Mapping subtidal seagrass in the turbid Baltic Sea: Rethinking satellite sensor selection using a sensor-agnostic pipeline
Conservation and restoration of seagrass ecosystems critically depend on accurate habitat distribution data. Traditional monitoring methods for subtidal seagrass meadows are costly and labour-intensive for large-scale, repetitive mapping efforts. Satellite Earth Observation (EO) offer a more cost-ef...
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Elsevier
2025-06-01
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| Series: | Science of Remote Sensing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666017225000495 |
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| author | Eike M. Schütt Florian Uhl Philipp R. Schubert Thorsten B.H. Reusch Natascha Oppelt |
| author_facet | Eike M. Schütt Florian Uhl Philipp R. Schubert Thorsten B.H. Reusch Natascha Oppelt |
| author_sort | Eike M. Schütt |
| collection | DOAJ |
| description | Conservation and restoration of seagrass ecosystems critically depend on accurate habitat distribution data. Traditional monitoring methods for subtidal seagrass meadows are costly and labour-intensive for large-scale, repetitive mapping efforts. Satellite Earth Observation (EO) offer a more cost-effective alternative, but best practices for EO-based seagrass monitoring are lacking. Selecting the most suitable satellite sensor is particularly challenging and requires balancing budget and sensor specifications. The growing number of commercial, very high-resolution (<10 m) sensors further complicates this decision. To address this, we evaluate the suitability of four commonly used satellite sensors (Pléiades-1, WorldView-3, Planet SuperDove, and Sentinel-2) across seven configurations (single scenes, compositing, de-striping) for key seagrass monitoring tasks, including detecting submerged aquatic vegetation, differentiating seagrass from algae, and estimating seagrass cover. Using a sensor-agnostic pipeline for preprocessing, feature selection, and XGBoost model training, we show that Sentinel-2 and Planet SuperDove face limitations in capturing fine-scale meadow fragmentation due to their large Spatial Resolution Distance. In contrast, WorldView-3 and especially Pléiades-1 resolve meter-scale habitat fragmentation. However, Sentinel-2 and Planet SuperDove remain valuable for large-scale assessments due to significant price-per-area advantages. For spatially aggregated metrics like total vegetation cover or biomass, these sensors can provide reliable estimates when mixed-pixel effects are considered. Our results highlight the need for different preprocessing steps, such as water column correction, segmentation, compositing and de-striping, to maximize model performance based on sensor characteristics. Nevertheless, none of our tested sensors could distinguish seagrass from algae, highlighting a significant challenge for advancing EO-based seagrass monitoring in the future. |
| format | Article |
| id | doaj-art-31b5c84b1f2c435c9b0bac0a5cf0963a |
| institution | Kabale University |
| issn | 2666-0172 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
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| series | Science of Remote Sensing |
| spelling | doaj-art-31b5c84b1f2c435c9b0bac0a5cf0963a2025-08-20T03:47:20ZengElsevierScience of Remote Sensing2666-01722025-06-011110024310.1016/j.srs.2025.100243Mapping subtidal seagrass in the turbid Baltic Sea: Rethinking satellite sensor selection using a sensor-agnostic pipelineEike M. Schütt0Florian Uhl1Philipp R. Schubert2Thorsten B.H. Reusch3Natascha Oppelt4Earth Observation and Modelling, Department of Geography, Kiel University, Kiel, 24118, Schleswig-Holstein, Germany; Corresponding author.Earth Observation and Modelling, Department of Geography, Kiel University, Kiel, 24118, Schleswig-Holstein, GermanyMarine Evolutionary Ecology, GEOMAR Helmholtz Center for Ocean Research, Kiel, 24148, Schleswig-Holstein, GermanyMarine Evolutionary Ecology, GEOMAR Helmholtz Center for Ocean Research, Kiel, 24148, Schleswig-Holstein, GermanyEarth Observation and Modelling, Department of Geography, Kiel University, Kiel, 24118, Schleswig-Holstein, GermanyConservation and restoration of seagrass ecosystems critically depend on accurate habitat distribution data. Traditional monitoring methods for subtidal seagrass meadows are costly and labour-intensive for large-scale, repetitive mapping efforts. Satellite Earth Observation (EO) offer a more cost-effective alternative, but best practices for EO-based seagrass monitoring are lacking. Selecting the most suitable satellite sensor is particularly challenging and requires balancing budget and sensor specifications. The growing number of commercial, very high-resolution (<10 m) sensors further complicates this decision. To address this, we evaluate the suitability of four commonly used satellite sensors (Pléiades-1, WorldView-3, Planet SuperDove, and Sentinel-2) across seven configurations (single scenes, compositing, de-striping) for key seagrass monitoring tasks, including detecting submerged aquatic vegetation, differentiating seagrass from algae, and estimating seagrass cover. Using a sensor-agnostic pipeline for preprocessing, feature selection, and XGBoost model training, we show that Sentinel-2 and Planet SuperDove face limitations in capturing fine-scale meadow fragmentation due to their large Spatial Resolution Distance. In contrast, WorldView-3 and especially Pléiades-1 resolve meter-scale habitat fragmentation. However, Sentinel-2 and Planet SuperDove remain valuable for large-scale assessments due to significant price-per-area advantages. For spatially aggregated metrics like total vegetation cover or biomass, these sensors can provide reliable estimates when mixed-pixel effects are considered. Our results highlight the need for different preprocessing steps, such as water column correction, segmentation, compositing and de-striping, to maximize model performance based on sensor characteristics. Nevertheless, none of our tested sensors could distinguish seagrass from algae, highlighting a significant challenge for advancing EO-based seagrass monitoring in the future.http://www.sciencedirect.com/science/article/pii/S2666017225000495SeagrassSubmerged aquatic vegetationMappingSensor comparisonXGBoostSHAP |
| spellingShingle | Eike M. Schütt Florian Uhl Philipp R. Schubert Thorsten B.H. Reusch Natascha Oppelt Mapping subtidal seagrass in the turbid Baltic Sea: Rethinking satellite sensor selection using a sensor-agnostic pipeline Science of Remote Sensing Seagrass Submerged aquatic vegetation Mapping Sensor comparison XGBoost SHAP |
| title | Mapping subtidal seagrass in the turbid Baltic Sea: Rethinking satellite sensor selection using a sensor-agnostic pipeline |
| title_full | Mapping subtidal seagrass in the turbid Baltic Sea: Rethinking satellite sensor selection using a sensor-agnostic pipeline |
| title_fullStr | Mapping subtidal seagrass in the turbid Baltic Sea: Rethinking satellite sensor selection using a sensor-agnostic pipeline |
| title_full_unstemmed | Mapping subtidal seagrass in the turbid Baltic Sea: Rethinking satellite sensor selection using a sensor-agnostic pipeline |
| title_short | Mapping subtidal seagrass in the turbid Baltic Sea: Rethinking satellite sensor selection using a sensor-agnostic pipeline |
| title_sort | mapping subtidal seagrass in the turbid baltic sea rethinking satellite sensor selection using a sensor agnostic pipeline |
| topic | Seagrass Submerged aquatic vegetation Mapping Sensor comparison XGBoost SHAP |
| url | http://www.sciencedirect.com/science/article/pii/S2666017225000495 |
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