Remotely sensed water properties are important for a variety of applications, including validation of Earth systems models (ESMs), habitat suitability models, and sea level rise projections. For the validation of next-generation, high or multi-resolution (30 to 60 km) ESMs in particular, the usefulness of operational forecasting products and directly observing satellite-based sensors for validation is limited due to their temporal availability and spatial resolution of <1 year (in some cases) and >30 km², respectively. To address this validation data gap, we developed a data-driven model to produce high-resolution (<1 km²) estimates of temperature, salinity, and turbidity over decadal time scales as required by next-generation ESMs. Our model fits daily moderate resolution imaging spectroradiometer (MODIS) Aqua reflectance data to surface observations (<1 m depth) from 2000–2021 in the Chesapeake Bay. The resulting models have similar error statistics as prior efforts of this type for salinity [root mean square error (RMSE): 2.3] and temperature (RMSE: 1.8 C). However, unlike prior efforts, our model is designed as a pipeline meaning that it has the advantage of producing predictions of water properties in future time periods as additional MODIS data becomes available. We also include novel geographically aware predictive features insofar as they capture geographic variation in the influence of flow and surface water exchange in upstream coastal watersheds.