Contrastive-Learning-Based Time-Series Feature Representation for Parcel-Based Crop Mapping Using Incomplete Sentinel-2 Image Sequences  
[PERSON]  
1 College of Geography and Remote Sensing, Hohai University, Nanjing 211100, China; [EMAIL_ADDRESS] (Y.Z.) [PERSON]  
1 College of Geography and Remote Sensing, Hohai University, Nanjing 211100, China; [EMAIL_ADDRESS] (Y.Z.) [PERSON]  
2 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China  
can be challenging to distinguish between diverse crop types using a single satellite image, especially for crops grown during the same season. Exploring and learning time-series information from multi-temporal satellite images is, therefore, a promising method for improving crop classification [3; 6; 7; 8]. Additionally, optical satellite images are easy to comprehend and interpret, as well as some vegetation indices (such as the normalized difference vegetation index, NDVI) derived from optical spectral bands, which can indicate crop growth stages explicitly. Traditionally, agricultural remote sensing applications have relied heavily on satellite data from optical sensors such as MODIS, Landsat, SPOT, and the Chinese Gaofen [5; 9; 10]. However, due to the occlusion of clouds and shadows, for a special location, optical image observation sequences may be incomplete, as some observations can be missing. This poses a significant challenge for these methods (especially in cloudy and rainy regions). On the one hand, the absence of images at essential phenological stages could lead to inadequate crop classification performance. On the other hand, incomplete image sequences increase difficulties when following tasks and severely restrict the application of time-series crop monitoring [11; 12; 13]. Therefore, how to extract inherent time-series features that can distinguish crop types from these incomplete observation sequences becomes the key to remote sensing and crop mapping [14; 15]. Considerable research and effort have been devoted to constructing time-series features (representation) for improving crop classification [16], which can be categorized into three major groups, (1) important feature-based methods, (2) time-series composition methods, (3) time-series reconstruction methods, and others. Instead of reconstructing regular time-series images or features, important feature-based methods attempt to select prominent images captured during crucial phenological stages for crop identification [17]. For instance, rape and sunflower exhibit distinct yellow spectral features (with greater spectral reflectance on the red and green bands) during flowering, and paddy fields planted with rice seedlings are saturated with water, exhibiting higher water index values [3]. In other words, this method is based on an in-depth understanding of crop growth and phenology and attempts to identify crop types using minor but significant images. Some studies attempt to apply time-series filtering (such as S-G filtering) on incomplete multi-temporal images to manually generate phenological dates (such as the start or the end of the growing season) and then use these dates to identify crop types [18; 19; 20; 21]. However, such methods rely heavily on satellite images during crucial phenological stages, which may not always be available. In addition, these methods can only differentiate between crops with significant phenological differences, such as winter wheat and summer corn. It is challenging to distinguish crops during the same seasons (for example, soy and corn). Contrary to important feature-based methods, time-series composition methods attempt to use all available satellite images to construct more complete image sequences for crop classification (but with a longer time interval). In particular, the construction of satellite constellations significantly shortens their Earth observation (or revisit) periods.