## 1 Introduction  
The Northeast Black Soil Region is China's main commercial grain base [1, 2, 3]. Timely and accurate access to crop planting information in the early stages of the Black Soil Region is of great significance to improving agricultural management and productivity and ensuring national food security [4, 5]. Remote sensing has proven to be a practical and efficient way to obtain information for crop mapping [6]. According to different monitoring phenological periods, current crop identification can be divided into pre-season, mid-season, and post-season crop identification, as early identification has relatively little research due to the lack of available image and sample data and the difficulty of capturing the characteristic information of the crop at the crop early stage [7]. However, due to sufficient image and sample data, satisfactory identification accuracy has been achieved in mid-season and post-season, but the mapping results are obtained relatively late and cannot meet the demand of relevant departments for timely access to crop acreage information [8; 9]. In contrast, early crop identification can obtain crop planting information at an earlier time and is more practical, which can provide timely information reference for planting management and food production security, make the formulation of relevant government policies more directional, and provide a guarantee for the healthy and sustainable agricultural development [10; 11]. Finding distinguishable features of crops in the early growing season is essential to improving the accuracy of early crop identification [12]. Currently, commonly used remote sensing identification features include spectral, spatial, temporal, and polarization features, as well as auxiliary features such as Digital Elevation Models (DEMs) [13; 14; 15]. Among them, temporal characteristics can reflect crop growth and development during the growth stage. Many researchers have found that combining temporal characteristics with other features is conducive to improving crop identification accuracy at the early growth stage [16; 17; 18]. Crop identification methods based on single-temporal remote sensing imagery distinguish ground objects by finding the features of crops that differ significantly in the \"critical period\". For example, rice in the irrigation period, rapeseed in the flowering period, and cotton in the boll opening period appear white, and all have more distinctive characteristics compared to other crops in the key phenological period. Early identification of these crops can be achieved by finding sensitive bands and constructing indices to enhance their performance with only one phase of imagery [19; 20; 21]. However, homozygosity and heterozygosity are more serious for areas with complex crop planting structures, which may lead to low recognition accuracy. Multi-temporal remote sensing data can effectively capture the spectral confusion between crops caused by different crop phenological periods and effectively improve crop discriminability, which has been widely applied to remote sensing crop recognition [22; 23]. Some researchers have collected images of crops over the entire fertility period from sowing to pre-harvest, then used multi-source remote sensing data fusion and multiple remote sensing indices to improve crop information and identify crops earlier. [PERSON] et al. [24] achieved early identification of corn, rice, and soybeans by collecting images of the entire growth period and integrating them with multiple time-phased information via an incremental design to make up for the lack of a single phase. In addition, some researchers have used different curve shapes in the time-series images of different crops to identify early-stage crops by mining obvious differences at certain periods or time points. They then used relevant decision knowledge or similarity matching to set appropriate thresholds for these differences. For example, [PERSON] et al.