Increasing demands for lasting and environmentally conscious use of natural resources together with a cost effective and restrictive use of fertilizers and pesticides require the employment of new technologies in agriculture. The preliminary results presented here consist in the automatic land use classification of agricultural fields based on multi-temporal TerraSAR-X images in dual polarization obtained in the high resolution Spotlight mode of the satellite. The classified data in turn can be used to enhance and validate existing models on ground water quality as a function of agricultural usage and soil treatment. Within the past years investigations have been carried out on the usefulness of ENVISAT ASAR dual polarimetric data for environmental mapping in the same area, which show some deficiencies mainly because of the spatial resolution of the data, which was too coarse for many cultivations and could not reflect agricultural treatments (irrigation, fertilization, soil and plant treatment) sufficiently. However, the ENVISAT investigations showed that a proper selection of images out of a time series according to the crop-calendar of that region is beneficial and gives in general more accurate results than using all of the images. This is due to the fact that some fields are covered by different types of crops during the year and such sequence is often hard to model because it is usually governed by phenologic, ecologic, and economic reasons. The latter might be influenced either from sudden change of global or national economic constraints (e.g., oil prize, taxes, and subsidies), by strategies of individual farmers, or both. In this paper, results obtained from multi-temporal classifications of TerraSAR-X image pairs (HH and VV) covering a whole season (11 images from March to November) are presented. Even though the temporal grid was irregular (revisit time was nine times 22 days and once 44 days) in every month at least one pair was available. The investigations and results are based on standard pixel based Maximum Likelihood classification techniques, which however are amended by the use of regional crop calendar conditions and rules to account for seasonal variations of specific cultivations with respect to permanent crops. Results obtained have been compared to ground truth, which has been carried out in-situ to the satellite measurements. It can be shown that even when not using all images of the year, but only those which are indicated by the crop-calendar or those which show high loadings using Factor Analysis a considerable classification accuracy of more than 90% can be achieved. Besides, the crop-calendar, which has been set-up using ground observations can be verified or sometimes improved by this method. The accuracy obtained, can be improved by different types of pre-processing (i.e., filtering) as is demonstrated. Some remaining discrepancies for some species can be explained by investigating the structural behaviour of the plants on ground as compared to close range photos being taken during ground truth. As could be demonstrated the use of time-series of images from TerraSAR-X despite of frequent cloud cover offers an excellent tool for monitoring crops and serve as indicator for the estimation of the amount of fertilizers used within that area. Using this information, farmers could improve their efforts in establishing good agricultural practice, as being claimed by recent legal and environmental jurisdiction. In future work the validity of this work, which is limited by the small amount of training and control fields will be extended and also other modern classification techniques applied, such as support vector machine.