Use of remotely sensed land surface temperature as a proxy for air temperatures at high elevations: Findings from a 5000 m elevational transect across Kilimanjaro

Abstract

High elevations are thought to be warming more rapidly than lower elevations, but there is a lack of air temperature observations in high mountains. This study compares instantaneous values of land surface temperature (10:30/22:30 and 01:30/13:30 local solar time) as measured by Moderate Resolution Imaging Spectroradiometer MOD11A2/MYD11A2 at 1 km resolution from the Terra and Aqua platforms, respectively, with equivalent screen‐level air temperatures (in the same pixel). We use a transect of 22 in situ weather stations across Kilimanjaro ranging in elevation from 990 to 5803 m, one of the biggest elevational ranges in the world. There are substantial differences between LST and Tair, sometimes up to 20°C. During the day/night land surface temperature tends to be higher/lower than Tair. LST‐Tair differences (ΔT) show large variance, particularly during the daytime, and tend to increase with elevation, particularly on the NE slope which faces the morning Sun. Differences are larger in the dry seasons (JF and JJAS) and reduce in cloudy seasons. Healthier vegetation (as measured by normalized difference vegetation index) and increased humidity lead to reduced daytime surface heating above air temperature and lower ΔT, but these relationships weaken with elevation. At high elevations transient snow cover cools LST more than Tair. The predictability of ΔT therefore reduces. It will therefore be challenging to use satellite data at high elevations as a proxy for in situ air temperatures in climate change assessments, especially for daytime Tmax. ΔT is smaller and more consistent at night, so it will be easier to use LST to monitor changes in Tmin.

Scale‐dependent complementarity of climatic velocity and environmental diversity for identifying priority areas for conservation under climate change

“…There has been recent progress in using remotely‐sensed land surface temperature data to identify microrefugia. However, this data also has limitations in terms of spatial (MODIS) and temporal (Landsat) resolution (Pepin, Maeda, & Williams, ). Further work is needed using these remotely sensed data to validate diversity metrics derived from elevation data and improve the ability of climate downscaling methods to represent local climate.…”

An Examination of Temperature Trends at High Elevations Across the Tibetan Plateau: The Use of MODIS LST to Understand Patterns of Elevation‐Dependent Warming

“…In the Tibetan Plateau in contrast, lapse rates estimated for daily mean temperature from LST were found to show similar seasonal patterns to those based on T air (Zhang, Zhang, Zhang, Che, et al, ). There have been relatively few detailed studies of the difference between T air and LST at extreme high elevations above 5,000 m, but those that have been performed tend to show increased variance in the difference, a lower correlation between T air and LST, and larger mean differences (Pepin et al, ). Taking all these studies together, clearly, a correction is essential before LST can be used to measure elevation‐dependent air temperature changes.…”

“…T air is more of a regional mean value and shows less dramatic temporal and spatial gradients than LST, particularly at high elevation where the transfer of heat into the lower atmosphere through sensible and latent heat fluxes is less efficient. Therefore, there are challenges in converting LST to a realistic measure of air temperature at high elevations (Pepin et al, ). Well‐known issues include contamination by cloud cover (Zhang, Zhang, Ye, et al, ; Zhang, Zhang, Zhang, et al, ), the influence of ephemeral snow cover (Shamir & Georgakakos, ; Williamson et al, ), the role of vegetation in increasing latent heat at the expense of sensible heat (Vancutsem et al, ), and timing and spatial differences between the two measurements (point vs pixel).…”

Detecting microrefugia in semi-arid landscapes from remotely sensed vegetation dynamics

“…In addition, other strategies and other types of remote sensing may contribute to microrefugia detection. For example, there is interest in directly detecting refugial microclimates using thermal remote sensing, although this is challenged by the complex relationships between land surface temperature detected by remote sensors and air temperatures (Pepin et al, 2016). Alternatively, LiDAR data have the potential to indicate microrefugia by characterizing vegetation structures (Frey et al, 2016;Lenoir et al, 2017) or small topographic features (Lenoir et al, 2017) that buffer microclimates, or by detecting the relict vegetation communities occurring within microrefugia (Schut et al, 2014).…”