The successful candidate will have a strong background in remote sensing, with experience in Google Earth Engine using remotely-sensed imagery for mapping applications and analysis. The successful candidate will have strong programming skills, including proficiency with Python, R and Google Earth Engine (GEE). Candidate is required to have proficiency with GIS and image processing software ArcGIS.

Requirements

• Acquire and pre-process HLS data. UseClient's available R code for automating HLS
  geotiff extraction from Client's Land Processes Distributed Active Archive Center (LP
  DAAC) archives, based on region of interest and specified temporal range.
• Generate HLS NDVI time series to identify peak greenness from cloud-masked data.
• Further identify dates of peak greenness across various elevation bands of interest.
  These NDVI time series will be compared to concurrent 250m daily MODIS NDVI time
  series for additional validation of ESI phenological peaks. Generate code to automate
  the extraction of HLS image granule representing peak greenness for regions of interest.
  Similarly run process to identify optimal late-season imagery.