© 2008 International Society for Photogrammetry and Remote Sensing. All rights reserved.The aim of this study was to compare percent tree cover products of Envisat MERIS and MODIS data of Seyhan River Basin at the Eastern Mediterranean Region of Turkey. In this study, Regression Tree (RT) algorithm was used to estimate percent tree cover maps. This technique is well suited for percentage tree cover mapping because, as a non-parametric classifier, it requires no prior assumptions about the distribution of the training data. This model also allows for the calibration of the model along the entire continuum of tree cover, avoiding the problems of using only end members for calibration.The medium resolution Envisat MERIS with a 300 m and MODIS with a 500 m pixel representation data were used as predictor variables. Three scenes of high resolution IKONOS images were employed as a training data, and testing the accuracy of model. The regression tree method for this study consisted of six steps: i) generate reference percentage tree cover data, ii) derive metrics from Envisat MERIS and MODIS data, iii) select predictor variables, iv) fit RT model, v) undertake accuracy assessment and produce final model and map, vi) compare results. The training data set was derived supervised land cover classification of IKONOS imagery to generate reference percent tree cover data. Specifically, this classification was aggregated to estimate percent tree cover at the MERIS and MODIS spatial resolution.The predictor variables incorporated the MERIS and MODIS wavebands in addition to biophysical variables estimated from the MERIS and MODIS data. Percent tree cover maps were derived from MERIS and MODIS data for Seyhan upper Basin as final outputs. These final outputs consisted of spatially distributed estimates of percent tree cover at 300 m and 500 m spatial resolution and error estimates obtained through validation. This study showed that Envisat MERIS data can be used to predict percentage tree cover with greater spatial detail than using MODIS data. This finer-scale depiction should be of great utility for environmental monitoring purposes at the regional scale.