Emil A. Cherrington, Ph.D.

My professional work blends Earth observation (EO) 🛰️ and data science 💻, as one does these days 🙂. While I've been dabbling in scientific coding 👨‍💻 for a decade, starting with and in 2015, and moving to JavaScript for cloud computing with Google Earth Engine 🌎 (GEE) in 2017, I have been working professionally with EO and geospatial 🗺️ data since mid-2004. Over the years, I have applied EO and geospatial science specifically in the areas of land cover change detection, ecosystem monitoring, disaster response, and integrated water resource management, and I supported the former NASA Earth Action Capacity Building Program for the better part of the previous twenty years. Most of the GitHub repos I am hosting ⬇️ are hence linked to that work. Across my career, I have worked with multispectral, thermal, hyperspectral, synthetic aperture radar (SAR), and LiDAR data [airborne + spaceborne], and I was one of the editors of the 2019 SERVIR-SilvaCarbon SAR Handbook 📔.

I currently serve as a Principal Research Scientist 👨‍🔬 with the Lab for Applied Science 🔬 within the Earth System Science Center of the University of Alabama in Huntsville (UAH), and I am also an affiliate member of UAH's graduate faculty. From 2022-2026, I was a Google Developer Expert for GEE (before that GDE category was discontinued). I am an Early Adopter for NASA's PACE hyperspectral 🛰️ mission (for which I developed a toolkit), and also for the NASA-ISRO SAR (NISAR) 🛰️ mission. As of early 2026, I am also a member of the User Working Group for NASA's Ocean Biology Distributed Active Archive Center (OB.DAAC). My educational background 👨‍🎓 includes a [2016] double Ph.D. in forest ecology from AgroParisTech (France) and Technische Universität Dresden (Germany), a [2004] master's degree in forest resources from the University of Washington, and an undergraduate degree [2001] in biology from Loyola University Maryland. If you're so inclined, you can check out the recording of my Dec. 2016 doctoral dissertation defense, which combined the mapping of forest types with EO big data (i.e., geospatial machine learning, #GeoML / #GeoAI). 😉