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Lai L, Zhang Y, Han T, Zhang M, Cao Z, Liu Z, Yang Q, Chen X. Satellite mapping reveals phytoplankton biomass's spatio-temporal dynamics and responses to environmental factors in a eutrophic inland lake. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 360:121134. [PMID: 38749137 DOI: 10.1016/j.jenvman.2024.121134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 05/06/2024] [Accepted: 05/09/2024] [Indexed: 06/05/2024]
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 (Beu). Here, a long-term Beu 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 Beu (total Beu 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 Beu (total Beu 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 Beu 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 Beu 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.
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Affiliation(s)
- Lai Lai
- 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
| | - Yuchao Zhang
- 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.
| | - Tao Han
- 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
| | - Min Zhang
- 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
| | - Zhen Cao
- 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
| | - Zhaomin Liu
- 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
| | - Qiduo Yang
- 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
| | - Xi Chen
- 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
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Wen Z, Shang Y, Lyu L, Tao H, Liu G, Fang C, Li S, Song K. Re-estimating China's lake CO 2 flux considering spatiotemporal variability. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2024; 19:100337. [PMID: 38107556 PMCID: PMC10724546 DOI: 10.1016/j.ese.2023.100337] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 10/25/2023] [Accepted: 11/06/2023] [Indexed: 12/19/2023]
Abstract
The spatiotemporal variability of lake partial carbon dioxide pressure (pCO2) introduces uncertainty into CO2 flux estimates at the lake water-air interface. Knowing the variation pattern of pCO2 is important for obtaining accurate global estimation. Here we examine seasonal and trophic variations in lake pCO2 based on 13 field campaigns conducted in Chinese lakes from 2017 to 2021. We found significant seasonal fluctuations in pCO2, with decreasing values as trophic states intensify within the same region. Saline lakes exhibit lower pCO2 levels than freshwater lakes. These pCO2 dynamics result in variable areal CO2 emissions, with lakes exhibiting different trophic states (oligotrophication > mesotrophication > eutrophication) and saline lakes differing from freshwater lakes (-23.1 ± 17.4 vs. 19.3 ± 18.3 mmol m-2 d-1). These spatiotemporal pCO2 variations complicate total CO2 emission estimations. Using area proportions of lakes with varying trophic states and salinity in China, we estimate China's lake CO2 flux at 8.07 Tg C yr-1. In future studies, the importance of accounting for lake salinity, seasonal dynamics, and trophic states must be noticed to enhance the accuracy of large-scale carbon emission estimates from lake ecosystems in the context of climate change.
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Affiliation(s)
- Zhidan Wen
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
| | - Yingxin Shang
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
| | - Lili Lyu
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
| | - Hui Tao
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
| | - Ge Liu
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
| | - Chong Fang
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
| | - Sijia Li
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
| | - Kaishan Song
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
- School of Environment and Planning, Liaocheng University, Liaocheng, 252000, China
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Cai X, Lei S, Li Y, Li J, Xu J, Lyu H, Li J, Dong X, Wang G, Zeng S. Humification levels of dissolved organic matter in the eastern plain lakes of China based on long-term satellite observations. WATER RESEARCH 2024; 250:120991. [PMID: 38113596 DOI: 10.1016/j.watres.2023.120991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 11/23/2023] [Accepted: 12/07/2023] [Indexed: 12/21/2023]
Abstract
Under the influence of intensive human activities and global climate change, the sources and compositions of dissolved organic matter (DOM) in the eastern plain lake (EPL) region in China have fluctuated sharply. It has been successfully proven that the humification index (HIX), which can be derived from three-dimensional excitation-emission matrix fluorescence spectroscopy, can be an effective proxy for the sources and compositions of DOM. Therefore, combined with remote sensing technology, the sources and compositions of DOM can be tracked on a large scale by associating the HIX with optically active components. Here, we proposed a novel HIX remote sensing retrieval (IRHIX) model suitable for Landsat series sensors based on the comprehensive analysis of the covariation mechanism between HIX and optically active components in different water types. The validation results showed that the model runs well on the independent validation dataset and the satellite-ground synchronous sampling dataset, with an uncertainty ranging from 30.85 % to 36.92 % (average ± standard deviation = 33.6 % ± 3.07 %). The image-derived HIX revealed substantial spatiotemporal variations in the sources and compositions of DOM in 474 lakes in the EPL during 1986-2021. Subsequently, we obtained three long-term change modes of the HIX trend, namely, significant decline, gentle change, and significant rise, accounting for 74.68 %, 17.09 %, and 8.23 % of the lake number, respectively. The driving factor analysis showed that human activities had the most extensive influence on the DOM humification level. In addition, we also found that the HIX increased slightly with increasing lake area (R2 = 0.07, P < 0.05) or significantly with decreasing trophic state (R2 = 0.83, P < 0.05). Our results provide a new exploration for the effective acquisition of long-term dynamic information about the sources and compositions of DOM in inland lakes and provide important support for lake water quality management and restoration.
