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Long-term MODIS time series Land Surface Temperature (LST) observations over coal fire affected areas were utilized for detection and characterization of coal fire as a function of its temporal intensity variation (e.g. growing, temporally consistent or diminishing) on annual basis in Jharia coalfield. LST pixel time series (LPTS) vectors were generated for selected coal fire and non-coal fire locations using 782 LST maps of the duration 2001 − 2017. LPTS vectors were decomposed to extract the nonlinear trend component using Seasonal Trend decomposition based on Loess model. Background-referenced trends were generated for coal fire pixels. Slope,  p -value of Mann-Kendall test and average annual deviation parameters were calculated for annually segmented background-referenced coal fire trends to characterize coal fire on annual basis and to separate coal fire induced anomalous trend and non-coal fire trends. The results were validated with that of published literature.

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MODIS land surface temperature time series decomposition for detecting and characterizing temporal intensity variations of coal fire induced thermal anomalies in Jharia coalfield, India