## 1 Introduction
These methods often approach the STF task as a super-resolution (SR) problem, where the resolution of the coarse image is enhanced to generate a high-resolution image with rich details. For example, STFDCNN [37] employs a five-layer super-resolution CNN to model the nonlinear relationship between MODIS and Landsat images, combining high-pass modulation to fully exploit information from the reference image. Furthermore, some STF networks align with reference-based SR tasks. Notable examples include GAN-STFM [22], MCBAM-GAN [38], STF-Trans [28], and CTSTFM [29], which directly input fine images from the reference time and coarse-resolution images from the target time. These approaches achieve high-accuracy spatiotemporal fusion while reducing dependence on the number of input images. However, in highly heterogeneous regions, particularly when significant land cover changes occur between prediction and reference dates, models face the dual challenge of capturing temporal variations and preserving spatial structures. Some models address this challenge through parallel processing; for instance, DL-SDFM [39] employs dual CNN architectures to independently predict temporal changes and spatial information before fusing them using weighting functions. MTDL-STF [40] utilizes separate SR and STF networks to predict normalized difference vegetation index (NDVI) values along spatial and temporal dimensions, respectively. However, such decoupled approaches often overlook the intricate interdependencies between temporal and spatial scales, resulting in inaccurate mapping of temporal changes across different scales. Furthermore, significant land cover changes imply that the spectral information from the coarse image at the prediction date may be the only reliable source for predicting the spectra of new land cover types. However, most existing methods fail to fully utilize the prior spectral information provided by coarse images, leading to spectral distortion and insufficient consistency in the fusion results. Based on the above research, a Bidirectional Cross Spatiotemporal Fusion Network with Spectral Restoration (BCSR-STF) is proposed. This framework achieves bidirectional fusion for temporal change modeling (time direction) and detail enhancement modeling (scale direction) through a Progressive Spatiotemporal Feature Fusion and Restoration (PSTFR) module. The PSTFR module incorporates a multi-scale optimization design with local and global attention. At each level, it enables bidirectional spatiotemporal feature interaction across short-range and long-range distances, and it adaptively optimizes the fused features using spectral prior knowledge. The contributions of this work are summarized as follows:  
1. An end-to-end BCSR-STF model is proposed. The PSTFR module within BCSR-STF employs multi-scale iterative optimization, enabling effective exchange between high-level and low-level information. The design enhances the model's capability to address variations in object scale within input images, thereby improving the accuracy of spatiotemporal fusion. 2. A Bidirectional Cross Fusion (BCF) module is designed to leverage the advantages and mitigate the limitations of temporal and scale directions. This module simultaneously considers temporal variations and scale differences, utilizing short-range and long-range attention mechanisms based on the Vision Transformer to enhance interactions between temporal and spatial information, thereby improving fusion accuracy. 3. The Global Spectral Restoration and Feature Enhancement (GSRFE) module is introduced to restore and enhance spectral information often overlooked in coarse images. By incorporating Adaptive Instance Normalization (AdaIN) and spatial attention mechanisms, GSRFE adaptively adjusts spectral distributions and enhances the quality of spatiotemporal fusion through feature enhancement. The remainder of this work is structured as follows: Section 2 provides a detailed explanation of the framework and the specifics of the proposed network.