Yield forecasting in maize: Performance and limits of unmanned aerial vehicle and PlanetScope remote sensing across multiple growth cycles

Abstract

This study addresses the challenge of forecasting maize yield in southeastern Quebec by comparing weekly Unmanned Aerial Vehicle (UAV) and PlanetScope satellite imagery throughout the cropping season and across diverse growing conditions. Using five nitrogen treatments over three years with two sowing windows each year to generate variability within the dataset, eleven vegetation indices were evaluated to identify the best-performing indices and the optimal forecasting window. Indices were interpolated using curve fitting to enable evaluation at any stage of the growing season. Cross-validation simulated real-world application by excluding entire sowing events during model testing. Using a linear regression approach, results demonstrate that indices combining green and near-infrared bands (Green Normalized Difference Vegetation Index [GNDVI] and Chlorophyll Index Green [CIG]) exhibit superior forecasting potential compared to red-near-infrared (like NDVI) and RGB-based indices (like NGRDI). The optimal forecast window occurs during early grain filling (R2-R3 stages, around 2300 Crop Heat Units [CHU]), achieving Root Mean Square Coefficient of Variation (RMSCV) values of 12.51 % for UAVs and 15.28 % for PlanetScope. While PlanetScope maximum performance approached UAV capabilities, results showed a CHU range between 200 and 400 in the effective forecasting period (RMSCV < 20 %) compared to 850 to 1450 for UAV. For PlanetScope, adding multiple indices marginally improved precision, slightly reduced forecasting window and reduced model transferability. The analysis revealed weak correlations between indices and yield during early vegetative and senescence phases, indicating limited potential for enabling timely in-season management interventions. This study established UAV-based models as a reference point for assessing the limitations of satellite-derived forecasts.

Plant phenotyping relevance

UAV・衛星画像と植生指数によるトウモロコシ収量推定を比較・交差検証しており、植物収量という形質の取得・予測手法が中心である。

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