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Uncertainties in empirical downscaling of land surface temperature

Land surface temperature (LST) is an important quantity in land surface-atmosphere interaction can in principal be well monitored by thermal infrared satellite remote sensing. Especially the diurnal temperature cycle (DTC) reveals information about relevant surface characteristics like thermal inertia. However, the acquisition is severely restricted by the orbital parameters of the sensors. The high temporal resolutions required for DTC monitoring can only be achieved from geostationary satellites at an altitude of about 36000 km and hence a poor spatial resolution (about 3 to 5 km depending on sensor and latitude). On the other hand, sensors in geostationary orbits with small swath width have higher spatial resolution (about 100 m) but a long revisit time of one to two weeks. To close this gap, in this case study a downscaling scheme for LST from the SEVIRI sensor onboard the geostationary meteorological Meteosat Second Generation to spatial resolutions of down to 100 was developed and tested for Hamburg. Therefore, aggregated parameters from multi-temporal thermal data were applied like following. First, the predictors were upscaled to the coarse resolution, then a relationship between predictors and LST was empirically calibrated, which eventually was transferred to the higher resolution domain. The downscaling was validated with LST data from ASTER and ETM+ for single times as well as longer time series of in situ LST measurements for single locations. The validation with the ASTER data showed good results with a high R2 of up tp 0.71 % and relatively low RMSE of about 2.2 K. The results with the ETM+ data were slightly worse due to the limited quality of the LST retrieval algorithm. Compared to the in situ data the DTC were essentially well represented but the downscaling did not improve the accuracy.