Implications of atmospheric conditions for analysis of surface temperature variability derived from landscape-scale thermography

Thermal infrared (TIR) cameras perfectly bridge the gap between (i) on-site measurements of land surface temperature (LST) providing high temporal resolution at the cost of low spatial coverage and (ii) remotely sensed data from satellites that provide high spatial coverage at relatively low spatio-temporal resolution. While LST data from satellite (LSTsat) and airborne platforms are routinely corrected for atmospheric effects, such corrections are barely applied for LST from ground-based TIR imagery (using TIR cameras; LSTcam). We show the consequences of neglecting atmospheric effects on LSTcamof different vegetated surfaces at landscape scale. We compare LST measured from different platforms, focusing on the comparison of LST data from on-site radiometry (LSTosr) and LSTcamusing a commercially available TIR camera in the region of Bozen/Bolzano (Italy). Given a digital elevation model and measured vertical air temperature profiles, we developed a multiple linear regression model to correct LSTcamdata for atmospheric influences. We could show the distinct effect of atmospheric conditions and related radiative processes along the measurement path on LSTcam, proving the necessity to correct LSTcamdata on landscape scale, despite their relatively low measurement distances compared to remotely sensed data. Corrected LSTcamdata revealed the dampening effect of the atmosphere, especially at high temperature differences between the atmosphere and the vegetated surface. Not correcting for these effects leads to erroneous LST estimates, in particular to an underestimation of the heterogeneity in LST, both in time and space. In the most pronounced case, we found a temperature range extension of almost 10 K