For instance, the revisit periods of the Sentinel-2 constellation with two satellites and the virtual constellation between Landsat-8 and Landsat-9 are five and eight days, respectively. Multiple images are captured by these satellites during a particular phenological stage. Therefore, these images with close acquisition dates can be composited and mosaiced to produce composited images with lower cloud/shadow coverage [22]. Such approaches can significantly enhance the completeness of time-series observations. However, such methods expand the spectral ranges of crop phenological stages, resulting in mixed feature spaces and overlapping type spaces for crop mapping [23]. Meanwhile, this kind of method has limited improvement in crop classification. In addition, they cannot completely eliminate the missing values to construct regular time-series observations in cloudy and rainy regions. Time-series reconstruction methods are promising alternatives for dealing with incomplete time-series observations. By exploring spatial similarity, spectral correlation, and temporal trends, time-series reconstruction can predict cloud- and shadow-covered pixels to generate regular time-series images [12; 13]. Compared to time-series composition methods, these methods produce time-series images with original (or even shorter) time intervals, which are practical and effective in time-series crop classification. Nevertheless, a few studies [12; 24] found that a larger percentage of missing data (including significant gaps in timestamps and large-area missing) results in greater uncertainty and over-smoothing, which could mislead subsequent time-series analysis. In addition, time-series reconstruction methods necessitate additional effort. To address the issue of incomplete time-series data, another idea is to design algorithms that are capable of utilizing incomplete time series directly. Recent developments in machine learning have also begun to address the issue of an incomplete time-series analysis. For instance, by default, the eXtreme Gradient-Boosting (XGBoost) algorithm can handle missing values. Whenever a missing value is encountered during XGBoost-based prediction, a default (right) direction is created at each tree node [25; 26]. In addition, masking layers can be used to identify the missing position in time-series data and then feed them directly into Long Short-Term Memory (LSTM)-based networks. Despite this encouraging improvement in time-series feature representation for crop classification using incomplete image sequences achieved using the aforementioned methods, these approaches have a number of limitations: (1) Existing techniques have not established a general framework for constructing or learning inherent time-series feature representations from incomplete image sequences. Moreover, manually crafted features are limited in their ability to identify specific crop types or phenological periods. (2) Supervised LSTM-based methods typically require a large number of labeled samples for training. It is, thus, difficult to directly apply them in remote sensing applications with limitations in labeling [3]. (3) From the standpoint of implementation, algorithms like XGBoost use a default or assumed trick to handle missing values in time-series data, as opposed to producing regular feature representation. Therefore, can a general framework be developed to represent inherent time-series features from incomplete image sequences in crop classification? Recent research has focused more on self-supervised learning to extract effective representations from unlabeled data. Self-supervised pre-trained models with limited labeled data can achieve comparable performance to supervised models trained on complete and labeled data. Particularly, contrastive learning has recently demonstrated its strength for self-supervised representation learning in the computer vision domain due to its capacity to learn invariant representations from augmented data [27; 28].