Scaling up benthic primary productivity estimates in a large intertidal estuary using remote sensing

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As two main primary producers in temperate intertidal regions, seagrass and microphytobenthos (MPB) support estuarine ecosystem functions in multiple ways, such as by stabilizing food webs and regulating sediment resuspension. Monitoring estuary productivity at large scales can inform how whole ecosystems respond to environmental stressors (e.g. climate change, pollution and habitat degradation).

Here we use a case study to show that Sentinel-2 data can be used to estimate estuary-wide emerged and submerged gross primary productivity (GPP) by coupling a new artificial intelligence model to map seagrass and unvegetated tidal flats with literature-derived photosynthesis-irradiance (P-I) relationships. The model consisted of (1) supervised classification with random forest to delineate seagrass and unvegetated areas and (2) artificial neural network (ANN) regression to predict the coverage of seagrass.

This model can be used to predict future habitats of GPP and MPB in this estuary with sea level rise models. The model can be used to inform sea level rise projections, which show that the future state will be very sensitive to turbidity. Therefore, controlling water turbidity might be an efficient solution to maintaining the current GPP as sea level rises, at least in this case-study harbour.