Estimation of Winter Wheat Yield from UAV-Based Multi-Temporal Imagery Using Crop Allometric Relationship and SAFY Model

Abstract

Crop yield prediction and estimation play essential roles in the precision crop management system. The Simple Algorithm for Yield Estimation (SAFY) has been applied to Unmanned Aerial Vehicle (UAV)-based data to provide high spatial yield prediction and estimation for winter wheat. However, this crop model relies on the relationship between crop leaf weight and biomass, which only considers the contribution of leaves on the final biomass and yield calculation. This study developed the modified SAFY-height model by incorporating an allometric relationship between ground-based measured crop height and biomass. A piecewise linear regression model is used to establish the relationship between crop height and biomass. The parameters of the modified SAFY-height model are calibrated using ground measurements. Then, the calibrated modified SAFY-height model is applied on the UAV-based photogrammetric point cloud derived crop height and effective leaf area index (LAIe) maps to predict winter wheat yield. The growing accumulated temperature turning points of an allometric relationship between crop height and biomass is 712 °C. The modified SAFY-height model, relative to traditional SAFY, provided more accurate yield estimation for areas with LAI higher than 1.01 m2/m2. The RMSE and RRMSE are improved by 3.3% and 0.5%, respectively.