Site selection is a vital step in starting a successful aquaculture bu siness. Bivalve growth is largely controlled by temperature and food (phytoplankton or suspended organic matter). However, the tools and methods that are required to measure these parameters over a typical growing season are often expensive and time consuming. Site selection can be especially difficult in Maine, where the coastline is convoluted . Most Maine aquaculture occurs in bays and estuaries that have vastly different environmental parameters despite close proximity to each other. Our work reduces the barriers of site selection by coupling high-resolution satellite data from Landsat 8 and Sentinel 2A/B with a dynamic energy budget (DEB) oyster growth model . The high spatial resolution as well as short revisit times of these satellites offer unprecedented farm scale site characterization.
Landsat 8 temperature products are provided at a 100 m resolution with a 16-day revisit time, while chlorophyll and turbidity can be derived from Sentinel 2 at 20 m resolution at a 5-day revisit time (Figure 1) . Previous work validated satellite products on a network of monitoring buoys along the coast of Maine. To validate the growth model, w e tracked tissue and shell growth of oysters at four farms with varying food and temperature conditions over 6 months. Predicted growth from DEB models run on hourly in situ data collected at the study sites were compared to observed growth. The final model was applied to monthly climatologies (2016 – 2020) of chlorophyll from Sentinel 2 (Figure 1) and daily climatologies of temperature (2013 – 2020) from Landsat 8 to predict time to market. While this work focuses on reducing the barriers for expansion of oyster aquaculture in Maine, it has the potential to inform siting of other emerging culture species such as sea scallops or established species in different environments such as offshore locations.