UAV Hyperspectral Remote Sensing Estimation of Soybean Yield Based on Physiological and Ecological Parameter and Meteorological Factor in China

Abstract

Accurate soybean yield estimates are important for establishing effective agricultural and soybean trade policies aimed at ensuring food security in China. Unmanned aerial vehicles (UAV) have been used to collect hyperspectral remote sensing data for soybean yield inversion owing to the flexibility, efficiency, and low cost of the technology. Traditional yield inversion algorithms are based on physiological and ecological parameters. However, meteorological factors also influence crop growth and yield and can even eliminate differences caused by physiological and ecological parameters at certain growth stages. The paper proposed a new yield estimation parameter index of effective accumulated temperature, incorporating the influence of meteorological factors into yield inversion models, which can improve the accuracy and universality of crop yield estimation. In the current study, soybean yield inversion models were developed for leaf area index (LAI), fresh biomass, and in-season estimated yield (INSEY) at different growth stages. The accuracies of the various models were evaluated by comparing yield estimates with ground-based sampling data. The analyses revealed that plant parameters, including plant height, LAI, dry biomass, and fresh biomass, correlated significantly with soybean yields, and that LAI and fresh biomass demonstrated the highest correlations with soybean yields. The results of soybean yield estimation at the single growth stage showed that the R2 value of the model and validation increased by 50.00, 26.60, and 34.40%, and 47.20, 12.50, and 31.90%, respectively, at the flowering stage, pod stage, and seed filling stage, indicating the highest accuracy of yield estimation at the beginning seed stage. The analysis of yield estimation results of soybean at multiple growth stages showed that R2 values of model and validation of soybean yield estimation models at three different growth stages increased by 4.50, 7.20, and 15.80%, and 4.00, 5.30, and 8.90%, respectively, compared with the beginning seed stage with the highest yield estimation accuracy. Compared with the yield estimation model of the single growth stage, the yield estimation accuracy of multiple growth stages is higher.