Knowledge about land cover and its change is an important input for the monitoring and modeling of ecological and environmental processes from the regional to the global scale. Considerable efforts have been made to develop global continuous fields for different land cover types at large spatial scales based on NOAA-AVHRR and TERRA-MODIS data and a range of techniques have been applied to depict the sub-pixel fraction of land cover types from these data. In this study, a new methodology is described for deriving and optimizing continuous fields of tree cover for complex topography at the regional scale of the European Alps using generalized linear models (GLM). MODIS data (MOD09) at a spatial resolution of 500 m were used to calibrate the models against regional training data of fractional tree cover. For evaluating the method we test the GLM model output to a regression tree model (using the same data structure). Further we test the resulting GLM-based tree cover continuous fields against two different, independent test data sets; one of which is spatially separated and the other is from within the calibration area. Finally, we compare the GLM model output with two available global data sets at spatial resolutions of I km and 3 km: (1) TERRA-MODIS Vegetation Continuous Fields product (MOD44), and (2) the NOAA-AVHRR vegetation continuous fields. Our GLM-based method results in high accuracy. (MAE=9. 1%) and low bias (- 1.2%) across the combined evaluation and calibration area, and with small differences only between the calibration and the spatially separated evaluation area (1.3%). Compared to the regression tree model the results from the GLM model for all analyses are significantly better. Thus we conclude that generalized linear models are appropriate for deriving continuous fields of fractional tree cover for complex topography at the regional scale. GLMs can handle nonlinear relationships present in the training data set well, and the method is robust with respect to sample size and the number of months used for calibration. Regional calibrations of vegetation continuous fields may offer significantly improved predictions compared to globally calibrated models. Such regionally calibrated and optimized models may serve as valuable tools for regional monitoring of land cover pattern and its temporal change.