USCC: A Benchmark Dataset for Crop Yield Prediction under Climate Extremes

Abstract

Climate variability and extremes are known to represent major causes for crop yield anomalies. They can lead to the reduction of crop productivity, which results in disruptions in food availability and nutritional quality, as well as in rising food prices. Extreme climates will become even more severe as global warming proceeds, challenging the achievement of food security. These extreme events, especially droughts and heat waves, are already evident in major food-production regions like the United States. Crops cultivated in this country such as corn and soybean are critical for both domestic use and international supply. Considering the sensitivity of crops to climate, here we present a dataset that couples remote sensing surface reflectances with climate variables (e.g. minimum and maximum temperature, precipitation, and vapor pressure) and extreme indicators. The dataset contains the crop yields of various commodities over the USA for nearly two decades. Given the advances and proven success of machine learning in numerous remote sensing tasks, our dataset constitutes a benchmark to advance the development of novel models for crop yield prediction, and to analyze the relationship between climate and crop yields for gaining scientific insights. Other potential use cases include extreme event detection and climate forecasting from satellite imagery. As a starting point, we evaluate the performance of several state-of-the-art machine and deep learning models to form a baseline for our benchmark dataset.