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Li Z, Zhang F, Shi J, Chan NW, Tan ML, Kung HT, Liu C, Cheng C, Cai Y, Wang W, Li X. Remote sensing for chromophoric dissolved organic matter (CDOM) monitoring research 2003-2022: A bibliometric analysis based on the web of science core database. MARINE POLLUTION BULLETIN 2023; 196:115653. [PMID: 37879130 DOI: 10.1016/j.marpolbul.2023.115653] [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: 07/07/2023] [Revised: 10/07/2023] [Accepted: 10/09/2023] [Indexed: 10/27/2023]
Abstract
Chromophoric dissolved organic matter (CDOM) occupies a critical part in biogeochemistry and energy flux of aquatic ecosystems. CDOM research spans in many fields, including chemistry, marine environment, biomass cycling, physics, hydrology, and climate change. In recent years, a series of remarkable research milestone have been achieved. On the basis of reviewing the research process of CDOM, combined with a bibliometric analysis, this study aims to provide a comprehensive review of the development and applications of remote sensing in monitoring CDOM from 2003 to 2022. The findings show that remote sensing data plays an important role in CDOM research as proven with the increasing number of publications since 2003, particularly in China and the United States. Primary research areas have gradually changed from studying absorption and fluorescence properties to optimization of remote sensing inversion models in recent years. Since the composition of oceanic and freshwater bodies differs significantly, it is important to choose the appropriate inversion method for different types of water body. At present, the monitoring of CDOM mainly relies on a single sensor, but the fusion of images from different sensors can be considered a major research direction due to the complex characteristics of CDOM. Therefore, in the future, the characteristics of CDOM will be studied in depth inn combination with multi-source data and other application models, where inversion algorithms will be optimized, inversion algorithms with low dependence on measured data will be developed, and a transportable inversion model will be built to break the regional limitations of the model and to promote the development of CDOM research in a deeper and more comprehensive direction.
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Affiliation(s)
- Zhihui Li
- College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
| | - Fei Zhang
- College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China.
| | - Jingchao Shi
- Department of Earth Sciences, The University of Memphis, Memphis, TN 38152, USA
| | - Ngai Weng Chan
- GeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, 11800, USM, Penang, Malaysia
| | - Mou Leong Tan
- GeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, 11800, USM, Penang, Malaysia
| | - Hsiang-Te Kung
- Department of Earth Sciences, The University of Memphis, Memphis, TN 38152, USA
| | | | - Chunyan Cheng
- College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
| | - Yunfei Cai
- College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
| | - Weiwei Wang
- College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
| | - Xingyou Li
- College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
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Zhu X, Guo H, Huang JJ, Tian S, Xu W, Mai Y. An ensemble machine learning model for water quality estimation in coastal area based on remote sensing imagery. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 323:116187. [PMID: 36261960 DOI: 10.1016/j.jenvman.2022.116187] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 09/01/2022] [Accepted: 09/02/2022] [Indexed: 06/16/2023]
Abstract
The accurate estimation of coastal water quality parameters (WQPs) is crucial for decision-makers to manage water resources. Although various machine learning (ML) models have been developed for coastal water quality estimation using remote sensing data, the performance of these models has significant uncertainties when applied to regional scales. To address this issue, an ensemble ML-based model was developed in this study. The ensemble ML model was applied to estimate chlorophyll-a (Chla), turbidity, and dissolved oxygen (DO) based on Sentinel-2 satellite images in Shenzhen Bay, China. The optimal input features for each WQP were selected from eight spectral bands and seven spectral indices. A local explanation strategy termed Shapley Additive Explanations (SHAP) was employed to quantify contributions of each feature to model outputs. In addition, the impacts of three climate factors on the variation of each WQP were analyzed. The results suggested that the ensemble ML models have satisfied performance for Chla (errors = 1.7%), turbidity (errors = 1.5%) and DO estimation (errors = 0.02%). Band 3 (B3) has the highest positive contribution to Chla estimation, while Band Ration Index2 (BR2) has the highest negative contribution to turbidity estimation, and Band 7 (B7) has the highest positive contribution to DO estimation. The spatial patterns of the three WQPs revealed that the water quality deterioration in Shenzhen Bay was mainly influenced by input of terrestrial pollutants from the estuary. Correlation analysis demonstrated that air temperature (Temp) and average air pressure (AAP) exhibited the closest relationship with Chla. DO showed the strongest negative correlation with Temp, while turbidity was not sensitive to Temp, average wind speed (AWS), and AAP. Overall, the ensemble ML model proposed in this study provides an accurate and practical method for long-term Chla, turbidity, and DO estimation in coastal waters.
