The global population growth, as well as the social and economic importance that the agricultural sector has in many regions of the world, makes it very important to develop methods to monitor the status of crops, to improve their management, as well as to be able to make early estimates of the agricultural production. One of the main causes of uncertainty in the production of crops is due to the weather, for example, in arid and semiarid regions of the world, periods of drought can generate big losses in agricultural production, which may result in famine. Thus, FAO, during their summit in June 2008, stressed the need to increase agricultural production as a measure to strengthen food security and reduce malnutrition in the world. Concern for increasing crop production, has generated, during the last decades, significant changes in agricultural techniques. For example, there has been a widespread use of pesticides, genetically modified crops, as well as an increase in intensive farming. In turn, the market influences crop rotations, and as a consequence, changes in the spatial distribution of crops are very common. Therefore, in order to make estimates of agricultural production, it is also necessary to map regularly the crop fields, as well as their state of development. The aim of this thesis is to develop methods based on remote sensing data, in the radar and optical spectral regions, in order to monitor crops, as well as a to map them. The results of this thesis can be combined with other techniques, especially with models of crop growth, to improve the prediction of crops. The optical remote sensing methods for classifying and for the cartography of crops are well established and can be considered almost operational. The disadvantage of the methods based on optical data is that they are not applicable to regions of the world where cloud coverage is frequent. In such cases, the use of radar data is more advisable. However, the classification methods using radar data are not as well established as the optical ones, therefore, there is a need for more scientific studies in this field. As a consequence, this thesis focuses on the classification of crops using radar data, particularly using AIRSAR airborne data and ASAR satellite data.