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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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Li T, Zhang G, Wang S, Mao C, Tang Z, Rao W. The isotopic composition of organic carbon, nitrogen and provenance of organic matter in surface sediment from the Jiangsu tidal flat, southwestern Yellow Sea. MARINE POLLUTION BULLETIN 2022; 182:114010. [PMID: 35933850 DOI: 10.1016/j.marpolbul.2022.114010] [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: 11/26/2021] [Revised: 07/24/2022] [Accepted: 07/29/2022] [Indexed: 06/15/2023]
Abstract
The Jiangsu tidal flat is a significant organic matter reservoir, but quantitative studies of organic matter sources are scarce. In this study, total organic carbon (TOC) and total nitrogen (TN) concentrations and δ13Corg and δ15Ntotal values of surface sediment from Jiangsu tidal flat were investigated for their distributions, influencing factors, and sources of organic matter. TOC and TN were high in the center of study area and correlated well with grain size, indicating hydrodynamic influence on organic matter. High TOC/TN and low δ13Corg and δ15Ntotal in estuaries were characteristic of C3 plants, soil, and fertilizer sources, suggesting source effect on the distribution of organic matter. The MixSIAR model revealed that marine sources were dominant with a contribution reaching 56.9 %, followed by uniform of C3 plants, soil and fertilizer, while domestic sewage was least prominent. This study enriched theories of the biogeochemical cycle and ecological protection in the southwestern Yellow Sea.
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Affiliation(s)
- Tianning Li
- College of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
| | - Gucheng Zhang
- Hainan Key Laboratory of Marine Geological Resources and Environment, Hainan Geological Survey, Haikou 570206, China.
| | - Shuai Wang
- College of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China; Yellow River Institute of Eco-Environment Research, YRBEEA, Zhengzhou 450003, China
| | - Changping Mao
- College of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China.
| | - Zhen Tang
- College of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
| | - Wenbo Rao
- College of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China.
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Evaluation of GOCI Remote Sensing Reflectance Spectral Quality Based on a Quality Assurance Score System in the Bohai Sea. REMOTE SENSING 2022. [DOI: 10.3390/rs14051075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In the application of ocean color remote sensing, remote sensing reflectance spectral (Rrs(λ)) is the most important and basic parameter for the development of bio-optical algorithms. Atmospheric correction of ocean color data is a key factor in obtaining accurate water Rrs(λ) data. Based on the QA (quality assurance) score spectral quality evaluation system, the quality of Rrs(λ) spectral of GOCI (Geostationary Ocean Color Imager) obtained from four atmospheric-correction algorithms in the Bohai Sea were evaluated and analyzed in this paper. The four atmospheric-correction algorithms are the NASA (National Aeronautics and Space Administration) standard near-infrared atmospheric-correction algorithm (denoted as Seadas—Default), MUMM (Management Unit of the North Sea Mathematical Models, denoted as Seadas—MUMM), and the standard atmospheric-correction algorithms of KOSC GOCI GDPS2.0 (denoted as GDPS2.0) and GDPS1.3 (denoted as GDPS1.3). It is shown that over 90% of the Rrs(λ) data are in good quality with a score ≥4/6 for the GDPS1.3 algorithm. The probability of Rrs(λ) with a QA score of 1 is significantly higher for the GDPS1.3 algorithm (57.36%), compared with Seadas—Default (37.91%), Seadas—MUMM (35.96%), and GDPS2.0 (33.05%). The field and MODIS measurements of Rrs(λ) were compared with simultaneous GOCI Rrs(λ), and they demonstrate that the QA score system is useful in evaluating the spectral shape of Rrs(λ). The comparison results indicate that higher QA scores have higher accuracy of the Rrs band ratio. The QA score system is helpful to develop and evaluate bio-optical algorithms based on the band ratio. The hourly variation of QA score from UTC 00:16 to 07:16 was investigated as well, and it demonstrates that the data quality of GOCI Rrs(λ) can vary in an hour scale. The GOCI data with high quality should be selected with caution when studying the hourly variation of biogeochemical properties in the Bohai Sea from GOCI measurements.
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Du Y, Song K, Wang Q, Li S, Wen Z, Liu G, Tao H, Shang Y, Hou J, Lyu L, Zhang B. Total suspended solids characterization and management implications for lakes in East China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 806:151374. [PMID: 34740658 DOI: 10.1016/j.scitotenv.2021.151374] [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: 07/22/2021] [Revised: 10/15/2021] [Accepted: 10/28/2021] [Indexed: 06/13/2023]
Abstract
In this study, we empirically developed a robust model (the Root Mean Square Error (RMSE), bias, NSE and RE were 26.63 mg/L, -4.86 mg/L, 0.47 and 16.47%, respectively) for estimating the total suspended solids (TSS) concentrations in lakes and reservoirs (Hereinafter referred to as lakes) across the Eastern Plain Lake (EPL) Zone. The model was based on 700 in-situ TSS samples collected during 2007-2020 and logarithmic transformed red band reflectance of Landsat data. Based on the Google Earth Engine (GEE), the TSS concentrations in 16,804 lakes were mapped from 1984 to 2019. The results demonstrated a decreasing tendency of TSS in 82.2% of the examined lakes (72.5% of the basins) indicating that the pollutants carried by TSS flowing into the lakes were decreasing. Statistically significant variation (p < 0.05) was found in half of these lakes (28.6% of the basins). High TSS level (>100 mg/L) was observed in 0.31% of lakes (1.1% of the basins). The changing rates of TSS in 47.8% of the lakes (52.7% of the basins) ranged between -50 mg/L/yr and 0. We found high and significantly increased relative spatial heterogeneity of TSS in 4.6% and 6.5% of lakes, respectively. Likewise, the environmental factors, i.e., fertilizer usage, domestic wastewater, industrial wastewater, precipitation, wind speed and Normalized Difference Vegetation Index (NDVI) exhibited a significant correlation with interannual TSS in 38, 21, 20, 11, 17 and 15 of the 91 basins, respectively. This analysis indicated that only precipitation and fertilizer usage were significantly (p < 0.05) related to the spatial distribution of TSS. The relative contributions of the six factors to the interannual TSS changes were varied in different basins. Overall, the NDVI (the representation of vegetation cover) had a high mean contribution to the interannual TSS changes with an average contribution of 7.2%, and contributions of fertilizer were varied greatly among the basins (0.01%-68%). Human activities (fertilizer usage, domestic wastewater, industrial wastewater) and natural factors (precipitation, wind speed and NDVI) played relatively important roles to TSS changes in 14 and 15 of the 91 basins, respectively. Beyond the six factors in this study, other unanalyzed factors (such as lake depth and soil texture) also had some impacts on the distribution of TSS in the study area.
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Affiliation(s)
- Yunxia Du
- Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China; Hainan Normal University, Haikou 571158, China
| | - Kaishan Song
- Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China; School of Environment and Planning, Liaocheng University, Liaocheng, 252000, China.
| | - Qiang Wang
- Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China
| | - Sijia Li
- Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China
| | - Zhidan Wen
- Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China
| | - Ge Liu
- Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China
| | - Hui Tao
- Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yingxin Shang
- Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China
| | - Junbin Hou
- Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China
| | - Lili Lyu
- Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China
| | - Bai Zhang
- Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China
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