Existing computer simulations of aircraft infrared signature (IRS) do not account for dispersion induced by uncertainty on input data, such as aircraft aspect angles and meteorological conditions. As a result, they are of little use to estimate the detection performance of IR optronic systems; in this case, the scenario encompasses a lot of possible situations that must be indeed addressed, but cannot be singly simulated. In this paper, we focus on low-resolution infrared sensors and we propose a methodological approach for predicting simulated IRS dispersion of poorly known aircraft and performing aircraft detection on the resulting set of low-resolution infrared images. It is based on a sensitivity analysis, which identifies inputs that have negligible influence on the computed IRS and can be set at a constant value, on a quasi-Monte Carlo survey of the code output dispersion, and on a new detection test taking advantage of level sets estimation. This method is illustrated in a typical scenario, i.e., a daylight air-to-ground full-frontal attack by a generic combat aircraft flying at low altitude, over a database of 90 000 simulated aircraft images. Assuming a white noise or a fractional Brownian background model, detection performances are very promising.

With the advancement of high spatial resolution imaging spectroscopy, an accurate surface reflectance retrieval is needed to derive relevant physical variables for land cover mapping, soil, and vegetation monitoring. One challenge is to deal with tree shadows using atmospheric correction models if the tree crown transmittance $T_c$ is not properly taken into account. This requires knowledge of the complex radiation mechanisms that occur in tree crowns, which can be provided by coupling the physical modeling of canopy radiative transfer codes (here DART) and the 3D representations of trees. First in this study, a sensitivity analysis carried out on DART simulations with an empirical 3D tree model led to a statistical regression predicting $T_c$ from the tree leaf area index (LAI) and the solar zenith angle with good performances (RMSE ≤ 4.3% and R² ≥ 0.91 for LAI ≤ 4 m².m⁻²). Secondly, more realistic 3D voxel-grid tree models derived from terrestrial LiDAR measurements over two trees were considered. The comparison of DART-simulated $T_c$ from these models with the previous predicted $T_c$ over 0.4–2.5 µm showed three main sources of inaccuracy quoted in order of importance: (1) the global tree geometry shape (mean bias up to 21.5%), (2) the transmittance fraction associated to multiple scattering, $T_{scat}$ (maximum bias up to 13%), and (3) the degree of realism of the tree representation (mean bias up to 7.5%). Results showed that neglecting $T_c$ leads to very inaccurate reflectance retrieval (mean bias > 0.04), particularly if the background reflectance is high, and in the near and shortwave infrared – NIR and SWIR – due to $T_{scat}$. The transmittance fraction associated to the non-intercepted transmitted light, $T_{dir}$, can reach up to 95% in the SWIR, and $T_{scat}$ up to 20% in the NIR. Their spatial contributions computed in the tree shadow have a maximum dispersion of 27% and 8% respectively. Investigating how to approximate $T_{dir}$ and $T_{scat}$ spectral and spatial variability along with the most appropriate tree 3D modeling is crucial to improve reflectance retrieval in tree shadows when using atmospheric correction models.

Urban trees contribute to urban well-being but face challenging environments that can reduce their lifespan and increase young tree mortality. Although many studies have used remote sensing data to monitor the functional status of trees in rural areas, few have done so in urban areas to assess the health or estimate the biomass of large green areas. This study assessed the suitability of using Sentinel-2 images to characterize two urban tree functional traits—leaf chlorophyll content (Cab) and leaf area density (LAD)—in isolated trees and tree rows. Simulated Sentinel-2 images were generated using the DART radiative transfer model, considering 16 tree-endogenous and 14 tree-exogenous parameters, with 15 vegetation indices (VIs) analyzed. Sensitivity analysis was performed in four contrasting urban environments using local climate zone taxonomy. The accuracy of the simulated images was validated with real Sentinel-2 images, field measurements, and ancillary data collected for four tree species in Rennes, France. The results showed that the tree parameters significantly influenced Sentinel-2 spectral bands, with NGBDI and OSAVI VIs being most sensitive to Cab and LAD. The model showed high accuracy, with a mean RMSE of 0.016 for key spectral bands. The results also highlighted the importance of considering ancillary data to capture specific urban characteristics.