Evaluating methods for high‐resolution, national‐scale seagrass mapping in Google Earth Engine

Abstract National‐scale benthic marine habitat maps underpin monitoring and conservation of vulnerable marine and coastal ecosystems. Cloud‐based satellite remote sensing can streamline these processes over spatial scales that would otherwise be financially and logistically challenging. Here, we test the sensitivity of mapped outputs to three key methodological choices when generating open‐source cloud‐based satellite maps of seagrass meadows: (1) period of image retrieval (seasonality, tested at n = 7 sites over n = 5 years); (2) machine learning classification method (SVM, RF, CART) over a range of training pixel densities ( n = 12 points with 0.0004–0.8757 training points/km 2 ) and (3) input satellite data choice ( n = 3: Landsat 8, Planet NICFI and Sentinel‐2). We found that in the Maldives, when using best available cloud masking methods, monsoonal cloud patterns introduce noise into satellite images, with implications for mapping accuracy. Comparing methods at the classification phase, Overall Accuracy (OA) was similar between classification methods, though SVM performed best (OA = 84.6%). We also determined that workflows using data derived from Sentinel‐2 resulted in the most accurate binary thematic seagrass map (OA = 80.3%), compared to Landsat 8 and Planet NICFI (OA = 72.7 and 74.8%, respectively). These results indicate that data source has a larger effect on OA than classifier type, and therefore should be the primary consideration for map producers. We further recommend that, as studies increasingly work over larger extents (i.e. >1,000 km 2 ), the minimum density of points used to train a binary classification of seagrass from Sentinel‐2 data ought to be 0.67/km 2 . We present an open‐source (for non‐commercial uses) workflow for generating high‐resolution national‐scale seagrass maps. Insights from this work can be applied in other settings globally to improve outcomes for marine planning and international targets on climate change and the conservation of biodiversity.