Satellite Data in Agricultural and Environmental Economics: Theory and Practice - David Wuepper; Wyclife Agumba Oluoch; Hadi Hadi (Agricultural Economics, 2025)
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 remotely sensed 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, machine learning algorithms, and econometric approaches. Our goal here is to provide agricultural and environmental economists with an accessible introduction to working with satellite data, show-case applications, discuss pitfalls and available solutions, and emphasize the best practices. This is supported by extensive supporting information, where we describe how to create different variables, common workflows, and a discussion of required resources and skills. Last but not least, example data and reproducible codes are made available online.
Keywords: 330; causal inference; geospatial analysis; machine learning; measurement error; remote sensing