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

Abstract

Highlights: What are the main findings? Simple, computationally efficient machine learning methods can reliably predict agricultural outcomes in data-scarce environments using publicly available imagery. Task-agnostic random convolutional features (RCFs) from satellite imagery achieve an out-of-sample (Formula presented.) of 0.83 in predicting maize yields across space and time in Zambia (2016–2021), outperforming traditional NDVI-based approaches. RCF models achieve strong performance, predicting exclusively temporal variation in yields ((Formula presented.) = 0.74) when explicitly trained on temporal anomalies and substantially outperforming NDVI-based approaches ((Formula presented.) = 0.39). What are the implications of the main findings? Effective crop-monitoring systems are feasible without expensive proprietary data or computationally intensive deep learning methods. Imagery-based monitoring can accurately detect temporal yield anomalies, enabling governments and agencies to target interventions like the expansion or release of government grain stocks, food aid, and agricultural insurance payouts. Task-agnostic satellite features offer a scalable, multipurpose alternative to traditional vegetation indices, as the same RCF features can be reused across different prediction tasks (income, forest cover, water availability) without customization. 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 (Formula presented.) score of 0.74 when predicting temporal variation alone, while the performance of the NDVI-based approach drops to an (Formula presented.) 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.