Combining UAV Multispectral and Thermal Infrared Data for Maize Growth Parameter Estimation

The leaf area index (LAI) and leaf chlorophyll content (LCC) are key indicators of crop photosynthetic efficiency and nitrogen status. This study explores the integration of UAV-based multispectral (MS) and thermal infrared (TIR) data to improve the estimation of maize LAI and LCC across different growth stages, aiming to enhance nitrogen (N) management. In field trials from 2022 to 2023, UAVs captured canopy images of maize under varied water and nitrogen treatments, while the LAI and LCC were measured. Estimation models, including partial least squares regression (PLS), convolutional neural networks (CNNs), and random forest (RF), were developed using spectral, thermal, and textural data. The results showed that MS data (spectral and textural features) had strong correlations with the LAI and LCC, and CNN models yielded accurate estimates (LAI: R 2 = 0.61–0.79, RMSE = 0.02–0.38; LCC: R 2 = 0.63–0.78, RMSE = 2.24–0.39 μg/cm 2 ). Thermal data reflected maize growth but had limitations in estimating the LAI and LCC. Combining MS and TIR data significantly improved the estimation accuracy, increasing R 2 values for the LAI and LCC by up to 23.06% and 19.01%, respectively. Nitrogen dilution curves using estimated LAIs effectively diagnosed crop N status. Deficit irrigation reduced the N uptake, intensifying the N deficiency, while proper water and N management enhanced the LAI and LCC.