H4. Advances in remote and in situ sensing in environmental science**
Physically based and machine learning models are increasingly data hungry. To keep pace with advances in environmental modeling and management, there is a corresponding need for novel data collection approaches that advance beyond past technology in terms of sensing resolution, spatial extent, and/or cost. We invite presentations of research related to new paradigms for environmental sensing. Topics could include UAV-based sensing, distributed fiber-optic sensing of temperature or other variables, development of inexpensive do-it-yourself or internet-of-things sensors, or any approach to environmental sensing that will help move environmental science disciplines forward as we try to better measure and understand the environment around us.