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Improving the accuracy of satellite-based high-resolution yield estimation: A test of multiple scalable approaches
Journal Article

Fast, accurate and inexpensive estimates of crop yields at the field scale are useful for many applications. Based on the Google Earth Engine (GEE) platform, we recently developed a Scalable satellite-based Crop Yield Mapper (SCYM) that integrates crop simulations with satellite imagery and gridded weather data to generate 30 m resolution yield estimates for multiple crops in different regions. Existing versions of SCYM typically capture one-third to half of the variation in reported county-scale yields. Using rainfed maize in the US Midwest as an example, this study tested multiple approaches for improving SCYM’s accuracy, including (i) calibrating the phenology parameters of the crop model (APSIM) used to generate training samples for SCYM; (ii) using an ensemble of three crop models (APSIM-Maize, CERES-Maize, and Hybrid-Maize) instead of a single model; (iii) using simulated biomass from the crop models instead of simulated yields to train SCYM, with the former assuming a constant harvest index (HI). Results show substantial improvement in performance, as assessed using reported county yields by USDA-NASS, both from calibrating APSIM phenology parameters and from training SCYM on simulated biomass rather than yields. Using a multi-model ensemble further improves SCYM, although the benefit is limited. The proposed preferred version of SCYM on average captures 75% of the yield variation for 2001–2015 in the 3I states (i.e. Illinois, Indiana and Iowa) where SCYM is trained, with RMSE typically less than 1 t/ha, and explains 41% to 83% of multi-year yield variations when tested across nine Midwestern US states for 2008–2015. This level of accuracy is particularly notable given that only data from 2014 were used to calibrate phenology parameters. The yield estimates for multiple years in multiple states utilized 1184 Landsat tiles, but could be completed in about 2 h per year by using the GEE platform. All approaches tested in this study do not require any site-specific measurements, and thus can be readily extended to other regions and crops.

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