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Affiliation(s)
- Xiaolan Cai
- School of Geography, Key Laboratory of Virtual Geographic Environment of Education Ministry, Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, China
| | - Shaohua Lei
- National Key Laboratory of Water Disaster Prevention, Nanjing Hydraulic Research Institute, Nanjing 210029, China
| | - Yunmei Li
- School of Geography, Key Laboratory of Virtual Geographic Environment of Education Ministry, Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, China.
| | - Jianzhong Li
- School of Geography, Key Laboratory of Virtual Geographic Environment of Education Ministry, Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, China
| | - Jie Xu
- Yangtze River Basin Ecological Environment Monitoring and Scientific Research Center, Yangtze River Basin Ecological Environment Supervision and Administration Bureau, Ministry of Ecological Environment, Wuhan 430010, China
| | - Heng Lyu
- School of Geography, Key Laboratory of Virtual Geographic Environment of Education Ministry, Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, China
| | - Junda Li
- School of Geography, Key Laboratory of Virtual Geographic Environment of Education Ministry, Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, China
| | - Xianzhang Dong
- School of Geography, Key Laboratory of Virtual Geographic Environment of Education Ministry, Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, China
| | - Gaolun Wang
- School of Geography, Key Laboratory of Virtual Geographic Environment of Education Ministry, Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, China
| | - Shuai Zeng
- Ministry of Ecology and Environment, South China Institute of Environmental Science, Guangzhou 510535, China
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Liao Y, Xiao Q, Li Y, Yang C, Li J, Duan H. Salinity is an important factor in carbon emissions from an inland lake in arid region. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 906:167721. [PMID: 37832686 DOI: 10.1016/j.scitotenv.2023.167721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 09/21/2023] [Accepted: 10/08/2023] [Indexed: 10/15/2023]
Abstract
Saline lakes, serving as the ultimate destination for most hydrological systems, accumulate substantial amounts of nutrients and organic matter from basins, and act as vast carbon reservoirs. These lakes exhibit exceptionally active biogeochemical cycling processes of carbon dioxide (CO2) and methane (CH4), and constitute integral components of the global carbon cycle. However, understanding of greenhouse gas emissions from saline lakes remains unclear mostly due to scarce data. In this study, we obtained CO2 and CH4 diffusive fluxes and biogeochemical parameters during ice-free period of 2021 at Bosten Lake, which is a representative inland saline lake located in China's arid region. Results revealed that Bosten Lake was a significant source of atmospheric gas carbon emissions, with average diffusion emissions of 12.645 ± 3.475 mmol m-2 d-1 for CO2 and 0.279 ± 0.069 mmol m-2 d-1 for CH4. Temporally, field measurements found a positive correlation between conductivity (Spc, a proxy of salinity) and CO2 emissions (R2 = 0.50, p < 0.01). Furthermore, the CH4 diffusive fluxes increased with the trophic state index (TSI, R2 = 0.31, p < 0.01). Spatially, exogenous inputs led to the spatial heterogeneity of carbon emissions. Our results highlighted that temporal variations in salinity constitute a crucial factor influencing CO2 emissions, and the saline lake has greater global warming potential compared to freshwater. The study provides an in-depth analysis of greenhouse gas emissions and driving factors in saline lakes of arid regions, and supports a further understanding of the carbon cycle in different types of lakes.
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Affiliation(s)
- Yuanshan Liao
- College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China; Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
| | - Qitao Xiao
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China.
| | - Yimin Li
- College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China; Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
| | - Chen Yang
- College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China; Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
| | - Junli Li
- Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
| | - Hongtao Duan
- College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China; 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, Nanjing 211135, China.
