###### Abstract  
Accurate and reliable crop classification information is a significant data source for agricultural monitoring and food security evaluation research. It is well-known that polarimetric synthetic aperture radar (PolSAR) data provides ample information for crop classification. Moreover, multi-temporal PolSAR data can further increase classification accuracies since the crops show different external forms as they grow up. In this paper, we distinguish the crop types with multi-temporal PolSAR data. First, due to the \"dimension disaster\" of multi-temporal PolSAR data caused by excessive scattering parameters, a neural network of sparse auto-encoder with non-negativity constraint (NC-SAE) was employed to compress the data, yielding efficient features for accurate classification. Second, a novel crop discrimination network with multi-scale features (MSCDN) was constructed to improve the classification performance, which is proved to be superior to the popular classifiers of convolutional neural networks (CNN) and support vector machine (SVM). The performances of the proposed method were evaluated and compared with the traditional methods by using simulated Sentinel-1 data provided by European Space Agency (ESA). For the final classification results of the proposed method, its overall accuracy and kappa coefficient reaches 99.33% and 99.19%, respectively, which were almost 5% and 6% higher than the CNN method. The classification results indicate that the proposed methodology is promising for practical use in agricultural applications. p 2021 27 May 2021 6  
respectively [11]. However, optical remote sensing data is susceptible to cloud and shadow interference during the collection, so it is difficult to obtain effective continuous optical remote sensing data in the critical period of crop morphological changes. In addition, optical remote sensing data only reflect the spectral signature of target surface. For the wide variety of ground objects, there exists the phenomenon of \"same object with different spectra and different objects with the same spectrum\". Therefore, the crop classification accuracy based on optical remote sensing data is limited to a certain extent. Unlike the optical remote sensing, PolSAR is an active microwave remote sensing technology, its working conditions cannot be restricted by weather and climate. Meanwhile, besides the signature of target surface, SAR remote sensing data provide other spectral signatures of target due to its penetrability. Therefore, increasing amounts of attention has been paid to the research with PolSAR data in crop classification [12,13]. However, the constraints of developing level for radar technology, the majority of classification research for crops used single-temporal PolSAR data. However, as for crop categories, identification, single-temporal PolSAR image offers only limited information for crops. Therefore, it is very difficult to identify different crop categories due to the same external phenomena in the certain period, especially during the sowing period [14]. Therefore, it is necessary to collect multi-temporal PolSAR data to further improve the crop classification accuracy. In recent two decades, an increasing number of satellite-borne SAR systems have been launched successfully and operate on-orbit, which made it available to acquire multi-temporal remote sensing data for desired target [15, 16, 17]. At present, there are several representative systems available for civilian applications, such as L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) [18], C-band Sentinel-1 [19,20], GF-3, RADARSAT-2 and Radarsat Constellation Mission (RCM) [21], and X-band Constellation of Small Satellites for Mediterranean basin observation (COSMO) and COSMO-SkyMed 2 nd Generation (CSG) [22]. Through these on-orbit SAR systems, a number of multi-temporal PolSAR images for the same area can be readily acquired for crop surveillance and other related applications.