Intra‐day variability of temperature and its near‐surface gradient with elevation over mountainous terrain: Comparing MODIS land surface temperature data with coarse and fine scale near‐surface measurements

Abstract

Where land surface air temperature data are not available, satellite land surface temperature are used. However, the coarse spatial resolution of satellitederived products may yield errors at the local scale. This work shows the differences between the MODIS Land Surface Temperature and Emissivity (MOD11A1) product and ground measurements at two different scales. We used data from 21 SNOTEL stations across the northern Front Range of Colorado to represent the coarse scale and 17 iButton temperature sensors across the Colorado State University Mountain Campus to represent the fine scale. We found significant differences in the temperature and its changes with elevation for the two spatial scales. At the fine scale, cold air drainage can induce an inversion of the temperature gradient with elevation. A higher correlation was found during the nighttime at the fine scale, while, at the coarse scale, higher correlations were observed during the daytime. On windy nights, temperatures do not cool as much as on calmer nights, and the coarse scale nearsurface temperature gradient with elevation persists, though the fine scale inversions do not develop. The near-surface temperature gradients with elevation based on the MODIS pixels are similar to the ground-based data at the coarse scale but not at the fine scale. Thus, one must be cautious in selecting the near-surface temperature gradients with elevation for mountainous terrain when different scales are considered, and a proper validation of satellite products is necessary prior to their use to avoid the propagation of uncertainties.

Assessment of High Resolution Air Temperature Fields at Rocky Mountain National Park by Combining Scarce Point Measurements with Elevation and Remote Sensing Data

“…Conversely, despite the good correlation of air temperature with LST (R 2 of 0.80), they are different variables and LST should not be used directly to characterize air temperature patterns. The comparison between the different estimations of air temperature with LST showed considerable biases, as previously observed [8]. For the best estimation of air temperature in this study (merged KED), 73% of the MODIS pixels showed a value of LST within the stated uncertainty range (from estimate −σ to estimate + σ).…”

“…SNOTEL temperature decreases with elevation but the correlation between both variables is moderate; the R 2 of the linear relationship is 0.21 for the whole domain and 0.50 for the estimation area (see Figure 3a). Similar results were found for this study area in a previous analysis [8]. The scarce air temperature data from SNOTEL were complemented by well-sampled secondary information from elevation and LST in order to obtain air temperature fields.…”

“…Air temperature is influenced by several land and atmospheric processes and can have an important variability due to spatiotemporal changes in these processes [7]. Therefore, depending on the scale of interest, the air temperature patterns can be very different for the same case study [8]. Conversely, air temperature data are usually scarce, especially in montane regions, due to the problems of maintaining the monitoring system under harsh climatic conditions [9].…”

“…sometimes modify the theoretical near surface temperature gradient [16]. These conditions can even induce inverse temperature gradients at local scales [8].…”

“…This variable is also usually correlated with elevation, and spatially well-sampled. Sometimes this variable is used directly in hydrological applications [21,22], but the difference between LST and air temperature can be considerable [8].…”