Estimating Temperature Fields from MODIS Land Surface Temperature and Air Temperature Observations in a Sub-Arctic Alpine Environment

Spatially continuous satellite infrared temperature measurements are essential for understanding the consequences and drivers of change, at local and regional scales, especially in northern and alpine environments dominated by a complex cryosphere where in situ observations are scarce. We describe two methods for producing daily temperature fields using MODIS ―clear-sky‖ day-time Land Surface Temperatures (LST). The Interpolated Curve Mean Daily Surface Temperature (ICM) method, interpolates single daytime Terra LST values to daily means using the coincident diurnal air temperature curves. The second method calculates daily mean LST from daily maximum and minimum LST (MMM) values from MODIS Aqua and Terra. These ICM and MMM models were compared to daily mean air temperatures recorded between April and October at seven locations in southwest Yukon, Canada, covering characteristic alpine land cover types (tundra, barren, glacier) at elevations between 1,408 m and 2,319 m. Both methods for producing mean daily surface temperatures have advantages and disadvantages. ICM signals are strongly correlated with air temperature (R 2 = 0.72 to 0.86), but have relatively large variability (RMSE = 4.09 to 4.90 K), while MMM values had a stronger correlation to air temperature (R 2 = 0.90) and smaller variability (RMSE = 2.67 K). Finally, when comparing OPEN ACCESS Remote Sens. 2014, 6 947 8-day LST averages, aggregated from the MMM method, to air temperature, we found a high correlation (R 2 = 0.84) with less variability (RMSE = 1.54 K). Where the trend was less steep and the y-intercept increased by 1.6 °C compared to the daily correlations. This effect is likely a consequence of LST temperature averages being differentially affected by cloud cover over warm and cold surfaces. We conclude that satellite infrared skin temperature (e.g., MODIS LST), which is often aggregated into multi-day composites to mitigate data reductions caused by cloud cover, changes in its relationship to air temperature depending on the period of aggregation.