In today’s Satellite Data for Agricultural Economists session, we applied a pre-trained spatial machine learning model to map tea plantations in a different year—using Google Earth Engine composites, Area of Applicability diagnostics, and ensemble predictions to assess temporal transferability and quantify changes in tea area between 2020 and 2025.

Today’s session in our Satellite Data for Agricultural Economists course walked participants through building a spatial machine learning model in R to map tea plantations using Sentinel-2 imagery, training data, and ensemble methods for robust, high-resolution crop classification.

Three models were developed to predict monthly energy cost using building characteristics, occupancy, customer type, and regional information. A multiple linear regression (MLR), a Random Forest (RF) model, and an Extreme Gradient Boosting (XGBoost) model. Model performance was evaluated using the Root Mean Square Error (RMSE) and the Coefficient of Determination (R²)

Teaching a hands-on module on Satellite Data for Agricultural Economists, covering Sentinel-2 preprocessing, cloud masking, canopy height integration, and building machine-learning training data in Google Earth Engine. Practical, coding-oriented, and focused on real agricultural applications.

Kicking off our Satellite Data for Agricultural Economists course with a hands-on introduction to R, geospatial data, and Google Earth Engine—guiding participants from setting up their first R project to extracting and visualizing real-world boundary data for applied agricultural analysis.

R data analysis

This R script details the process of using API call from "nasapower" package, to download temperature data for statistical analysis to isolate heatwaves frequency and visualizations of trends.

In this study, a conditional logistic regression model was employed to explore the relationship between selected clinical and behavioral predictors—smoking status, systolic blood pressure (SBP), and electrocardiogram (ECG) abnormalities—and the likelihood of myocardial infarction.

This cross-sectional analysis explores how demographic and behavioral variables influence knowledge levels, perceptions, and attitudes towards participation in COVID-19 clinical trials.