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Affiliation(s)
- Xiaotong Zhu
- College of Environmental Science and Engineering/Sino-Canada Joint R&D Centre for Water and Environment Safety,Nankai University, Tianjin, 300071, PR China
| | - Hongwei Guo
- College of Environmental Science and Engineering/Sino-Canada Joint R&D Centre for Water and Environment Safety,Nankai University, Tianjin, 300071, PR China
| | - Jinhui Jeanne Huang
- College of Environmental Science and Engineering/Sino-Canada Joint R&D Centre for Water and Environment Safety,Nankai University, Tianjin, 300071, PR China.
| | - Shang Tian
- College of Environmental Science and Engineering/Sino-Canada Joint R&D Centre for Water and Environment Safety,Nankai University, Tianjin, 300071, PR China
| | - Wang Xu
- Shenzhen Environmental Monitoring Center, Shenzhen, Guangdong, 518049, PR China
| | - Youquan Mai
- Shenzhen Environmental Monitoring Center, Shenzhen, Guangdong, 518049, PR China
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Cao Q, Yu G, Qiao Z. Application and recent progress of inland water monitoring using remote sensing techniques. ENVIRONMENTAL MONITORING AND ASSESSMENT 2022; 195:125. [PMID: 36401670 DOI: 10.1007/s10661-022-10690-9] [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: 12/23/2021] [Accepted: 10/19/2022] [Indexed: 06/16/2023]
Abstract
Hyperspectral remote sensing, which retrieves the water quality parameters by direct high-resolution analysis of the electromagnetic spectrum reflected from the water surface, has been widely applied for inland water quality detection. Such a new approach provides an opportunity to generate real-time data from water with the noncontact method, largely improving working efficiency. By summarizing the development and current applications of hyperspectral remote sensing, we compare the relative merits of varying remote sensing platforms, popular inversion models, and the application of hyperspectral monitoring of chlorophyll-a (Chl-a), transparency, total suspended solids (TSS), colored dissolved organic matter (CDOM), phycocyanin (PC), total phosphorus (TP), and total nitrogen (TN) water quality parameters. Most studies have focused on spaceborne remote sensing, which is usually used to monitor large waterbodies for Chl-a and other water quality parameters with optical properties; semiempirical, bio-optical, and semianalytical models are frequently used. With the rapid development of aerospace technology and near-surface remote sensing, the spectral resolution of remote sensing imaging technology has been dramatically improved and has begun to be applied to small waterbodies. In the future, the multiplatform linkage monitoring approach may become a new research direction. Advanced computer technology has also enabled machine learning models to be applied to water quality parameter inversion, and machine learning models have higher robustness than the three commonly used models mentioned above. Although nitrogen and phosphorus, with nonoptical properties, have also received attention and research from some scholars in recent years, the uncertainty of their mechanisms makes it necessary to maintain a cautious attitude when treating such research.
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Affiliation(s)
- Qi Cao
- Tianjin Key Laboratory of Aqua-Ecology and Aquaculture, College of Fisheries, Tianjin Agricultural University, Tianjin, 300384, China
| | - Gongliang Yu
- CAS Key Laboratory of Algal Biology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China
| | - Zhiyi Qiao
- Tianjin Key Laboratory of Aqua-Ecology and Aquaculture, College of Fisheries, Tianjin Agricultural University, Tianjin, 300384, China.
