Mapping subtidal seagrass in the turbid Baltic Sea: Rethinking satellite sensor selection using a sensor-agnostic pipeline - Eike M. Schütt; Florian Uhl; Philipp R. Schubert; Thorsten B.H. Reusch; Natascha Oppelt (Science of Remote Sensing)
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.