Monitoring Maize Yield Variability over Space and Time with Unsupervised Satellite Imagery Features

Cullen Molitor, Juliet Cohen, Grace Lewin, Steven Cognac, Protensia Hadunka, Jonathan Proctor and Tamma Carleton

Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series from Department of Agricultural & Resource Economics, UC Berkeley

Abstract: Recent innovations in task-agnostic imagery featurization have lowered the computational costs of using machine learning to predict ground conditions from satellite imagery. These methods hold particular promise for the development of imagery-based monitoring systems in low-income regions, where data and computational resources can be limited. However, these relatively simple prediction pipelines have not been evaluated in developing-country contexts over time, limiting our understanding of their performance in practice. Here, we compute task-agnostic random convolutional features from satellite imagery and use linear ridge regression models to predict maize yields over space and time in Zambia, a country prone to severe droughts and crop failure. Leveraging Landsat and Sentinel 2 satellite constellations, in combination with district-level yield data, our model explains 83% of the out-of-sample maize yield variation from 2016 to 2021, slightly outperforming a model trained on Normalized Difference Vegetation Index (NDVI) features, a common remote sensing approach used by practitioners to monitor crop health. Our approach maintains an R2 score of 0.74 when predicting temporal variation alone, while the performance of the NDVI-based approach drops to an R2 of 0.39. Our findings imply that this task-agnostic featurization can be used to predict spatial and temporal variation in agricultural outcomes, even in contexts with limited ground truth data. More broadly, these results point to imagery-based monitoring as a promising tool for assisting agricultural planning and food security, even in contexts where computationally expensive methodologies remain out of reach.

Keywords: 37 Earth Sciences (for-2020); 3704 Geoinformatics (for-2020); Machine Learning and Artificial Intelligence (rcdc); Networking and Information Technology R&D (NITRD) (rcdc); Generic health relevance (hrcs-hc); 2 Zero Hunger (sdg); maize; yield prediction; Landsat; Sentinel; MOSAIKS; Zambia; 0203 Classical Physics (for); 0406 Physical Geography and Environmental Geoscience (for); 0909 Geomatic Engineering (for); 3701 Atmospheric sciences (for-2020); 3709 Physical geography and environmental geoscience (for-2020); 4013 Geomatic engineering (for-2020) (search for similar items in EconPapers)
Date: 2025-01-01
New Economics Papers: this item is included in nep-ict
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.escholarship.org/uc/item/9c92m41k.pdf;origin=repeccitec (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cdl:agrebk:qt9c92m41k

Access Statistics for this paper

More papers in Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series from Department of Agricultural & Resource Economics, UC Berkeley Contact information at EDIRC.
Bibliographic data for series maintained by Lisa Schiff ().