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Zhou Y, Yu D, Cheng W, Gai Y, Yao H, Yang L, Pan S. Monitoring multi-temporal and spatial variations of water transparency in the Jiaozhou Bay using GOCI data. MARINE POLLUTION BULLETIN 2022; 180:113815. [PMID: 35671614 DOI: 10.1016/j.marpolbul.2022.113815] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/10/2022] [Accepted: 05/30/2022] [Indexed: 06/15/2023]
Abstract
Water transparency, commonly measured as Secchi disk depth (SDD), is essential for describing the optical properties of coastal waters. We proposed a regional linear corrected SDD estimation model based on the North Sea Mathematical Models for GOCI and the mechanical model developed by Lee et al. (2015) in the Jiaozhou Bay. Combined with the multiple variable linear regression analysis, the diurnal SDD variations of the bay inside and the bay mouth are controlled by the solar zenith angle (SZA) and tides. The bay outside mainly varies with SZA. From GOCI observations between 2011 and 2021, wind force influenced the entire area on the inner-annual SDD variations. It exhibits an increasing trend in the inter-annual dynamics, which was more stable inside the bay with an annual increase of 0.035 m, and air temperature was the most significant contribution. However, human activities cannot be ignored in causing water environment changes.
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Affiliation(s)
- Yan Zhou
- Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266100, China
| | - Dingfeng Yu
- Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266100, China; Hydro-Environmental Research Centre, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK.
| | - Wentao Cheng
- Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China
| | - Yingying Gai
- Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266100, China
| | - Huiping Yao
- College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China
| | - Lei Yang
- Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266100, China
| | - Shunqi Pan
- Hydro-Environmental Research Centre, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK
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Shang Y, Jacinthe PA, Li L, Wen Z, Liu G, Lyu L, Fang C, Zhang B, Hou J, Song K. Variations in the light absorption coefficients of phytoplankton, non-algal particles and dissolved organic matter in reservoirs across China. ENVIRONMENTAL RESEARCH 2021; 201:111579. [PMID: 34197817 DOI: 10.1016/j.envres.2021.111579] [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: 03/09/2021] [Revised: 05/06/2021] [Accepted: 06/20/2021] [Indexed: 06/13/2023]
Abstract
Reservoirs were critical sources of drinking water for many large cities around the world, but progress in the development of large-scale monitoring protocols to obtain timely information about water quality had been hampered by the complex nature of inland waters and the various optical conditions exhibited by these aquatic ecosystems. In this study, we systematically investigated the absorption coefficient of different optically-active constituents (OACs) in 120 reservoirs of different trophic states across five eco-regions in China. The relationships were found between phytoplankton absorption coefficient at 675 nm (aph (675)) and Chlorophyll a (Chla) concentration in different regions (R2:0.60-0.82). The non-algal particle (NAP) absorption coefficient (aNAP) showed an increasing trend for reservoirs with trophic states. Significant correlation (p < 0.05) was observed between chromophoric dissolved organic matter (CDOM) absorption and water chemical parameters. The influencing factors for contributing the relative proportion of OACs absorption including the hydrological factors and water quality factors were analyzed. The non-water absorption budget from our data showed the variations of the dominant absorption types which underscored the need to develop and parameterize region-specific bio-optical models for large-scale assessment in water reservoirs.
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Affiliation(s)
- Yingxin Shang
- Northeast Institute of Geography and Agroecology, CAS, Changchun, 130102, Jilin, China; Changchun Jingyuetan Remote Sensing Observation Station, Chinese Academy of Sciences, Changchun, 130102, China; University of Chinese Academy of Science, Beijing, 100049, China
| | - Pierre-Andre Jacinthe
- Department of Earth Sciences, Indiana University-Purdue University, Indianapolis, IN, USA
| | - Lin Li
- Department of Earth Sciences, Indiana University-Purdue University, Indianapolis, IN, USA
| | - Zhidan Wen
- Northeast Institute of Geography and Agroecology, CAS, Changchun, 130102, Jilin, China; Changchun Jingyuetan Remote Sensing Observation Station, Chinese Academy of Sciences, Changchun, 130102, China
| | - Ge Liu
- Northeast Institute of Geography and Agroecology, CAS, Changchun, 130102, Jilin, China; Changchun Jingyuetan Remote Sensing Observation Station, Chinese Academy of Sciences, Changchun, 130102, China
| | - Lili Lyu
- Northeast Institute of Geography and Agroecology, CAS, Changchun, 130102, Jilin, China; University of Chinese Academy of Science, Beijing, 100049, China
| | - Chong Fang
- Northeast Institute of Geography and Agroecology, CAS, Changchun, 130102, Jilin, China; University of Chinese Academy of Science, Beijing, 100049, China
| | - Bai Zhang
- Northeast Institute of Geography and Agroecology, CAS, Changchun, 130102, Jilin, China; Changchun Jingyuetan Remote Sensing Observation Station, Chinese Academy of Sciences, Changchun, 130102, China
| | - Junbin Hou
- Northeast Institute of Geography and Agroecology, CAS, Changchun, 130102, Jilin, China
| | - Kaishan Song
- Northeast Institute of Geography and Agroecology, CAS, Changchun, 130102, Jilin, China; Changchun Jingyuetan Remote Sensing Observation Station, Chinese Academy of Sciences, Changchun, 130102, China; School of Environment and Planning, Liaocheng University, Liaocheng, 252000, China.
