Estimating Leaf Nitrogen Accumulation Considering Vertical Heterogeneity Using Multiangular Unmanned Aerial Vehicle Remote Sensing in Wheat.

Abstract

The accuracy of leaf nitrogen accumulation (LNA) estimation is often compromised by the vertical heterogeneity of crop nitrogen. In this study, an estimation model of LNA considering vertical heterogeneity of wheat was developed based on unmanned aerial vehicle (UAV) multispectral data and near-ground hyperspectral data, both collected at different view zenith angles (e.g., 0°, -30°, and -45°). Winter wheat plants were evenly divided into 3 layers from top to bottom, and LNA was obtained for the upper, middle, and lower leaf layers, as well as for various combinations of these layers (upper and middle, middle and lower, and the entire canopy, referred to as LNA_Canopy). The linear regression (LR) and random forest regression (RF) models were constructed to estimate the LNA for each individual leaf layer. Subsequently, models for estimating LNA_Canopy that considered the impact of vertical heterogeneity (namely, LR-LNA_Sum and RF-LNA_Sum) were established based on the relationships between LNA_Canopy and LNA in different leaf layers. Meanwhile, LNA models that did not consider the effect of vertical heterogeneity (LR-LNA_non and RF-LNA_non) were used for comparative validation. The validation datasets consisted of UAV-simulated data from hyperspectral reflectance and UAV-measured data. Results showed that LNA_Sum models had markedly higher accuracy compared to LNA_non. The optimal scheme for estimating LNA_Canopy was the combination of the upper, middle, and lower layers based on the normalized difference red edge index. Among these models, RF-LNA_Sum demonstrated higher accuracy than LR-LNA_Sum, with a validation relative root mean square error of 19.3% and 17.8% for the UAV-measured and simulated dataset, respectively.