Time series corrections and analyses in thermal remote sensing

Abstract

The time span of surface thermal data bases now reaches a few decades. However, studies using surface thermal time series are seldom, due to the difficulty of obtaining temporally coherent estimations for this parameter. Applications for surface thermal multitemporal analysis range from climate change studies and modeling to anomaly detection for natural or industrial hazard detection. This chapter presents methods to improve the temporal coherence of temperature time series, through data reconstruction of atmospheric and cloud contaminated observations, and through the correction of the orbital drift effect which hinders the use of the longest data sets. Then, methods for the analysis of time series are presented, including both image to image comparison and trend detection, the choice between these methods depending on the spatial resolution of the dataset and the aims of the considered study.