Land Surface Temperature (LST) is an Essential Climate Variable (ECV) and is part of the surface energy budget. It can be used as indicator for climate change.
LST is calculated with the SurfTemp software, developed at DLR-DFD, which processes L2 LST and emissivity datasets from L1b brightness temperatures. The selected algorithms feature both a comparable high accuracy together with low sensitivity to input bands (Frey et al. 2017). For AVHRR/2 and AVHRR/3 the formulation provided by Becker and Li (1990) was selected. For AVHRR/1 - which does provide only one band in the thermal domain - the Qin et al. (2001) algorithm was chosen. For both formulations, new coefficients were calculated for a large range of atmospheric conditions and view geometries. The figure below shows the achievable accuracy in terms of the root mean square (RMS) and the standard deviation (STDEV) of these algorithms (please refer to Case A).
Both algorithms need the surface emissivity as input. This is calculated using the Vegetation cover method (VCM) from Caselles et al. (2012).
Maximum recorded LST during August 2009
Frey, CM; Künzer, C & Dech, S (2017) Assessment of Mono- and Split-Window Approaches for Time Series Processing of LST from AVHRR—A TIMELINE Round Robin. Remote sensing, 9(1): 72.
Caselles, E; Valor, E; Abad, F & Caselles, V (2012) Automatic classification-based generation of thermal infrared land surface emissivity maps using AATSR data over Europe. Remote Sens. Environ. 124, 321-333.
Becker, F; Li, ZL (1990) Towards a local split window method over land surfaces. Int. J. Remote Sens. 11, 369-393.
Qin, Z; Karnieli, A & Berliner, P (2001) A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. Int. J. Remote Sens., 22(18), 3719-3746.
Frey, CM; Künzer, C & Dech, S (2012) Quantitative comparison of the operational NOAA-AVHRR LST product of DLR and the MODIS LST product V005. International Journal of Remote Sensing, 33 (22), 7165-7183.