A fundamental challenge in environmental science is applying the knowledge that scientists discover at a particular location or time to understanding phenomena occurring at other times and/or locations. In the traditional approach, environmental scientists collect field data and perform experiments at, for example, a particular river basin, and then repeat this process at a different time and/or location to see whether their conclusions generalize. This approach is rigorous, but limited because it is time-, labor-, and cost-intensive; thus there exists relatively sparse ground-collected data across the planet. Another challenge is that intensifying human activities amplify the rates of change in conditions per location and time, so knowledge discovered in the past will likely fail to predict outcomes in the future. The challenge of predicting and ameliorating the effects of environmental change disproportionately affects under-resourced communities, including those most vulnerable to environmental changes that lead to food insecurity and hence greater socioeconomic instability. Traditional Artificial Intelligence (AI) approaches cannot resolve this challenge because they require extensive human input, for example due to the need for labeling ground-collected data or other data layers, such as high-resolution satellite imagery. In this project, on-the-ground human observations and labels are replaced with AI-based discovery from abundantly available, mostly unlabeled visual da