## 1 Introduction  
Crop area identification plays a significant role in food security [1, 2]. Timely and accurate crop spatial distribution maps are essential for agricultural production, crop planting structures, and the formulation of national food policies [3, 4]. The traditional approaches for acquiring information on crop planting areas relied on manual measurement and statistical sampling, resulting in time consumption, the wastage of human resources, and inefficiencies [5]. Over the past few years, remote sensing (RS) technology has proven effective in monitoring the dynamics of crop-growing areas on a regional or global scale owing to its advantages in rapidity and accuracy [6; 7; 8]. Many researchers have utilized RS images with temporal features of crop growth to map crop planting areas [9; 10; 11]. The temporal features derived from time series captured by Moderate Resolution Imaging Spectroradiometer (MODIS) data, such as normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), include different stages of growth and development in crops [12; 13]. Time series of vegetation indices (VI) have provided rich and valuable information in detecting crop growth variations and spatial planting distribution [14]. Based on the temporal data derived from MODIS images, [PERSON] et al. [15] and [PERSON] et al. [16] combined the NDVI and EVI with Attention-based Long Short-term Memory networks (A-LSTM) and phenological information to identify the winter-wheat area in Huanghuaihai region and corn in Northeast China, respectively. [PERSON] et al. [17] inserted NaE (a combination of NDVI and EVI) into the model and found that the classification results were better than the data from the use of either the NVDI or the EVI. The NaE inserts both the NDVI and the EVI into the model simultaneously, which will effectively avoid data collection errors due to MODIS sensors, clouds, or rain, thereby in turn effectively avoiding classification errors caused by data loss or measurement errors and allowing the model to extract more features. Therefore, the NaE was used as input data in this study. Various methods have processed data in multi-temporal RS to extract temporal information or phenological metrics for crop classification [18; 19]. The traditional methods for crop identification are simple statistics, threshold-based equations, and pre-defined mathematical equations [20]. [PERSON] et al. [21] combined inflection and threshold-based methods to detect the key phenological stages of maize, wheat, and rice for crop distribution across China. [PERSON] et al. [22] used a fused representation-based algorithm for cotton area identification and obtained the best accuracy result of 79.46%. Although the above approaches in temporal feature extraction provide many options for crop classification, there are some problems. Firstly, the threshold-based methods relied on manual work, and the model design depended on human experience and domain knowledge. This is a time-consuming and ineffective process [20]. The temporal feature extracted from time series based on human experts' experience will result in an information loss in granular data and the incomplete utilization of interval features. Secondly, pre-defined mathematical functions inevitably constrain flexibility in handling temporal data; selecting a suitable function for all crop types presents challenges, especially in diverse crop classification studies [14]. The dynamic field of deep learning offers solutions to address these weaknesses. In recent years, using deep learning classification methods for identifying crop distribution has achieved positive outcomes [23; 24; 25]. One advantage of the deep learning method is its flexibility, as it does not confine itself to predefined models [20]. The models learn feature representations from data in an end-to-end form, avoiding the need for manual feature engineering based on human experience and prior knowledge [26].