Remote Sensing Estimates of Particulate Organic Carbon Sources in the Zhanjiang Bay Using Sentinel-2 Data and Carbon Isotopes
The source information of coastal particulate organic carbon (POC) with high spatial and temporal resolution is of great significance for the study of marine carbon cycles and marine biogeochemical processes. Over the past decade, satellite ocean color remote sensing has greatly improved our underst...
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| Format: | Article |
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MDPI AG
2023-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/15/3768 |
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| author | Guo Yu Yafeng Zhong Sihai Liu Qibin Lao Chunqing Chen Dongyang Fu Fajin Chen |
| author_facet | Guo Yu Yafeng Zhong Sihai Liu Qibin Lao Chunqing Chen Dongyang Fu Fajin Chen |
| author_sort | Guo Yu |
| collection | DOAJ |
| description | The source information of coastal particulate organic carbon (POC) with high spatial and temporal resolution is of great significance for the study of marine carbon cycles and marine biogeochemical processes. Over the past decade, satellite ocean color remote sensing has greatly improved our understanding of the spatiotemporal dynamics of ocean particulate organic carbon concentrations. However, due to the complexity of coastal POC sources, remote sensing methods for coastal POC sources have not yet been established. With an attempt to fill the gap, this study developed an algorithm for retrieving coastal POC sources using remote sensing and geochemical isotope technology. The isotope end-member mixing model was used to calculate the proportion of POC sources, and the response relationship between POC source information and in situ remote sensing reflectance (<i>R<sub>rs</sub></i>) was established to develop a retrieval algorithm for POC sources with the following four bands: (<i>R<sub>rs</sub></i>(443)/<i>R<sub>rs</sub></i>(492)) × (<i>R<sub>rs</sub></i>(704)/<i>R<sub>rs</sub></i>(665)). The results showed that the four-band algorithm performed well with R<sup>2</sup>, mean absolute percentage error (MAPE) and root mean square error (RMSE) values of 0.78, 33.57% and 13.74%, respectively. Validation against in situ data showed that the four-band algorithm derived calculated the proportion of marine POC accurately, with an MAPE and RMSE of 27.49% and 13.58%, respectively. The accuracy of the algorithm was verified based on the Sentinel-2 data, with an MAPE and RMSE of 28.02% and 15.72%, respectively. Additionally, we found that the proportion of marine POC sources was higher outside the Zhanjiang Bay than inside it using in situ survey data, which was consistent with the retrieved results. Influencing factors of POC sources may be due to the occurrence of phytoplankton blooms outside the bay and the impact of terrestrial inputs inside the bay. Remote sensing in combination with carbon isotopes provides important technical assistance in comprehending the biogeochemical process of POC and uncovering spatiotemporal variations in POC sources and their underlying causes. |
| first_indexed | 2024-03-11T00:17:27Z |
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| id | doaj.art-618e888b2a7c492193471cd8b53f0331 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T00:17:27Z |
| publishDate | 2023-07-01 |
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| series | Remote Sensing |
| spelling | doaj.art-618e888b2a7c492193471cd8b53f03312023-11-18T23:30:32ZengMDPI AGRemote Sensing2072-42922023-07-011515376810.3390/rs15153768Remote Sensing Estimates of Particulate Organic Carbon Sources in the Zhanjiang Bay Using Sentinel-2 Data and Carbon IsotopesGuo Yu0Yafeng Zhong1Sihai Liu2Qibin Lao3Chunqing Chen4Dongyang Fu5Fajin Chen6College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, ChinaCollege of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, ChinaCollege of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, ChinaCollege of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, ChinaCollege of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, ChinaCollege of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, ChinaCollege of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, ChinaThe source information of coastal particulate organic carbon (POC) with high spatial and temporal resolution is of great significance for the study of marine carbon cycles and marine biogeochemical processes. Over the past decade, satellite ocean color remote sensing has greatly improved our understanding of the spatiotemporal dynamics of ocean particulate organic carbon concentrations. However, due to the complexity of coastal POC sources, remote sensing methods for coastal POC sources have not yet been established. With an attempt to fill the gap, this study developed an algorithm for retrieving coastal POC sources using remote sensing and geochemical isotope technology. The isotope end-member mixing model was used to calculate the proportion of POC sources, and the response relationship between POC source information and in situ remote sensing reflectance (<i>R<sub>rs</sub></i>) was established to develop a retrieval algorithm for POC sources with the following four bands: (<i>R<sub>rs</sub></i>(443)/<i>R<sub>rs</sub></i>(492)) × (<i>R<sub>rs</sub></i>(704)/<i>R<sub>rs</sub></i>(665)). The results showed that the four-band algorithm performed well with R<sup>2</sup>, mean absolute percentage error (MAPE) and root mean square error (RMSE) values of 0.78, 33.57% and 13.74%, respectively. Validation against in situ data showed that the four-band algorithm derived calculated the proportion of marine POC accurately, with an MAPE and RMSE of 27.49% and 13.58%, respectively. The accuracy of the algorithm was verified based on the Sentinel-2 data, with an MAPE and RMSE of 28.02% and 15.72%, respectively. Additionally, we found that the proportion of marine POC sources was higher outside the Zhanjiang Bay than inside it using in situ survey data, which was consistent with the retrieved results. Influencing factors of POC sources may be due to the occurrence of phytoplankton blooms outside the bay and the impact of terrestrial inputs inside the bay. Remote sensing in combination with carbon isotopes provides important technical assistance in comprehending the biogeochemical process of POC and uncovering spatiotemporal variations in POC sources and their underlying causes.https://www.mdpi.com/2072-4292/15/15/3768remote sensinggeochemical isotopePOC sourcesempirical algorithmZhanjiang Bay |
| spellingShingle | Guo Yu Yafeng Zhong Sihai Liu Qibin Lao Chunqing Chen Dongyang Fu Fajin Chen Remote Sensing Estimates of Particulate Organic Carbon Sources in the Zhanjiang Bay Using Sentinel-2 Data and Carbon Isotopes Remote Sensing remote sensing geochemical isotope POC sources empirical algorithm Zhanjiang Bay |
| title | Remote Sensing Estimates of Particulate Organic Carbon Sources in the Zhanjiang Bay Using Sentinel-2 Data and Carbon Isotopes |
| title_full | Remote Sensing Estimates of Particulate Organic Carbon Sources in the Zhanjiang Bay Using Sentinel-2 Data and Carbon Isotopes |
| title_fullStr | Remote Sensing Estimates of Particulate Organic Carbon Sources in the Zhanjiang Bay Using Sentinel-2 Data and Carbon Isotopes |
| title_full_unstemmed | Remote Sensing Estimates of Particulate Organic Carbon Sources in the Zhanjiang Bay Using Sentinel-2 Data and Carbon Isotopes |
| title_short | Remote Sensing Estimates of Particulate Organic Carbon Sources in the Zhanjiang Bay Using Sentinel-2 Data and Carbon Isotopes |
| title_sort | remote sensing estimates of particulate organic carbon sources in the zhanjiang bay using sentinel 2 data and carbon isotopes |
| topic | remote sensing geochemical isotope POC sources empirical algorithm Zhanjiang Bay |
| url | https://www.mdpi.com/2072-4292/15/15/3768 |
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