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Towards fine resolution global maps of crop yields: Testing multiple methods and satellites in three countries
Journal Article

One of the greatest challenges in monitoring food security is to provide reliable crop yield information that is temporally consistent and spatially scalable. An ideal yield dataset would not only extend globally and across multiple years, but would also have enough spatial granularity to characterize productivity at the field and subfield level. Rapid increases in satellite data acquisition and platforms such as Google Earth Engine that can efficiently access and process vast archives of new and historical data offer an opportunity to map yields globally, but require efficient and robust algorithms to combine various data streams into yield estimates. We recently introduced a Scalable satellite-based Crop Yield Mapper (SCYM) that combines crop models simulations with imagery and weather data to generate 30 m resolution yield estimates without the need for ground calibration. In this study, we tested new large-scale implementations of SCYM, focusing on three regions with varying crops, field sizes and landscape heterogeneity: maize in the U.S. corn belt (390,000 km2), maize in Southern Zambia (86,000 km2), and wheat in northern India (450,000 km2). As a benchmark, we also tested a simpler empirical approach (PEAKVI) that relates yield to the peak value of a time series of spatially aggregated vegetation indices, similar to methods used in current operational monitoring. Both SCYM and PEAKVI were applied to data from all Landsat's sensors and MODIS for more than a decade in each region, and evaluated against ground-based estimates at the finest available administrative level (e.g., counties in the U.S.). We found consistently high correlations (R2 ≥ 0.5) between the spatial pattern of ground- and satellite-based estimates in both U.S. maize and India wheat, with small differences between methods and source of satellite data. In the U.S., SCYM outperformed PEAKVI in tracking temporal yield variations, likely owing to its explicit consideration of weather. In India, both methods failed to track temporal yield changes, with various possible explanations discussed. In Zambia, the PEAKVI approach applied to MODIS tracked yield variations much better (R2 > 0.5) than any other yield estimate, likely because the frequent cloud cover in this region confounds the other approaches. Overall, this study demonstrates successful approaches to yield estimation in each region, and illustrates the importance of distinguishing between accuracy for spatial and temporal variation. The 30 m resolution of Landsat-based SCYM does not appear to offer large benefits for tracking aggregate yields, but enables finer scale analyses than possible with the other approaches.



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