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Multiple Images Improve Lake CDOM Estimation: Building Better Landsat 8 Empirical Algorithms across Southern Canada. REMOTE SENSING 2021. [DOI: 10.3390/rs13183615] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Coloured dissolved organic matter (CDOM) is an important water property for lake management. Remote sensing using empirical algorithms has been used to estimate CDOM, with previous studies relying on coordinated field campaigns that coincided with satellite overpass. However, this requirement reduces the maximum possible sample size for model calibration. New satellites and advances in cloud computing platforms offer opportunities to revisit assumptions about methods used for empirical algorithm calibration. Here, we explore the opportunities and limits of using median values of Landsat 8 satellite images across southern Canada to estimate CDOM. We compare models created using an expansive view of satellite image availability with those emphasizing a tight timing between the date of field sampling and the date of satellite overpass. Models trained on median band values from across multiple summer seasons performed better (adjusted R2 = 0.70, N = 233) than models for which imagery was constrained to a 30-day time window (adjusted R2 = 0.45). Model fit improved rapidly when incorporating more images, producing a model at a national scale that performed comparably to others found in more limited spatial extents. This research indicated that dense satellite imagery holds new promise for understanding relationships between in situ CDOM and satellite reflectance data across large areas.
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Machine Learning Algorithms for Chromophoric Dissolved Organic Matter (CDOM) Estimation Based on Landsat 8 Images. REMOTE SENSING 2021. [DOI: 10.3390/rs13183560] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Chromophoric dissolved organic matter (CDOM) is crucial in the biogeochemical cycle and carbon cycle of aquatic environments. However, in inland waters, remotely sensed estimates of CDOM remain challenging due to the low optical signal of CDOM and complex optical conditions. Therefore, developing efficient, practical and robust models to estimate CDOM absorption coefficient in inland waters is essential for successful water environment monitoring and management. We examined and improved different machine learning algorithms using extensive CDOM measurements and Landsat 8 images covering different trophic states to develop the robust CDOM estimation model. The algorithms were evaluated via 111 Landsat 8 images and 1708 field measurements covering CDOM light absorption coefficient a(254) from 2.64 to 34.04 m−1. Overall, the four machine learning algorithms achieved more than 70% accuracy for CDOM absorption coefficient estimation. Based on model training, validation and the application on Landsat 8 OLI images, we found that the Gaussian process regression (GPR) had higher stability and estimation accuracy (R2 = 0.74, mean relative error (MRE) = 22.2%) than the other models. The estimation accuracy and MRE were R2 = 0.75 and MRE = 22.5% for backpropagation (BP) neural network, R2 = 0.71 and MRE = 24.4% for random forest regression (RFR) and R2 = 0.71 and MRE = 24.4% for support vector regression (SVR). In contrast, the best three empirical models had estimation accuracies of R2 less than 0.56. The model accuracies applied to Landsat images of Lake Qiandaohu (oligo-mesotrophic state) were better than those of Lake Taihu (eutrophic state) because of the more complex optical conditions in eutrophic lakes. Therefore, machine learning algorithms have great potential for CDOM monitoring in inland waters based on large datasets. Our study demonstrates that machine learning algorithms are available to map CDOM spatial-temporal patterns in inland waters.
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