatively few. Especially for mid-to-high latitude lakes, the research is even more blank. Therefore, this study proposes a machine learning method based on OLCI/Sentinel-3 satellite imagery to retrieve algal biomass abundance. Remote sensing models were developed to estimate the biomass abundance of three major algal groups: Cyanophyta, Chlorophyta, and Bacillariophyta. This study compared and evaluated 6 commonly used machine learning models, including extreme gradient boosting (XGBoost), support vector regression (SVR), backpropagation neural network (BP), gradient boosting decision tree (GBDT), random forest (RF), and categorical boosting (CatBoost). The results indicated that XGBoost exhibited the highest accuracy (R2 = 0.92, RMSE = 1.78%, MAPE = 9.96%) in estimating Cyanophyta’s biomass abundance. The RF model demonstrated the highest accuracy for estimating Chlorophyta’s biomass abundance (R2 = 0.72, RMSE = 6.57%, MAPE = 50.8%), while the GBDT model exhibited the highest accuracy for estimating Bacillariophyta’s biomass abundance (R2 = 0.9, RMSE = 4.66%, MAPE = 47.87%). The models were subsequently applied to all cloud-free OLCI images from Hulun Lake during the ice-free periods from 2016 to 2023, producing spatiotemporal distribution maps of the different phytoplankton biomass abundance. Cyanophyta dominated the biomass abundance (44.62 ± 3.47%), followed by Bacillariophyta (36.35 ± 2.68%), and Chlorophyta had the lowest proportion (10.42 ± 1.08%). Together, these three algae groups constituted 91.4 ± 1.55% of all phytoplankton in Hulun Lake. Significant annual variations in the biomass abundance of Cyanophyta and Bacillariophyta were observed, whereas those of Chlorophyta remained stable. Additionally, this study examined the effects of climatic factors and water quality parameters on the biomass abundance of algae. The findings suggest that temperature, wind speed, and atmospheric pressure are critical factors influencing the biomass abundance of the different algae groups. This study not only fills the gaps in the related field, but also provides a new method for monitoring algae, as well as a strong support for realizing the goals of sustainable management of water resources and ecological protection.https://doi.org/10.1038/s41598-025-87338-4Algal biomass abundanceMachine learningRemote sensingOLCI dateLake Hulun |
| spellingShingle | Zhaojiang Yan Chong Fang Kaishan Song Xiangyu Wang Zhidan Wen Yingxin Shang Hui Tao Yunfeng Lyu Spatiotemporal variation in biomass abundance of different algal species in Lake Hulun using machine learning and Sentinel-3 images Scientific Reports Algal biomass abundance Machine learning Remote sensing OLCI date Lake Hulun |
| title | Spatiotemporal variation in biomass abundance of different algal species in Lake Hulun using machine learning and Sentinel-3 images |
| title_full | Spatiotemporal variation in biomass abundance of different algal species in Lake Hulun using machine learning and Sentinel-3 images |
| title_fullStr | Spatiotemporal variation in biomass abundance of different algal species in Lake Hulun using machine learning and Sentinel-3 images |
| title_full_unstemmed | Spatiotemporal variation in biomass abundance of different algal species in Lake Hulun using machine learning and Sentinel-3 images |
| title_short | Spatiotemporal variation in biomass abundance of different algal species in Lake Hulun using machine learning and Sentinel-3 images |
| title_sort | spatiotemporal variation in biomass abundance of different algal species in lake hulun using machine learning and sentinel 3 images |
| topic | Algal biomass abundance Machine learning Remote sensing OLCI date Lake Hulun |
| url | https://doi.org/10.1038/s41598-025-87338-4 |
| work_keys_str_mv | AT zhaojiangyan spatiotemporalvariationinbiomassabundanceofdifferentalgalspeciesinlakehulunusingmachinelearningandsentinel3images AT chongfang spatiotemporalvariationinbiomassabundanceofdifferentalgalspeciesinlakehulunusingmachinelearningandsentinel3images AT kaishansong spatiotemporalvariationinbiomassabundanceofdifferentalgalspeciesinlakehulunusingmachinelearningandsentinel3images AT xiangyuwang spatiotemporalvariationinbiomassabundanceofdifferentalgalspeciesinlakehulunusingmachinelearningandsentinel3images AT zhidanwen spatiotemporalvariationinbiomassabundanceofdifferentalgalspeciesinlakehulunusingmachinelearningandsentinel3images AT yingxinshang spatiotemporalvariationinbiomassabundanceofdifferentalgalspeciesinlakehulunusingmachinelearningandsentinel3images AT huitao spatiotemporalvariationinbiomassabundanceofdifferentalgalspeciesinlakehulunusingmachinelearningandsentinel3images AT yunfenglyu spatiotemporalvariationinbiomassabundanceofdifferentalgalspeciesinlakehulunusingmachinelearningandsentinel3images |