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Lei S, Xu J, Li Y, Du C, Liu G, Zheng Z, Xu Y, Lyu H, Mu M, Miao S, Zeng S, Xu J, Li L. An approach for retrieval of horizontal and vertical distribution of total suspended matter concentration from GOCI data over Lake Hongze. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 700:134524. [PMID: 31693957 DOI: 10.1016/j.scitotenv.2019.134524] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 09/14/2019] [Accepted: 09/16/2019] [Indexed: 06/10/2023]
Abstract
There are a few studies working on the vertical distribution of TSM, however, understanding the underwater profile of TSM is of great benefit to the study of biogeochemical processes in the water column that still require further research. In this study, three data-gathering expeditions were conducted in Lake Hongze (HZL), China, between 2016 and 2018. Based on the in situ optical and biological data, a multivariate linear stepwise regression method was applied for retrieval of the surface horizontal distribution of TSM (TSM0.2) using GOCI (Geostationary Ocean Color Imager) data. Then, the estimation model of vertical structure of underwater TSM was constructed using layer-by-layer recursion. This study drew several crucial findings: (1) the approach proposed in this paper generated very high goodness of fit results, with determination coefficients (R2) of 0.83 (p < 0.001, N = 54), and with smaller prediction errors (the mean absolute percentage error is determined to be 16.34%, the root mean square error is 9.01 mg l-1, and the mean ratio is 1.00, N = 26). (2) The monthly surface TSM and the column mass of suspended matter (CMSM) are affected by both wind speed and precipitation in HZL. In addition, the hourly variation of surface TSM and CMSM are driven by local wind, most especially in spring and winter. (3) Compared with the non-uniform hypothesis, the CMSM derived by conventional vertical uniformity hypothesis was underestimated by almost 10% in HZL during 2016. This should warrant the attention of lake managers and lake environmental evolution researchers.
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Affiliation(s)
- Shaohua Lei
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Key Laboratory of Virtual Geographical Environment of Ministry of Education, College of Geographical Science, Nanjing Normal University, Nanjing 210023, China
| | - Jie Xu
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Key Laboratory of Virtual Geographical Environment of Ministry of Education, College of Geographical Science, Nanjing Normal University, Nanjing 210023, China
| | - Yunmei Li
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Key Laboratory of Virtual Geographical Environment of Ministry of Education, College of Geographical Science, Nanjing Normal University, Nanjing 210023, China.
| | - Chenggong Du
- Jiangsu Collaborative Innovation Center of Regional Modern Agriculture & Environmental Protection, Huaiyin Normal University, Huaian 223300, China
| | - Ge Liu
- Northeast Institute of Geography and Agricultural Ecology, Chinese Academy of Sciences, Changchun 130102, China
| | - Zhubin Zheng
- School of Geography and Environmental Engineering, Gannan Normal University, Ganzhou 341000, China
| | - Yifan Xu
- State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
| | - Heng Lyu
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Key Laboratory of Virtual Geographical Environment of Ministry of Education, College of Geographical Science, Nanjing Normal University, Nanjing 210023, China
| | - Meng Mu
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Key Laboratory of Virtual Geographical Environment of Ministry of Education, College of Geographical Science, Nanjing Normal University, Nanjing 210023, China
| | - Song Miao
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Key Laboratory of Virtual Geographical Environment of Ministry of Education, College of Geographical Science, Nanjing Normal University, Nanjing 210023, China
| | - Shuai Zeng
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Key Laboratory of Virtual Geographical Environment of Ministry of Education, College of Geographical Science, Nanjing Normal University, Nanjing 210023, China
| | - Jiafeng Xu
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Key Laboratory of Virtual Geographical Environment of Ministry of Education, College of Geographical Science, Nanjing Normal University, Nanjing 210023, China
| | - Lingling Li
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Key Laboratory of Virtual Geographical Environment of Ministry of Education, College of Geographical Science, Nanjing Normal University, Nanjing 210023, China
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Reynolds RA, Stramski D. Optical characterization of marine phytoplankton assemblages within surface waters of the western Arctic Ocean. LIMNOLOGY AND OCEANOGRAPHY 2019; 64:2478-2496. [PMID: 31894158 PMCID: PMC6919934 DOI: 10.1002/lno.11199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 04/17/2019] [Accepted: 04/29/2019] [Indexed: 06/10/2023]
Abstract
An extensive data set of measurements within the Chukchi and Beaufort Seas is used to characterize the optical properties of seawater associated with different phytoplankton communities. Hierarchical cluster analysis of diagnostic pigment concentrations partitioned stations into four distinct surface phytoplankton communities based on taxonomic composition and average cell size. Concurrent optical measurements of spectral absorption and backscattering coefficients and remote-sensing reflectance were used to characterize the magnitudes and spectral shapes of seawater optical properties associated with each phytoplankton assemblage. The results demonstrate measurable differences among communities in the average spectral shapes of the phytoplankton absorption coefficient. Similar or smaller differences were also observed in the spectral shapes of nonphytoplankton absorption coefficients and the particulate backscattering coefficient. Phytoplankton on average, however, contributed only 25% or less to the total absorption coefficient of seawater. Our analyses indicate that the interplay between the magnitudes and relative contributions of all optically significant constituents generally dampens any influence of varying phytoplankton absorption spectral shapes on the total absorption coefficient, yet there is still a marked discrimination observed in the spectral shape of the ratio of the total backscattering to total absorption coefficient and remote-sensing reflectance among the phytoplankton assemblages. These spectral variations arise mainly from differences in the bio-optical environment in which specific communities were found, as opposed to differences in the spectral shapes of phytoplankton optical properties per se. These results suggest potential approaches for the development of algorithms to assess phytoplankton community composition from measurements of seawater optical properties in western Arctic waters.
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Affiliation(s)
- Rick A. Reynolds
- Marine Physical LaboratoryScripps Institution of Oceanography, University of California San DiegoLa JollaCalifornia
| | - Dariusz Stramski
- Marine Physical LaboratoryScripps Institution of Oceanography, University of California San DiegoLa JollaCalifornia
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