This material is for the workshop on Leveraging Open-Source Data for Precision Agriculture Applications that will take place on Sunday, November 10, from 1 PM to 5 PM at the ASA-CSSA-SSSA Annual Meeting in San Antonio, TX.
The workshop consists of using high-resolution satellite data for delineation of farm management zones. If time permits, we’ll also explore additional analyses using soil moisture observations.
Andres Patrignani - Associate Professor in Soil Water Processes at Kansas State University. I’ve been teaching coding skills to graduate students for the past ten years, and I’m excited to join this workshop so we can dive into coding together! (andrespatrignani@ksu.edu)
Gabriel da Rocha Hintz - Graduate Student in Cropping Systems at Kansas State University (ghintz@ksu.edu)
- Google Earth Engine (GEE) basics with Python and how to download data to your local drive ( we will not use the online GEE console)
- Working with vector and raster maps in Python
- Clustering of multiple layers
- Creating publication-quality figures
- Install Anaconda: We recommend using Anaconda for managing your Python environment.
- Use your "gmail" account to create a Google Earth Engine (GEE) account: This is essential for accessing the GEE API during the workshop. It can take Google from a few hours to a couple of days to approve the request for a non-commercial account.
- Follow the "setup_python_geospatial_analysis.ipynb" notebook to get all the modules installed.
Figure: Multiple NDVI images from Sentinel-2 used in this tutorial. Field management zones were delineated by calculating the mean relative difference of the NDVI maps, followed by clustering with a bisecting K-Means algorithm.