{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:40:04Z","timestamp":1760233204983,"version":"build-2065373602"},"reference-count":89,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T00:00:00Z","timestamp":1671667200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2022YFC3104901","GML2019ZD0602","2021CXLH0014"],"award-info":[{"award-number":["2022YFC3104901","GML2019ZD0602","2021CXLH0014"]}]},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)","award":["2022YFC3104901","GML2019ZD0602","2021CXLH0014"],"award-info":[{"award-number":["2022YFC3104901","GML2019ZD0602","2021CXLH0014"]}]},{"name":"Hainan Province Science and Technology Plan Sanya Yazhou Bay Science and Technology City Science and Technology Innovation Joint Project","award":["2022YFC3104901","GML2019ZD0602","2021CXLH0014"],"award-info":[{"award-number":["2022YFC3104901","GML2019ZD0602","2021CXLH0014"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Biosiliceous sedimentation, closely related to carbon sedimentation in water, has a significant impact on the marine biogeochemical cycle. However, large-scale monitoring data are scarce due to the constraints of biosiliceous sedimentation flux (BSF) gathering methods. There are few reports on the spatiotemporal variation of BSF in estuaries and offshore waters. Additionally, few studies have used satellite remote sensing methods to retrieve BSF. In the paper, satellite images from 2000 to 2020 were used for the first time to estimate the BSF distribution of the Pearl River Estuary (PRE) over the past 20 years, based on a remote sensing model combined with particulate organic carbon (POC) deposition data and water depth data. The results showed that the BSF ranged from 100 to 2000 mg\/(m2 \u00d7 d). The accuracy tests indicated that the correlation coefficient (R2) and significance (P) of Pearson correlation analysis were 0.8787 and 0.0018, respectively. The BSF value varied seasonally and increased every year. The BSF did not follow a simple trend of decreasing along the coast to open water. Shenzhen Bay (SZB) generally had a higher BSF value than the Dragon\u2019s Den Waterway (DDW). The BSF in autumn and winter was investigated using empirical orthogonal function analysis (EOF). In autumn, the BSF of the PRE\u2019s eastern bank showed little change, while the BSF of the western bank showed obvious differences. In winter, the BSF in Hong Kong waters and inlet shoals fluctuated less, whereas the BSF in DDW and Lingding Waterway (LW) fluctuated more. The grey correlation analysis (GRA) identified two factors affecting BSF: chromophoric dissolved organic matter (CDOM) and total suspended solids (TSS). Most BSF were primarily affected by TSS during winter. In spring, the two effects were balanced. TSS affected the east coast in summer, and CDOM was the dominant effect in autumn. Four main parameters influencing the distribution of BSF in the PRE were analyzed: ecosystem, reef, flow field and flocculation. This study showed that using satellite remote sensing to estimate BSF has excellent potential, which is worthy of further discussion in terms of spatiotemporal resolution and model optimization.<\/jats:p>","DOI":"10.3390\/rs15010058","type":"journal-article","created":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T03:26:25Z","timestamp":1671765985000},"page":"58","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["First Estimate Biosiliceous Sedimentation Flux in the Pearl River Estuary from 2000\u20132020 by Satellite Remote Sensing"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7606-6389","authenticated-orcid":false,"given":"Rong","family":"Zhong","sequence":"first","affiliation":[{"name":"State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Dingtian","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China"},{"name":"Sanya Institute of Ocean Eco-Environmental Engineering, Sanya 572000, China"}]},{"given":"Linhong","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Xiaoqing","family":"Yin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,22]]},"reference":[{"key":"ref_1","unstructured":"Trujillo, A.P., and Thurman, H.V. 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