Earth Science Branch
The Earth Science Branch conducts research of the Earth as a system with a focus on lightning and precipitation processes, weather and climate variability, monitoring fluxes of heat and water from the surface, and associated data management and mining activities for scientific discovery and applications for societal benefit.
Earth Science Branch Projects
On 8/3/23, NASA, IBM, and Hugging Face announced the public release of a Geospatial FM for NASA Earth Observation data. In collaboration with Clark University’s Center for Geospatial Analytics, the European Space Agency (ESA), USGS, and Oak Ridge National Laboratory, …
Public Release of the HLS Geospatial Foundation Model (FM) on Hugging Face Read More »
On 8/1/23, the SERVIR Science Coordination Office’s Emil Cherrington and Christine Evans provided a virtual training on greenhouse gas emission estimation as a pre-event to the AmeriGEO Week 2023 activity. In the context of NASA’s declaring 2023 to be “A …
SERVIR Hosts Virtual Training on Greenhouse Gas Estimation for AmeriGEO Week Event Read More »
A 2-year proposal, led by Jordan Bell (ST11), will focus on the use of freely available Earth observations and data from the Commercial Smallsat Data Acquisition program to identify, map, and analyze hail and wind damage swaths was recently selected. …
Commercial SmallSat Data Analysis Proposal Selected Read More »
Team members from the MSFC Earth Science Disasters Team have been providing significant response support to NASA Headquarters/Earth Science Division in the aftermath of record-setting wildfires that impacted Maui in early August 2023. Ronan Lucey (UAH/ST11) and Jordan Bell (ST11) …
MSFC Earth Science Disasters Team Support for Wildfires in Maui and for Hurricane Idalia Read More »
During IGARSS 2023 in Pasadena, CA, the Interagency Implementation and Advanced Concepts Team (IMPACT) members Manil Maskey, Iksha Gurung, and Muthukumaran Ramasubramanian (ST11) teamed up with researchers from the Jülich Supercomputing Centre for the extensive, full-day tutorial End-to-End Machine Learning …