Comparing Cloud Mask Products for Seagrass Mapping Over Sentinel‐2 Imagery: Toward a First National Seagrass Map for Venezuela
Abstract Despite providing many valuable ecosystem services, seagrasses are a threatened habitat and their global distribution is not fully known. For example, Venezuela lacks a national seagrass map. An established regional mapping approach for seagrass exists for the Google Earth Engine (GEE) plat...
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Journal of Geophysical Research: Machine Learning and Computation |
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| Online Access: | https://doi.org/10.1029/2024JH000559 |
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| author | Chengfa Benjamin Lee Ana Carolina Peralta Brichtova Mar Roca Tylar Murray Oswaldo David Bolivar Rodriguez Daniele Cerra Frank E. Muller‐Karger |
| author_facet | Chengfa Benjamin Lee Ana Carolina Peralta Brichtova Mar Roca Tylar Murray Oswaldo David Bolivar Rodriguez Daniele Cerra Frank E. Muller‐Karger |
| author_sort | Chengfa Benjamin Lee |
| collection | DOAJ |
| description | Abstract Despite providing many valuable ecosystem services, seagrasses are a threatened habitat and their global distribution is not fully known. For example, Venezuela lacks a national seagrass map. An established regional mapping approach for seagrass exists for the Google Earth Engine (GEE) platform, but requires a long time window to obtain sufficient data to overcome cloud and other challenges. Recently, GEE has released a Cloud Score+ quality band product for the purpose of cloud masking. Cloud masking could potentially reduce the time window needed for a representative multitemporal composite, which would allow for temporal analyses. We compare the performance of Cloud Score+ derived products against previously established multitemporal image composites acquired in different time ranges, and the ACOLITE‐processed single image composite. The Sentinel‐2 (S2) Level‐1C (L1C) imagery for the whole Venezuelan coastline was processed following three different approaches: (a) using a multitemporal composition of the full S2 L1C archive available and processed in GEE using the Dark Object Subtraction; (b) integrating Cloud Score+ data set into the previous approach; and (c) using a single‐image offline approach applying ACOLITE atmospheric correction. Additional raster features were generated and a two‐step classification approach was performed with five classes, namely sand, seagrass, turbid water, deep water, and coral, and bootstrapped 20 times. Quantitatively, the performance within the Cloud Score+ derived products were largely similar. While the full archive approach had the best quantitative results, the ACOLITE approach produced the best maps qualitatively. With this, we produced the first national seagrass map for Venezuela. |
| format | Article |
| id | doaj-art-3020be3f06054228b6843c13c30aee4e |
| institution | Kabale University |
| issn | 2993-5210 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Geophysical Research: Machine Learning and Computation |
| spelling | doaj-art-3020be3f06054228b6843c13c30aee4e2025-08-20T03:27:37ZengWileyJournal of Geophysical Research: Machine Learning and Computation2993-52102025-06-0122n/an/a10.1029/2024JH000559Comparing Cloud Mask Products for Seagrass Mapping Over Sentinel‐2 Imagery: Toward a First National Seagrass Map for VenezuelaChengfa Benjamin Lee0Ana Carolina Peralta Brichtova1Mar Roca2Tylar Murray3Oswaldo David Bolivar Rodriguez4Daniele Cerra5Frank E. Muller‐Karger6German Aerospace Center (DLR) Remote Sensing Technology Institute (IMF) Berlin GermanyCollege of Marine Science Institute for Marine Remote Sensing University of South Florida St. Petersburg Florida USASpanish National Research Council (CSIC) Department of Ecology and Coastal Management Institute of Marine Sciences of Andalusia (ICMAN) Cadiz SpainCollege of Marine Science Institute for Marine Remote Sensing University of South Florida St. Petersburg Florida USAEnergy and Environment Unit Institute of Advanced Studies IDEA Caracas VenezuelaGerman Aerospace Center (DLR) Remote Sensing Technology Institute (IMF) Oberpfaffenhofen GermanyCollege of Marine Science Institute for Marine Remote Sensing University of South Florida St. Petersburg Florida USAAbstract Despite providing many valuable ecosystem services, seagrasses are a threatened habitat and their global distribution is not fully known. For example, Venezuela lacks a national seagrass map. An established regional mapping approach for seagrass exists for the Google Earth Engine (GEE) platform, but requires a long time window to obtain sufficient data to overcome cloud and other challenges. Recently, GEE has released a Cloud Score+ quality band product for the purpose of cloud masking. Cloud masking could potentially reduce the time window needed for a representative multitemporal composite, which would allow for temporal analyses. We compare the performance of Cloud Score+ derived products against previously established multitemporal image composites acquired in different time ranges, and the ACOLITE‐processed single image composite. The Sentinel‐2 (S2) Level‐1C (L1C) imagery for the whole Venezuelan coastline was processed following three different approaches: (a) using a multitemporal composition of the full S2 L1C archive available and processed in GEE using the Dark Object Subtraction; (b) integrating Cloud Score+ data set into the previous approach; and (c) using a single‐image offline approach applying ACOLITE atmospheric correction. Additional raster features were generated and a two‐step classification approach was performed with five classes, namely sand, seagrass, turbid water, deep water, and coral, and bootstrapped 20 times. Quantitatively, the performance within the Cloud Score+ derived products were largely similar. While the full archive approach had the best quantitative results, the ACOLITE approach produced the best maps qualitatively. With this, we produced the first national seagrass map for Venezuela.https://doi.org/10.1029/2024JH000559seagrass mappinggoogle earth enginecloud |
| spellingShingle | Chengfa Benjamin Lee Ana Carolina Peralta Brichtova Mar Roca Tylar Murray Oswaldo David Bolivar Rodriguez Daniele Cerra Frank E. Muller‐Karger Comparing Cloud Mask Products for Seagrass Mapping Over Sentinel‐2 Imagery: Toward a First National Seagrass Map for Venezuela Journal of Geophysical Research: Machine Learning and Computation seagrass mapping google earth engine cloud |
| title | Comparing Cloud Mask Products for Seagrass Mapping Over Sentinel‐2 Imagery: Toward a First National Seagrass Map for Venezuela |
| title_full | Comparing Cloud Mask Products for Seagrass Mapping Over Sentinel‐2 Imagery: Toward a First National Seagrass Map for Venezuela |
| title_fullStr | Comparing Cloud Mask Products for Seagrass Mapping Over Sentinel‐2 Imagery: Toward a First National Seagrass Map for Venezuela |
| title_full_unstemmed | Comparing Cloud Mask Products for Seagrass Mapping Over Sentinel‐2 Imagery: Toward a First National Seagrass Map for Venezuela |
| title_short | Comparing Cloud Mask Products for Seagrass Mapping Over Sentinel‐2 Imagery: Toward a First National Seagrass Map for Venezuela |
| title_sort | comparing cloud mask products for seagrass mapping over sentinel 2 imagery toward a first national seagrass map for venezuela |
| topic | seagrass mapping google earth engine cloud |
| url | https://doi.org/10.1029/2024JH000559 |
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