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Land Surface Temperature (LST) data around Kathmandu Valley of Nepal from 2000 to 2015 were analyzed to determine the temperature patterns. The cubic spline function was used to find the seasonal patterns, which were similar for all sub-regions, with a single summer peak and a winter trough. The data were then seasonally adjusted to remove seasonal effects and filtered with the first-order autocorrelation. The second-degree polynomial regression model identified fifteen different patterns; 65.4% of the area had an ‘accelerating’ pattern and 34.6% had a ‘non-accelerating’ pattern. The logistic regression confirmed that the patterns were significantly associated with altitude (P = 0.006). This study identified a varying pattern of temperature by location and time and the methods can be generalized to larger areas.

Modeling of Land Surface Temperatures to Determine Temperature Patterns and Detect their Association with Altitude in the Kathmandu Valley of Nepal