Observing the response of terrestrial vegetation to climate variability across a range of time scales by time series analysis of land surface temperature

Abstract

Satellite observations of the terrestrial biosphere cover a period of time sufficiently extended to allow the calculation of a reliable climatology. The latter is particularly relevant for studies of vegetation response to climate variability. Observations from space of the land surface are hampered by clouds at shorter wavelength and affected by water in the atmosphere in the microwave range. Both polar orbiting and geostationary satellites have a revisit frequency high enough to allow for some redundancy relative to the processes being observed, so that time series where a fraction of observations are removed and the resulting gaps filled are still very useful to monitor land surface processes. Two examples illustrate this concept in two different spectral regions: Thermal Infrared (TIR) and observations of land surface temperature to study the thermal behavior of the land surface in response to weather and climate and 37 GHz observations of the polarization difference in brightness temperature to retrieve the fractional abundance of watersaturated soil. Three applications of time series of land surface temperature are presented: (a) monitoring of spectral thermal admittance of the land surface; (b) estimation and mapping of air temperature and (c) monitoring of thermal load to assess the risk of forest fires. Two methods were applied to identify and remove anomalous observations (outliers) and to fill the resulting gaps: Harmonic ANalysis of Time Series (HANTS) and theMultichannel Singular Spectrum Analysis (M-SSA). The HANTS algorithm has been widely used to reconstruct time series of Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Land Surface Temperature (LST) as well as the polarization difference brightness temperature (PDBT) during the past 20 years to remove random noise or eliminate cloud/snow contamination. The M-SSA, an advanced methodology for time series analysis, was utilized to reconstruct gap-free LST time series using both the spatial and the temporal information content in the data set.