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Lai L, Liu Y, Zhang Y, Cao Z, Yang Q, Chen X. MODIS Terra and Aqua images bring non-negligible effects to phytoplankton blooms derived from satellites in eutrophic lakes. WATER RESEARCH 2023; 246:120685. [PMID: 37804806 DOI: 10.1016/j.watres.2023.120685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 09/18/2023] [Accepted: 09/29/2023] [Indexed: 10/09/2023]
Abstract
Phytoplankton-induced lake eutrophication has drawn ongoing interest on a global scale. One of the most popular remote sensing satellite data for observing long-term dynamic changes in phytoplankton is Moderate-resolution Imaging Spectroradiometer (MODIS). However, it is worth noting that MODIS provides two images with different transit times: Terra (local time, about 10:30 am) and Aqua (local time, about 1:30 pm), which may result in a considerable bias in monitoring phytoplankton bloom areas due to the rapid migration of phytoplankton under wind or hydrodynamic conditions. To analyze this quantitatively, we selected MODIS Terra and Aqua images to generate datasets of phytoplankton bloom areas in Lake Taihu from 2003 to 2022. The results showed that Terra more frequently detected larger ranges of phytoplankton blooms than Aqua, whether on daily, monthly, or annual scales. In addition, long-term trend changes, seasonal characteristics, and abrupt years also varied with different transit times. Terra detected mutation years earlier, while Aqua displayed more pronounced seasonal characteristics. There were also differences in sensitivity to climate factors, with Terra being more responsive to temperature and wind speed on monthly and annual scales, while Aqua was more sensitive to nutrient and meteorological factors. These conclusions have also been further confirmed in Lake Chaohu, Lake Dianchi, and Lake Hulun. In conclusion, our findings strongly advocate for a linear relationship to fit Terra to Aqua results to mitigate long-term monitoring errors of phytoplankton blooms in inland lakes (R2 = 0.70, RMSE = 101.56). It is advised to utilize satellite data with transit times between 10 am and 1 pm to track phytoplankton bloom changes and to consider the diverse applications resulting from the transit times of Terra and Aqua.
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Affiliation(s)
- Lai Lai
- 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
| | - Yuchen Liu
- School of Geography and Ocean Science, Nanjing University, Nanjing, 210023, China; Collaborative Innovation Center of South China Sea Studies, Nanjing University, Nanjing, 210093, China
| | - Yuchao Zhang
- 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.
| | - Zhen Cao
- 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
| | - Qiduo Yang
- 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
| | - Xi Chen
- 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
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Duan H, Xiao Q, Qi T, Hu C, Zhang M, Shen M, Hu Z, Wang W, Xiao W, Qiu Y, Luo J, Lee X. Quantification of Diffusive Methane Emissions from a Large Eutrophic Lake with Satellite Imagery. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:13520-13529. [PMID: 37651621 DOI: 10.1021/acs.est.3c05631] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Lakes are major emitters of methane (CH4); however, a longstanding challenge with quantifying the magnitude of emissions remains as a result of large spatial and temporal variability. This study was designed to address the issue using satellite remote sensing with the advantages of spatial coverage and temporal resolution. Using Aqua/MODIS imagery (2003-2020) and in situ measured data (2011-2017) in eutrophic Lake Taihu, we compared the performance of eight machine learning models to predict diffusive CH4 emissions and found that the random forest (RF) model achieved the best fitting accuracy (R2 = 0.65 and mean relative error = 21%). On the basis of input satellite variables (chlorophyll a, water surface temperature, diffuse attenuation coefficient, and photosynthetically active radiation), we assessed how and why they help predict the CH4 emissions with the RF model. Overall, these variables mechanistically controlled the emissions, leading to the model capturing well the variability of diffusive CH4 emissions from the lake. Additionally, we found climate warming and associated algal blooms boosted the long-term increase in the emissions via reconstructing historical (2003-2020) daily time series of CH4 emissions. This study demonstrates the great potential of satellites to map lake CH4 emissions by providing spatiotemporal continuous data, with new and timely insights into accurately understanding the magnitude of aquatic greenhouse gas emissions.
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Affiliation(s)
- Hongtao Duan
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, Jiangsu 210008, People's Republic of China
- University of Chinese Academy of Sciences, Nanjing, Jiangsu 211135, People's Republic of China
| | - Qitao Xiao
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, Jiangsu 210008, People's Republic of China
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, Jiangsu 210044, People's Republic of China
| | - Tianci Qi
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, Jiangsu 210008, People's Republic of China
| | - Cheng Hu
- College of Biology and the Environment, Joint Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, Jiangsu 210037, People's Republic of China
| | - Mi Zhang
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, Jiangsu 210044, People's Republic of China
| | - Ming Shen
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, Jiangsu 210008, People's Republic of China
| | - Zhenghua Hu
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, Jiangsu 210044, People's Republic of China
| | - Wei Wang
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, Jiangsu 210044, People's Republic of China
| | - Wei Xiao
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, Jiangsu 210044, People's Republic of China
| | - Yinguo Qiu
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, Jiangsu 210008, People's Republic of China
| | - Juhua Luo
