Precision of mono- and split window algorithms for LST estimation from AVHRR

Kurzfassung

In order to re-process DLRs 1km AVHRR data archive to different geophysical and descriptive parameters of the land surface and the atmosphere, a series of scientific data processors is being developed in the framework of the TIMELINE project. One of the parameter is the land surface temperature (LST), which is being derived with the SurfTemp data processor. SurfTemp processes L2 LST and emissivity datasets on the basis of AVHRR top of atmosphere brightness temperatures, Era Interim Water Vapour data, the Fraction of Vegetation Cover (FVC), and a land use classification. The development of the data processor included the selection of statistical procedures suitable for time series processing, as the data archive ranges back to the 80ies. As such, the procedures included four mono-window and six split window algorithms. For almost all of these algorithms, new constants were generated, to better account for different atmospheric and geometric acquisition situations. The selection of optimal algorithms for SurfTemp is based on a round robin approach, in which the selected mono-window and split window algorithms are tested on the basis of a large number of TOA radiance/LST pairs, which were generated using a radiative transfer model and the SeeBorV5 profile database. The original LSTs are thereby compared to the LSTs derived from the TOA radiances using the mono- and split window algorithms, revealing measures of precision. Further, a sensitivity assessment of emissivity and LST estimation is shown, to estimate the influence of the accuracy of various input bands to the emissivity and LST estimation.