Matching the right species to the right location is critical for success of new aquaculture farms, particularly for non-fed species, such as oysters, where the farmer relies on the site for both appropriate temperatures and naturally occurring food. These conditions vary in both time and space and the equipment and means to measure conditions in several potential locations over the necessary timescales is out of reach of most farmers. Remote sensing from earth observing satellites can provide the necessary spatial-explicit time series data to reduce the risk in site selection. Thermal sensors can retrieve sea surface temperature (SST). Proxies for shellfish food, such as chlorophyll a, can be retrieved from ocean color sensors. However, typical ocean observing satellites with 1 km resolution cannot reliably retrieve data from narrow estuaries, and small bays where oyster culture typical happens. High-resolution satellite (<100 m) such as the Landsat suite and Sentinel 2 can provide this data at the scale of nearshore oyster farm.
To help reduce risks of site selection we (1) estimated and validated a regional Dynamic Energy Budget model for eastern oysters, (2) generated daily climatologies from a decade of Landsat 8 & 9 SST as well as yearly medians of chlorophyll and POM, and (3) coupled the DEB model and satellite products to predict time to market estimates across southern and midcoast Maine. Satellite forced predicted growth was validated on eight datasets of shell height and six data sets of dry tissue weight spanning four separate estuary systems and five years. Only ~8% of the region reached market size in the second growing season (1-1.5 years), while the majority of the region took 2-5 years. This method shows potential of using high resolution satellites to derisk nearshore shellfish site selection.