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, Jiangsu 210008, People's Republic of China
| | - Xuhui Lee
- School of the Environment, Yale University, New Haven, Connecticut 06511, United States
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Lai L, Zhang Y, Cao Z, Liu Z, Yang Q. Algal biomass mapping of eutrophic lakes using a machine learning approach with MODIS images. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 880:163357. [PMID: 37028659 DOI: 10.1016/j.scitotenv.2023.163357] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 04/03/2023] [Accepted: 04/03/2023] [Indexed: 05/27/2023]
Abstract
Algal blooms are a widespread issue in eutrophic lakes. Compared with the satellite-derived surface algal bloom area and chlorophyll-a (Chla) concentration, algae biomass is a more stable way to reflect water quality. Although satellite data have been adopted to observe the water column integrated algal biomass, the previous methods mostly are empirical algorithms, which are not stable enough for widespread use. This paper proposed a machine learning algorithm based on Moderate Resolution Imaging Spectrometer (MODIS) data to estimate the algal biomass, which was successfully applied to a eutrophic lake in China, Lake Taihu. This algorithm was developed by linking Rayleigh-corrected reflectance to in situ algae biomass data in Lake Taihu (n = 140), and the different mainstream machine learning (ML) methods were compared and validated. The partial least squares regression (PLSR) (R2 = 0.67, mean absolute percentage error (MAPE) = 38.88 %) and support vector machines (SVM) (R2 = 0.46, MAPE = 52.02 %) performed poor satisfactory. In contrast, random forest (RF) and extremely gradient boosting tree (XGBoost) algorithms had higher accuracy (RF: R2 = 0.85, MAPE = 22.68 %; XGBoost: R2 = 0.83, MAPE = 24.06 %), demonstrating greater application potential in algal biomass estimation. Field biomass data were further used to estimate the RF algorithm, which showed acceptable precision (R2 = 0.86, MAPE < 7 mg Chla). Subsequently, sensitivity analysis showed that the RF algorithm was not sensitive to high suspension and thickness of aerosols (rate of change <2 %), and inter-day and consecutive days verification showed stability (rate of change <5 %). The algorithm was also extended to Lake Chaohu (R2 = 0.93, MAPE = 18.42 %), demonstrating its potential in other eutrophic lakes. This study for algae biomass estimation provides technical means with higher accuracy and greater universality for the management of eutrophic lakes.
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Affiliation(s)
- Lai Lai
- 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
| | - Yuchao Zhang
- 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.
| | - Zhen Cao
- 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
| | - Zhaomin Liu
- 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
| | - Qiduo Yang
- 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
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Wang S, Zhang X, Wang C, Chen N. Temporal continuous monitoring of cyanobacterial blooms in Lake Taihu at an hourly scale using machine learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159480. [PMID: 36265631 DOI: 10.1016/j.scitotenv.2022.159480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/09/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
Cyanobacterial blooms in most lakes exhibit extraordinary changes in time and space. Herein, a cyanobacterial prediction model was designed for Lake Taihu based on a machine learning method. This method can generate temporally continuous (24 moments throughout the day) cyanobacterial data at a fine spatial scale of 9 km. The hourly meteorological data for 24 moments of the day were obtained from ERA5-Land data. Areal coverage of cyanobacterial blooms was derived from the hourly Geostationary Ocean Color Imager reflectance data observed only eight times a day (from ~8:00 to ~15:00, UTC+8). The cyanobacterial and meteorological data of eight moments in Lake Taihu from 2011 to 2020 were used to design the prediction model. The results were compared and validated employing nine training strategies to determine the best cyanobacterial prediction model for Lake Taihu (R = 0.42; root mean square error = 0.10). With the best-fitted model utilizing meteorological data (2011-2020), the area coverage of cyanobacterial blooms at the other 16 moments during a day were estimated. Based on this, the regional and temporal characteristics of diurnal bloom variation were evaluated at an hourly scale. The results indicated that the hourly variations in the areal coverage of cyanobacterial blooms at 24 moments of the day had similar patterns in each subregion of Lake Taihu with minor seasonal variations. The six meteorological variables adopted to construct the model had similar diurnal changes but with diverse value ranges among the seasons. Further analysis revealed that three meteorological variables (temperature, surface pressure, and evaporation) were positively related to diurnal bloom variations at an hourly scale. Overall, these results illustrate that meteorological conditions can affect the occurrence of cyanobacterial blooms at multiple time scales (e.g., hourly, daily, or monthly). The developed cyanobacterial prediction model can provide cyanobacterial data when cyanobacterial data is unavailable for the target waterbody.
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Affiliation(s)
- Siqi Wang
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China; Hubei Luojia Laboratory, Wuhan University, Wuhan 430079, China.
| | - Xiang Zhang
- Hubei Luojia Laboratory, Wuhan University, Wuhan 430079, China; National Engineering Research Centre of Geographic Information System, China University of Geosciences, Wuhan 430074, China
| | - Chao Wang
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China; Hubei Luojia Laboratory, Wuhan University, Wuhan 430079, China
| | - Nengcheng Chen
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China; Hubei Luojia Laboratory, Wuhan University, Wuhan 430079, China; National Engineering Research Centre of Geographic Information System, China University of Geosciences, Wuhan 430074, China.
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