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In this study we examine the relationship between remotely sensed, in situ and modelled land surface temperature (LST) over a heterogeneous land-cover (LC) enclosed in alpine terrain. This relationship can help to understand to what extent the remotely sensed data can be used to improve model simulations of land surface parameters such as LST in mountainous areas. LST from the MODerate resolution Imaging Spectro-radiometer (MODIS), the modelled surface skin temperature by the Weather Research and Forecasting (WRF) mesoscale numerical model and the in situ measurements of surface temperature are used in the analysis. The test-site is located in a mountain valley in the Southern Alps of New Zealand. Geospatial analysis in GIS is used to relate pixels, grid-cells and points from the MODIS LST, model simulations and the in situ data, respectively. Differences between LST from MODIS, the WRF model and the in situ data are presented with respect to surface LC at different times of day. Initial results from regression analysis of the three datasets showed a goodness of fit R2 coefficient of 0:77 for the model simulations and 0:35 for the MODIS LST. These values improved significantly when time-lags were considered and the few outliers were removed, giving R2 values of 0:80 for the model and 0:73 for the MODIS LST. These results show that the WRF model correlates better with the in situ measurements over various LC types in this region compared with the MODIS LST. Longer time-series, however, are required to draw more robust conclusions about the applicability of the MODIS LST product for improving WRF simulations over alpine complex terrain

Analysis of MODIS LST Compared with WRF Model and in situ Data over the Waimakariri River Basin, Canterbury, New Zealand