Bio 

Currently, at Gro Intelligence, Jerrod specializes in researching and creating crop masks for yield models with strong skills in Google Earth Engine (GEE), ArcGIS, and QGIS. He is skilled in raster and vector analysis using a wide array of geospatial and environmental data. He is adept at working with large datasets as well as data visualization and presentation. Jerrod is highly adaptable to any situation and has learned numerous new skills at Gro Intelligence including improving his coding proficiency in both Python and Javascript. 

Jerrod joined Gro Intelligence after nearly three years at the International Research Institute for Climate and Society at Columbia University’s Earth Institute. There, he was a NASA DEVELOP fellow, center lead, and communications manager. With the NASA DEVELOP program, Jerrod created a drought severity index for Uruguay, researched flood and vector-borne disease monitoring in East Africa, and he also investigated remotely sensed fire monitoring in Indonesia. At the International Research Institute for Climate and Society, Jerrod also worked on improving remotely sensed drought monitoring and crop detection in Uruguay. Earlier in his career, Jerrod was a geologist with AMEC, where he worked on various environmental projects throughout California.

Education

California State University, Fresno: M.S. Geology: Thesis on new thermobarometers for martian igneous rocks

California State University, Fresno: B.S. Geology

Areas of Expertise

Geographic Information Systems (GIS): Proficiency in GIS software tools like Google Earth Engine (GEE) is shown by the analysis and manipulation of MODIS, Landsat, and Sentinel imagery. Very knowledgeable in ArcGIS and QGIS to have them ready for visualization and productionisation of geospatial data.

Data Management Practices: Data acquisition, preparation/organization, and management was done on file types including shapefiles, GeoTIFFs, NetCDFs, and HDF files 

Spatial Analysis and Modeling: Spatial analysis techniques such as spatial statistics and interpolation, were used in order to assess the validity of data and spatial modeling experience for land cover/land use masks.

Programming and Scripting: Python and Javascript have been used extensively in the research and creation of the land cover/land use models, as those are the languages used by GEE’s Code Editor (Javascript) as well as the Geemap module for Colab and Jupyter notebooks (Python) .

Remote Sensing Knowledge: A keen understanding remote sensing principles and techniques, is exemplified by frequently giving company wide presentations such as ‘Remote Sensing 101’ and ‘Geospatial Data 101’. 

Cartographic Principles and Techniques: Creating maps using either ArcGIS or QGIS, including elements such as symbology, scale, and layout design.

Communication and Collaboration: Expertly communicates complex geospatial topics and principles to a variety of audiences, including experts and non experts alike.