Module 6900
Satellite Data in Agricultural and Environmental Economics

Lecturers

• Prof. Dr. David Wuepper, wuepper@uni-bonn.de
• Dr. Wyclife Agumba Oluoch
• Dr. Hadi
• Prof. Dr. Lisa Biber-Freudenberger

Course Description

Agricultural and environmental economists are in the fortunate position that a lot of what is happening on the ground is observable from space. Most agricultural production happens in the open and one can see from space when and where innovations are adopted, crop yields change, or forests are converted to pastures, to name just a few examples. However, converting images into measurements of a particular variable is not trivial, as there are more pitfalls and nuances than “meet the eye”. Overall, however, research benefits tremendously from advances in available satellite data as well as complementary tools, such as cloud-based platforms for data processing, and machine learning algorithms to detect phenomena and mapping variables. The focus of this course is to provide agricultural and environmental economists with an accessible introduction to working with satellite data, show-case applications, discuss advantages and weaknesses of satellite data, and emphasize best practices. This is supported by extensive practice sessions on technical foundations, learning in detail how to create different variables, about work flows, and what are the required resources and skills.

Competence to have / things to do for students before course starts:

Required Reading

• Wuepper, Oluoch, Hadi (2024). Satellite Data in Agricultural and Environmental Economics: Theory and Practice. ICAE 2024 Keynote Paper. https://ageconsearch.umn.edu/record/344359

Recommended Further Reading

• Burke, M., Driscoll, A., Lobell, D. B., Ermon, S. (2021). Using satellite imagery to understand and promote sustainable development. Science, 371(6535). https://www.science.org/doi/10.1126/science.abe8628
• Donaldson, D., Storeygard, A. (2016). The view from above: Applications of satellite data in economics. Journal of Economic Perspectives, 30(4), 171-198. https://www.aeaweb.org/articles?id=10.1257/jep.30.4.171
• Jain, M. (2020). The benefits and pitfalls of using satellite data for causal inference. Review of Environmental Economics and Policy. https://www.journals.uchicago.edu/doi/abs/10.1093/reep/rez023?journalCode=reep

Required Preparation

• Please create a Google Earth Engine Account and familiarize yourself a bit with the platform: https://earthengine.google.com/
• Also, install and familiarize yourself with the latest versions of R and
  R Studio (see e.g., the video Learn R in 39 Minutes: https://www.youtube.com/watch?v=yZ0bV2Afkjc) and
  the tidyverse workflow (especially see Workflow Basics: https://r4ds.hadley.nz/workflow-basics and
  Data Transformations: https://r4ds.hadley.nz/data-transform)
• Moreover, please already install the following R packages:
  tidyverse, raster, terra, ggplot2, sf, ncdf4, exactextract, fasterize, tictoc, tmap, ENMEval, sdm, geodata, usdm, caret, keras, and mapview.

Optional Preparation

You can already casually browse what we can generally see on satellite imagery
(see e.g., this web platform: https://apps.sentinel-hub.com/eo-browser/ and
this Earth Engine app: https://jstnbraaten.users.earthengine.app/view/landsat-timeseries-explorer), and
various satellite data products (see e.g., NASA Earth Data: https://search.earthdata.nasa.gov/search or
Resource Watch: https://resourcewatch.org/data/explore)

General structure

The general daily organization is such that there is a lecture in the morning and a practical computer session in the afternoon.

Day 1	Morning	Introduction and Applications	Afternoon	Tutorial 1
Day 2	Morning	Foundations and Workflows	Afternoon	Tutorial 2
Day 3	Morning	Creating Variables 1/2	Afternoon	Tutorial 3
Day 4	Morning	Creating Variables 2/2	Afternoon	Tutorial 4
Day 5	Morning	Using the Data	Afternoon	Tutorial 5