Modeling annual parameters of clear-sky land surface temperature variations and evaluating the impact of cloud cover using time series of Landsat TIR data

Land surface temperature (LST) is of primary importance in understanding global environment change, urban climatology, and land-atmosphere energy exchange. Analysis of long-term remotely sensed LST data remains a challenge for researchers. Most previous studies explored urban thermal pattern over space and time using a limited number of clear-sky images or by removing cloud-contaminated pixels. The limitation in the number of image scenes prevents from deriving long-term LST climatology for a particular region. Moreover, simply eliminating cloudy pixels inevitably obscures the spatial and temporal patterns of LST. This research attempts to characterize the annual and seasonal temperature behaviors during the period of year 2000 to year 2010 in Los Angeles by employing an annual temperature cycle (ATC) model. All 115 image scenes (path 41, row 36) of less than 30% of cloud cover available from the Landsat archive were utilized for the analysis. Three ATC parameters, i.e., mean annual surface temperature (MAST), yearly amplitude surface temperature (YAST), and the phase shift, were optimized with the Levenberg-Marquardt minimization scheme to understand the annual and seasonal characteristics of LST. The overall RMSE of 7.36 K was achieved for the optimization for all 115 images; while for the median monthly composite, the RMSE reached 2.85 K. The monthly median composite partly removed day-specific anomalies and the impact of cloud cover and reflected LSTs largely under clear sky conditions, leading to the improvement in the modeling result. The MAST and YAST derived from the modeling result of the monthly median composite data were further analyzed to relate to three normalized indices, i.e., normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference built-up index (NDBI). The results showed that the mean temperature of urban areas in LA was as high as in the barren land area, reaching almost 310 K, but the urban areas possessed a less fluctuation. Seasonal analysis suggested that in winter, the urban areas observed a higher temperature than the barren land/desert, implying that urban materials held a larger amount of heat for a longer time than the barren land. The separation of cloudy scenes into cloud percentage groups made it possible for the evaluation of the effect of cloud cover on the ATC modeling process. The sensitivity analysis indicated that the inclusion of cloudy images brought about a decrease in MAST ranging from 0.18 to 2.0 K, depending on the percentage of cloud cover and the number of cloudy scenes used for the modeling. The decrease is due likely to the inclusion of cloud temperatures in analysis rather than shaded or other land surface temperatures. When all cloudy images were included in the modeling, a decrease of 2 K in MAST and an increase of 0.15 in YAST were observed. The regression analysis demonstrated that NDBI and NDVI were the main factors influencing the spatial variations of MAST and YAST with the R2 value of 0.63 and 0.49, respectively. In addition, the spatial variation of YAST was found more complex than that of MAST, since the three indices can only explain up to 53% of its variance.