Using sentinel-2 satellite imagery to develop microphytobenthos-based water quality indices in estuaries

摘要: Abstract Microphytobenthos (MPB) is composed of unicellular photosynthetic organisms that colonize intertidal sediments within the first millimeters photic zone and form biofilms at low tide. In estuaries, this benthic group can represent main primary producer deliver several ecosystem services. However, it not currently used as a bioindicator water quality, contrary to widespread use phytobenthos in freshwater settings. This study assesses potential developing MBP metrics assess quality transitional waters using Sentinel-2 (S2) satellite imagery. A Random Forest machine learning classification was distinguish different types vegetation 26 French estuaries bays, particular MPB green macroalgae (chlorophytes), multispectral indices. High accuracy generally achieved for identification when compared with field validation data, both User’s Producer’s accuracies, which corresponded 94% 84% respectively. Two Earth observation variables were retrieved: Normalized Difference Vegetation Index (NDVI), proxy biomass, percent cover integrated over entire area body. From total 918 S2 images from full year, 28% exploitable due combined requirements cloud-free pixels collected during With its 10 m spatial resolution, able map all estuaries. showed stronger gradient between than NDVI. also significantly correlated cover, characterized by highest coverage eutrophic sites impacted intensive farming activities. multivariate analysis confirmed indeed related nutrients. It sediment type one factors underlying differences work step toward metric MPB, recommendations are proposed refine approach. imagery, publicly available, presents an interesting compromise estuarine microphytobenthos order ecological status waters.