Abstract: Abstract: Crop recognition is the initial phase and key link of an agricultural condition monitoring system. The accurate identification of a crop can achieve a good estimation for crop sown acreage, planting structure, and spatial distribution, as well as provide key input parameters for a crop yield estimation model. Due to that crop sown acreage, yield information is the important basis for making national food policy and an economic plan. Therefore, it is very important to conduct the study on crop identification. In view of the advantages of high temporal resolution, wide coverage, and low cost, remote sensing has been used in a wide array of earth observation activities, and thus provides a useful tool for crop recognition and planting acreage monitoring on a large scale. Since the 1980's, optical remote sensing has been widely used to identify various crops, and consequently, it has made obvious progress, no matter whether in the aspect of theory and technology. However, optical images are not often available in the key growth period of crops, owing to the cloudy and rainy weather. Thus, it has a negative effect on the accuracy and timeliness of crop area monitoring. As a new high-technology with an advantage of all-weather, all-time, high resolution, and wide coverage, synthetic aperture radar (SAR) has been widely applied in the agricultural condition monitoring field and thus provides a strong complement and support for crop identification in the data and technology aspects. As the updating and improvement of function parameters and performance index of radar sensors, it has been an important field of agriculture remote sensing in obtaining the information of crop sown acreage, growing condition, and yield by SAR. In this paper, according to a mainline of the development progress of radar technology in the recent twenty years, the progress of studies and applications on crop discrimination by SAR is systematically summarized, and the conclusion includes four aspects: the first is that early studies (from the late 1980's to 2002), are characterized by using single band, single polarization, and multi-temporal SAR data for crop identification; The second is crop acreage monitoring based on multi-polarization, multiband SAR data. Furthermore, this section can be divided into two subsections: one is crop recognition by multi-polarization SAR, the other is using multiple SAR sensors for crop classification; The third is studies on improving the accuracy and efficiency of crop identification by combining SAR with optical remote sensing; The last is the studies on crop classification algorithm using SAR data. According to the summary of previous studies, the problems existing in the crop identification by SAR can be analyzed as follows: the first is that crop types identified by SAR are still single; the second is that the accuracies of crop identification are not yet high; the last is that mechanism studies on the classification algorithm are lacking. Furthermore, the development trends are presented in this study. Dryland crop discrimination using SAR images under a complex crop planting structure, improving the accuracy and timeliness of crop identification by optimizing the operational parameters (e.g. polarization, frequency and incidence angle) of SAR system and combining it with optical remote sensing, and developing the mechanism-based algorithm of crop classification will be three area that will urgently be needed to be studied in the future.