Assessment of GCOM-C Satellite Imagery in Bloom Detection: A Case Study in the East China Sea
The coast of the East China Sea (ECS) is one of the regions most frequently affected by harmful algal blooms in China. Remote sensing monitoring could assist in understanding the mechanism of blooms and their associated environmental changes. Based on imagery from the Second-Generation Global Imager...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/3/691 |
| _version_ | 1797623357591519232 |
|---|---|
| author | Chi Feng Yuanli Zhu Anglu Shen Changpeng Li Qingjun Song Bangyi Tao Jiangning Zeng |
| author_facet | Chi Feng Yuanli Zhu Anglu Shen Changpeng Li Qingjun Song Bangyi Tao Jiangning Zeng |
| author_sort | Chi Feng |
| collection | DOAJ |
| description | The coast of the East China Sea (ECS) is one of the regions most frequently affected by harmful algal blooms in China. Remote sensing monitoring could assist in understanding the mechanism of blooms and their associated environmental changes. Based on imagery from the Second-Generation Global Imager (SGLI) conducted by Global Change Observation Mission-Climate (GCOM-C) (Japan), the accuracy of satellite measurements was initially validated using matched pairs of satellite and ground data relating to the ECS. Additionally, using SGLI data from the coast of the ECS, we compared the applicability of three bloom extraction methods: spectral shape, red tide index, and algal bloom ratio. With an RMSE of less than 25%, satellite data at 490 nm, 565 nm, and 670 nm showed good consistency with locally measured remote sensing reflectance data. However, there was unexpected overestimation at 443 nm of SGLI data. By using a linear correction method, the RMSE at 443 nm was decreased from 27% to 17%. Based on the linear corrected SGLI data, the spectral shape at 490 nm was found to provide the most satisfactory results in separating bloom and non-bloom waters among the three bloom detection methods. In addition, the capability in harmful algae distinguished using SGLI data was discussed. Both of the Bloom Index method and the green-red Spectral Slope method were found to be applicable for phytoplankton classification using SGLI data. Overall, the SGLI data provided by GCOM-C are consistent with local data and can be used to identify bloom water bodies in the ECS, thereby providing new satellite data to support monitoring of bloom changes in the ECS. |
| first_indexed | 2024-03-11T09:27:46Z |
| format | Article |
| id | doaj.art-d54597f7d1414d0eb9ce1c55f8fac8de |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T09:27:46Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d54597f7d1414d0eb9ce1c55f8fac8de2023-11-16T17:52:52ZengMDPI AGRemote Sensing2072-42922023-01-0115369110.3390/rs15030691Assessment of GCOM-C Satellite Imagery in Bloom Detection: A Case Study in the East China SeaChi Feng0Yuanli Zhu1Anglu Shen2Changpeng Li3Qingjun Song4Bangyi Tao5Jiangning Zeng6School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaKey Laboratory of Marine Ecosystem Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaCollege of Marine Ecology and Environment, Shanghai Ocean University, Shanghai 201306, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaNational Satellite Ocean Application Service, Ministry of Natural Resources of the People’s Republic of China, Beijing 100081, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaKey Laboratory of Marine Ecosystem Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaThe coast of the East China Sea (ECS) is one of the regions most frequently affected by harmful algal blooms in China. Remote sensing monitoring could assist in understanding the mechanism of blooms and their associated environmental changes. Based on imagery from the Second-Generation Global Imager (SGLI) conducted by Global Change Observation Mission-Climate (GCOM-C) (Japan), the accuracy of satellite measurements was initially validated using matched pairs of satellite and ground data relating to the ECS. Additionally, using SGLI data from the coast of the ECS, we compared the applicability of three bloom extraction methods: spectral shape, red tide index, and algal bloom ratio. With an RMSE of less than 25%, satellite data at 490 nm, 565 nm, and 670 nm showed good consistency with locally measured remote sensing reflectance data. However, there was unexpected overestimation at 443 nm of SGLI data. By using a linear correction method, the RMSE at 443 nm was decreased from 27% to 17%. Based on the linear corrected SGLI data, the spectral shape at 490 nm was found to provide the most satisfactory results in separating bloom and non-bloom waters among the three bloom detection methods. In addition, the capability in harmful algae distinguished using SGLI data was discussed. Both of the Bloom Index method and the green-red Spectral Slope method were found to be applicable for phytoplankton classification using SGLI data. Overall, the SGLI data provided by GCOM-C are consistent with local data and can be used to identify bloom water bodies in the ECS, thereby providing new satellite data to support monitoring of bloom changes in the ECS.https://www.mdpi.com/2072-4292/15/3/691bloom detectionremote sensingEast China SeaGCOM-C |
| spellingShingle | Chi Feng Yuanli Zhu Anglu Shen Changpeng Li Qingjun Song Bangyi Tao Jiangning Zeng Assessment of GCOM-C Satellite Imagery in Bloom Detection: A Case Study in the East China Sea Remote Sensing bloom detection remote sensing East China Sea GCOM-C |
| title | Assessment of GCOM-C Satellite Imagery in Bloom Detection: A Case Study in the East China Sea |
| title_full | Assessment of GCOM-C Satellite Imagery in Bloom Detection: A Case Study in the East China Sea |
| title_fullStr | Assessment of GCOM-C Satellite Imagery in Bloom Detection: A Case Study in the East China Sea |
| title_full_unstemmed | Assessment of GCOM-C Satellite Imagery in Bloom Detection: A Case Study in the East China Sea |
| title_short | Assessment of GCOM-C Satellite Imagery in Bloom Detection: A Case Study in the East China Sea |
| title_sort | assessment of gcom c satellite imagery in bloom detection a case study in the east china sea |
| topic | bloom detection remote sensing East China Sea GCOM-C |
| url | https://www.mdpi.com/2072-4292/15/3/691 |
| work_keys_str_mv | AT chifeng assessmentofgcomcsatelliteimageryinbloomdetectionacasestudyintheeastchinasea AT yuanlizhu assessmentofgcomcsatelliteimageryinbloomdetectionacasestudyintheeastchinasea AT anglushen assessmentofgcomcsatelliteimageryinbloomdetectionacasestudyintheeastchinasea AT changpengli assessmentofgcomcsatelliteimageryinbloomdetectionacasestudyintheeastchinasea AT qingjunsong assessmentofgcomcsatelliteimageryinbloomdetectionacasestudyintheeastchinasea AT bangyitao assessmentofgcomcsatelliteimageryinbloomdetectionacasestudyintheeastchinasea AT jiangningzeng assessmentofgcomcsatelliteimageryinbloomdetectionacasestudyintheeastchinasea |