Climate change increases water stress in Washington State watersheds due to multiple factors (e.g., loss of snowpack, shift in the timing of runoff). These impacts can either be alleviated or exacerbated by human behavior that shifts the magnitude and timing of demands on water, and many of these potential impacts have not been quantified. One such critical but overlooked aspect is double cropping – the ability to grow more than one crop in a given season. Climate change related warming and lengthening of the growing season allows an expansion of double cropping extent, with significant implications for both agricultural productivity and water resources. Before quantifying impacts of climate change on double cropping and associated implications for water resources in Washington State, it is critical to quantify the baseline current double cropping extent. Currently there is no source of comprehensive information for this, and algorithms based on satellite imagery offer a promising way to address this critical data and knowledge gap. Our goal for this proposed work is to leverage recent advances in open source satellite imagery, build/evaluate a machine learning model to estimate current double cropping extent, and develop a product for continued automated future monitoring of double cropping trends. This then facilitates the quantification of the impact of double cropping on water demands and water stress in Washington State watersheds.