Testing Remote Sensing Approaches for Assessing Yield Variability among Maize Fields

For remote sensing to be useful for analyzing crop yield gaps, methods should be accurate at the field scale without need for local ground calibration. We used an extensive field‐level data set of on‐farm yields from 134 irrigated and 94 rainfed maize (Zea mays L.) fields in Nebraska during a 4‐yr period to evaluate three methods that do not require ground‐based calibration. The first method is based on summing estimates of absorbed photosynthetically active radiation from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, the second on using either MODIS or Landsat data to calibrate a crop model (Hybrid‐Maize), and the third using Hybrid‐Maize simulations to train a simple regression, which is then applied to MODIS or Landsat data. For MODIS, all three methods performed similarly poorly at predicting maize yields, with an R2 between observed and predicted yields of roughly 0.10 for rainfed and 0.20 for irrigated fields. Estimates from Landsat were considerably more accurate, with up to 20% of rainfed and 50% of irrigated yield variation captured by the predictions. Across all methods and sensors, irrigated yield variations were more successfully captured than rainfed yields because of relatively smaller rainfed field sizes and the added difficulty of modeling crop water stress in rainfed fields. Agreement between observed and predicted yields was highest for the third approach, which is attractive because it leverages a crop model’s ability to synthesize knowledge on crop physiology and year‐to‐year differences in weather throughout the season, yet produces a simple regression that can be rapidly applied to Landsat imagery.