Thermal-infrared radiosity and heat-diffusion model for estimating sub-pixel radiant temperatures over the course of a day
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In temperature/emissivity estimation from remotely measured radiances the general assumption is that scene elements represented by pixels in fact have a single emissivity spectrum and are isothermal. Thus, estimated temperatures and emissivities are the effective values that would be found if these simplified assumptions were met. In reality, the physical scene is neither homogeneous nor isothermal, and the effective values are not strictly representative of it. This dissertation is devoted to thermal-infrared radiosity and a heat-diffusion model used for predicting effective emissivity spectra and radiant temperatures for rough natural surfaces, which allows one to estimate discrepancies between effective and actual values. Computer model results are compared to analytic model results in order to verify that the computer model is working properly. The model is validated against spectra measured in the field using a hyperspectral imaging spectrometer and a cm-scale DTM of the test scene acquired using a tripod-based LiDAR. The discrepancies between analytical and modeled values are less than 0.01%. Modeled emissivity spectra deviate from the measured by no more than 0.015 emissivity units. Modeled kinetic temperature on average deviates from measured by less than 1K over the course of a day. Possible applications of the developed model in remote sensing, planetary science, and geology are described.