Temporal Dynamics of Land Surface Temperature From Landsat TIR Time Series Images

Land surface temperature (LST) is a valuable parameter in studies of surface energy balance, landscape thermal patterns, and human–environment interactions. An effective way to quantify the LST dynamics over spatial and temporal domains is to utilize the consistent Landsat thermal infrared (TIR) data since 1982. Currently, only a small proportion of studies utilized the Landsat TIR data for investigating both the intra- and interannual LST variations. The objectives of the study are to provide statistical evidence for the existence of the annual temperature cycle (ATC) and to develop a decomposition technique to explore landscape thermal patterns by land cover. Eighty-two cloud-free TIR images of Los Angeles County from Landsat TM between 2000 and 2010 were collected and consistently calibrated to the LSTs. The LSTs were then analyzed by the Lomb–Scargle periodogram technique to test whether the time series LSTs showed rhythmic patterns and by a decomposition model to analyze the intra- and interannual landscape thermal patterns. The periodogram analysis confirmed that ATC was statistically significant with the periodic time of 362 days. Furthermore, sensitivity analysis showed that the Lomb–Scargle technique can still discover the ATC with the difference of up to five days, even when the number of images decreased to 60. Based on the periodogram analysis, a decomposition model was initialized to disassemble the time series LSTs into seasonality and trend components for comparisons among land covers. Results suggested that the developed areas exhibited relatively low seasonal amplitude of 11.7 K, while largest mean annual LST value is 302.8 K. The difference of the averaged trend component between urban and other land covers reached 1.1 K over the decade. Future research may be directed in dealing with the time-varying seasonality component for better quantifying the thermal patterns.