Part of a larger ongoing project to monitor land and water use by combining irrigation and gridded data via remote sensing data with machine learning. Specific goal is to develop and deploy spatiotemporal data mining methods that can integrate heterogeneous data from diverse hydrology and meteorology models to improve understanding and modeling of land and water use over the last decades.
This specific repository is to build a comprehensive, accessible, and user-friendly platform to harmonize heterogenous spatiotemporal gridded agriculture-related datasets.
Work in progress
rshiny4.R temporarily hosted at: https://syedmfuad.shinyapps.io/AgStat/
The rest of the files are older versions that do slightly different things.