Publications

Quan, JL; Zhan, WF; Chen, YH; Wang, MJ; Wang, JF (2016). Time series decomposition of remotely sensed land surface temperature and investigation of trends and seasonal variations in surface urban heat islands. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 121(6), 2638-2657.

Abstract
Previous time series methods have difficulties in simultaneous characterization of seasonal, gradual, and abrupt changes of remotely sensed land surface temperature (LST). This study proposed a model to decompose LST time series into trend, seasonal, and noise components. The trend component indicates long-term climate change and land development and is described as a piecewise linear function with iterative breakpoint detection. The seasonal component illustrates annual insolation variations and is modeled as a sinusoidal function on the detrended data. This model is able to separate the seasonal variation in LST from the long-term (including gradual and abrupt) change. Model application to nighttime Moderate Resolution Imaging Spectroradiometer (MODIS)/LST time series during 2000-2012 over Beijing yielded an overall root-mean-square error of 1.62K between the combination of the decomposed trend and seasonal components and the actual MODIS/LSTs. LST decreased (similar to -0.086K/yr, p<0.1) in 53% of the study area, whereas it increased with breakpoints in 2009 (similar to 0.084K/yr before and similar to 0.245K/yr after 2009) between the fifth and sixth ring roads. The decreasing trend was stronger over croplands than over urban lands (p<0.05), resulting in an increasing trend in surface urban heat island intensity (SUHII, 0.0220.006K/yr). This was mainly attributed to the trends in urban-rural differences in rainfall and albedo. The SUHII demonstrated a concave seasonal variation primarily due to the seasonal variations of urban-rural differences in temperature cooling rate (related to canyon structure, vegetation, and soil moisture) and surface heat dissipation (affected by humidity and wind).

DOI:
10.1002/2015JD024354

ISSN:
2169-897X