Suitability of split window algorithms for AVHRR LST processing using updated parameter sets

Kurzfassung

In the context of the TIMELINE project, which deals with time series processing of AVHRR data, a LST processor ‘SurfTemp’ is being developed to estimate land surface temperatures (LST) from the thermal channels of AVHRR. The SurfTemp processor aims to produce LST with highest possible accuracy given the constraints of old data and missing information about atmospheric composition in the past. Each pixel of the product shall be accompanied by an uncertainty and an accuracy measure. This requirement asks for a strict algorithm choice and method development. Many – but not all - of the AVHRR sensors flying on NOAA satellites feature two thermal channels, enabling the application of a split window algorithm to retrieve LST. Six different split window formulations are being extensively tested, by generating new parameter sets for each equation, selected atmospheric, viewing, and surface conditions, and each AVHRR sensor. The parameter generation is executed by least square minimization with simulated brightness temperatures, which were provided by varying radiative transfer model runs with MODTRAN using a precompiled atmospheric profile database. The parameters are derived three fold: a) for four different view angles, b) additionally for eight ranges of columnar water vapour, and c) additionally for four ranges of surface temperatures, resulting in three parameter sets for each formulation. Furthermore, the parameter sets are split to daytime and nighttime conditions. The six split window formulations with the newly generated parameters are then being compared in terms of a) accuracy of the resulting LST – derived from comparison with other simulated data – and b) the formulations sensitivity on their input parameter (e.g. emissivity or columnar water vapor). The analysis of sensitivity is considered as necessary, as the quality of additional input data besides the AVHRR thermal channels into LST retrieval may form a critical limitation to the accuracy of the final output product. The results are discussed in terms of suitability of each of the formulations together with the new parameter sets to time series processing of AHRR data.