## 1 Introduction  
Spatiotemporal Fusion (STF) integrates remote sensing data from different sensors and time points [1, 2, 3], aiming to generate time series remote sensing data with high temporal and spatial resolution. STF effectively addresses the trade-off between temporal and spatial resolution in remote sensing imagery, making it widely applicable in areas such as agricultural monitoring [4, 5, 6], ecosystem dynamics research [7], and urban heat island analysis [8, 9]. Traditional methods, including those based on unmixing [10, 11, 12], weighted functions [13, 14, 15], Bayesian [16, 17], and machine learning techniques [18, 19], have established the theoretical foundation for STF. While these methods are simple and unsupervised, their reliance on linear assumptions limits their ability to model complex remote sensing features. In recent years, deep learning (DL), with its powerful nonlinear modeling capabilities and automated feature learning, has transformed the research paradigm of STF. Convolutional neural networks (CNNs) are the most widely adopted for STF applications among diverse neural network designs. Models such as DCSTFN [20] and EDCSTFN [21] employ fully end-to-end architectures, using separate CNN branches to extract fine and coarse features. Generative adversarial networks (GANs) address the issue of overly smoothed fusion results, often encountered when training solely with CNNs, by leveraging adversarial loss. For example, GAN-STFM [22] integrates conditional GANs and switchable normalization techniques into STF. Similarly, MLFF-GAN [23] incorporates U-Net-like multi-level feature fusion into the GAN framework. Both approaches use CNNs as the backbone for generator and discriminator networks. However, CNNs struggle to capture long-range dependencies in large-scale spatiotemporal data and adapt to significant heterogeneity due to their fixed weight-sharing mechanism. Transformers [24; 25], which utilize self-attention mechanisms for dynamic weight adjustment, have been applied to STF to overcome these limitations. For instance, SwinSTFM [26] effectively combines window-based self-attention [27] with linear unmixing theory, significantly improving spatiotemporal feature modeling capabilities. Additionally, several studies combine CNNs and Transformers to exploit their complementary strengths. STF-Trans [28] employs CNNs as shallow feature extractors and Vision Transformers for deep modeling of long-range dependencies. CTSTFM [29] introduces spatial and channel attention modules along with cross-attention mechanisms to achieve efficient feature extraction and fusion. In addition to the selection and optimization of backbone networks, DL-based STF methods can also be examined in terms of their core tasks: temporal change capture or spatial detail enhancement. DL methods focused on temporal changes primarily analyze the relationship between images captured at different time points. These methods emphasize capturing variation features within time series and constructing fine-grained difference features. Some approaches directly utilize coarse-difference images and fine images as input. For instance, StfNet [30] adds the coarse-difference images and fine images before feeding them into a CNN-based fusion network. Similarly, PDCNN [31] and STFMCNN [32] stack these inputs for processing through the fusion network. DMNet [33], AMNet [34], and STFDSC [35] extract features from input data using CNNs and subsequently stack or sum these features before passing them to the fusion network. Additionally, networks like DCSTFN [20], EDCSTFN [21], and PSTAF-GAN [36] directly process sequential coarse images, employing difference modeling on the extracted coarse features to enhance temporal fusion. In contrast, DL methods focusing on spatial details emphasize the scale relationship between coarse and fine images captured on the same date.