Wetland Mapping Under Boundary Uncertainty with AlphaEarth Embeddings, LiDAR and Sentinel

Wetlands are important for water control, carbon storage, biodiversity and climate resilience, but they are difficult to map because their edges are often gradual and mixed with forest, shrubland or upland. The main problem is that many wetland maps use government polygon inventories as if every 10 m pixel inside or outside a polygon were perfectly correct. In practice, pixels near polygon boundaries can be uncertain, and this can affect both model training and accuracy assessment. It should be noted that another practical problem is data preparation: conventional wetland mapping often needs long satellite time series, cloud masking and many processing steps before a model can be trained. In this study, we address these two problems by using a boundary aware 10 m wetland mapping workflow for the 2024 GeoNB LiDAR covered part of New Brunswick, Canada. We used 64 AlphaEarth satellite embedding bands as the main predictor group, together with LiDAR terrain variables and Sentinel 2 spectral bands. We also removed a 20 m zone around wetland polygon edges from training and testing so that uncertain boundary pixels did not control the results. Two models were tested: an ExtraTrees classifier and an Attention U Net segmentation model. The ExtraTrees model reached a wetland F1 score of 75.17% and an overall accuracy of 96.51%. The Attention U Net model reached a wetland F1 score of 75.78% and an overall accuracy of 96.92%. These scores should be read within the evaluation setup used for each model, since the ExtraTrees metrics were computed on the full mapped domain and the Attention U Net metrics were computed on the held out test area. AlphaEarth variables appeared in 19 of the 20 most important ExtraTrees predictors, although these latent variables should not be read as direct physical measurements. The AlphaEarth only Attention U Net result was also close to the LiDAR only result. Both final maps were conservative in area: ExtraTrees mapped 6.75% wetland and Attention U Net mapped 6.38%, compared with the 7.29% buffered reference. On the basis of these results, AlphaEarth embeddings appear to carry useful wetland information while reducing the need for long satellite image collection, cloud masking and compositing. The study also shows that removing uncertain polygon edge pixels gives a clearer and more defensible way to evaluate wetland maps made from government inventory data.