Sensor radiance physical model for rugged heterogeneous surfaces in the 3-14 μm region.

Abstract

We present a physical model describing the radiance acquired by an infrared sensor over a rugged heterogeneous surface. This model predicts the radiance seen over complex landscapes like urban areas and provides an accurate analysis of the signal, as each component is available at ground and sensor level. Plus, it allows data comparison from different instruments. Two representative cases (natural and urban) are analysed to show the composition and the construction of the sensor signal and to highlight the importance of having a 3D model, especially for rugged surfaces where environment weights in the overall spectral domain.

Impact of 3-D Structures and Their Radiation on Thermal Infrared Measurements in Urban Areas

“…Assuming that the length of the road and building is infinite, these view factors can be classified into two types: one is the view factor between "two infinitely long, directly opposed parallel plates of the same finite width" [28] represented by F Rd→ A , F A→Rd , F RW→LW , F LW→RW ; the other is the view factor between "two infinitely long plates of unequal widths, having one common edge, and at an angle of 90 • to each other" [28] represented by F A→LW , F Rd→LW , F A→RW , F Rd→RW , F LW→ A , F RW→ A , F LW→Rd , and F RW→Rd . According to [28], the former can be calculated using (15) and (16) while the latter can be calculated using (17) and ( 18)…”

“…After decades of improvement, various types of LST retrieval algorithms have been developed [5]- [8] and have achieved great success for natural flat surfaces [9]- [15]. However, in urban areas, 3-D structures and their radiation affect satellite TIR measurements, especially in high spatial resolution images [16], [17], because the observed radiance would increase due to the radiation of the surroundings and reflections inside 3-D structures; the observed signal would be anisotropic through various viewing angles. Consequently, the performance of existing LST retrieval algorithms may deteriorate significantly if this impact is not well addressed in observed signals.…”

“…Gastellu-Etchegorry et al [22], [23] and Guillevic and Gastellu-Etchegorry [24] extended the discrete anisotropic radiative transfer (DART) model to the TIR region, providing a modified model capable of simulating the sensor-observed TIR spectral radiance of 3-D scenes. Although the current version of DART can also allow a term by term analysis of the radiative contributors on the signal at a sensor level, the model should be run several times which is not convenient for schematic case studies [16], [17]. Thus, Pallotta et al [17] developed another model to estimate the sensor-observed radiance.…”

“…Although the current version of DART can also allow a term by term analysis of the radiative contributors on the signal at a sensor level, the model should be run several times which is not convenient for schematic case studies [16], [17]. Thus, Pallotta et al [17] developed another model to estimate the sensor-observed radiance. However, vertical surfaces such as building walls could only be processed approximately because this model used a regular grid to digitalize the relief.…”