Chlorophyll a and b content (Cab) and leaf area index (LAI) are two key parameters of crops, and their quantitative inversions are important for growth monitoring and the field management of wheat. However, due to the close correlation between the spectral signals of these two parameters and the effects of soil and atmospheric conditions, as well as modeling errors, synchronous retrieval of LAI and Cab from remote sensing data is still a challenging task. In a previous study, we introduced the optimal estimation theory and established the inversion framework by coupling the PROSAIL (PROSPECT + SAIL) model with the unified linearized vector radiative transfer model (UNL-VRTM). The framework fully utilizes the simulated radiance spectra for synchronous retrieval of Cab and LAI at the UAV observation scale and has good convergence and self-consistency. In this study, based on this inversion framework, synchronized retrieval of Cab and LAI was carried out by real wheat UAV observation data and validated with the ground-measured data. By comparing with the empirical statistical model constructed by the PROSAIL model and coupled model, least squares support vector machine (LSSVM), and random forest (RF), the proposed method has the highest accuracy of Cab and LAI estimated from UAV multispectral data (for Cab, R² = 0.835, RMSE = 14.357; for LAI, R² = 0.892, RMSE = 0.564). Our proposed method enables the fast and efficient estimation of Cab and LAI in multispectral data without prior measurements and training.

High-temperature furnaces and coal-fired boilers are widely employed in the petrochemical and metal-smelting sectors. Over time, the deterioration, corrosion, and wear of pipelines can lead to equipment malfunctions and safety incidents. Nevertheless, effective real-time monitoring of equipment conditions remains insufficient, primarily due to the interference caused by flames generated from fuel combustion. To address this issue, in this study, a through-flame infrared imager is developed based on the mid-wave infrared (MWIR) radiation characteristics of the flame. The imager incorporates a narrowband filter that operates within the wavelength range of 3.80 μm to 4.05 μm, which is integrated into conventional thermal imagers to perform flame filtering. This configuration enables the radiation from the background to pass through the flame and reach the detector, thereby allowing the infrared imager to visualize objects obscured by the flame and measure their temperatures directly. Our experimental findings indicate that the imager is capable of through-flame imaging; specifically, when the temperature of the target exceeds 50 °C, the imager can effectively penetrate the outer flame of an alcohol lamp and distinctly capture the target’s outline. Importantly, as the temperature of the target increases, the clarity of the target’s contour in the images improves. The MWIR through-flame imager presents considerable potential for the real-time monitoring and preventive maintenance of high-temperature furnaces and similar equipment, such as detecting the degradation of refractory materials and damage to pipelines.