## 5 Conclusions  
This work proposes BCSR-STF, a model designed to generate time series remote sensing data via spatiotemporal fusion. BCSR-STF integrates a multi-layer PSTFR structure, enabling iterative optimization of spatiotemporal features across multiple scales and enhancing adaptability to variations in resolution and temporal differences. The core components of PSTFR are the Bidirectional Cross Fusion (BCF) and Global Spectral Restoration and Feature Enhancement (GSRFE) modules. BCF significantly improves the fusion of spatiotemporal features by simultaneously processing information along both temporal and scale dimensions. GSRFE effectively corrects sensor errors and spectral inconsistencies through adaptive spectral adjustment and feature enhancement. The effectiveness of each module is validated through a series of ablation experiments. Comparative studies demonstrate the superior performance of BCSR-STF. However, the model demands substantial computational resources, potentially limiting its applicability in resource-constrained environments. Future work will focus on reducing computational complexity and resource consumption through algorithmic optimizations, such as model pruning and quantization, as well as the design of lightweight network architectures. Conceptualization, D.Z. and K.W. ; methodology, D.Z. ; Writing--original draft, D.Z. ; writing--review and editing, K.W. and G.X. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China, grant number U21A2013; the Open Fund of Key Laboratory of Space Ocean Remote Sensing and Application, MNR, grant number 202401001; the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan), grant number 2642022009; the Open Fund of Wenzhou Future City Research Institute, grant number WL2023007; the Zhejiang Provincial Philosophy and Social \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **MODEL** & **Param. (M)** & **FLOPs** \\ \hline EDCSTFN & & 0.28 & 1.86 \(\times\) 10\({}^{10}\) \\ GAN-STFM & Generator & 0.58 & 3.78 \(\times\) 10\({}^{10}\) \\  & Discriminator & 3.67 & 1.03 \(\times\) 10\({}^{7}\) \\ MLFF-GAN & Generator & 5.93 & 1.36 \(\times\) 10\({}^{10}\) \\  & Discriminator & 2.78 & 3.77 \(\times\) 10\({}^{9}\) \\ STF-Trans & & 23.34 & 1.74 \(\times\) 10\({}^{11}\) \\ CTSTFM & & 6.30 & 3.8 \(\times\) 10\({}^{11}\) \\ BCSR-STF & & 34.80 & 2.71 \(\times\) 10\({}^{10}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model efficiency analysis of six deep learning-based spatiotemporal fusion models. Sciences Planning Project grant number 25 NDJC09 GYBM; the Major Science and Technology Research Projects in Wenzhou City in 2023, grant number ZZN2023005; the Global Change and Air-Sea Interaction II, grant number GASI-01-DLYG-WIND0; the Foundation of State Key Laboratory of Public Big Data, grant number PBD2023-28; the Open Fund of Key Laboratory of Regional Development and Environmental Response, grant number 2023(A)003; and the Open Fund of State Key Laboratory of Remote Sensing Science, grant number OFSLRSS202312. Not applicable. The data presented in this study were derived from the following resources available in the public domain: CIA, [[https://data.csiro.au/collections/collection/Clcsiro](https://data.csiro.au/collections/collection/Clcsiro)]([https://data.csiro.au/collections/collection/Clcsiro](https://data.csiro.au/collections/collection/Clcsiro)): 5846v3, accessed on 1 May 2025; LGC, [[https://data.csiro.au/collections/collection/Clcsiro:5847v003](https://data.csiro.au/collections/collection/Clcsiro:5847v003)]([https://data.csiro.au/collections/collection/Clcsiro:5847v003](https://data.csiro.au/collections/collection/Clcsiro:5847v003)), accessed on 1 May 2025; Wuhan, [[https://github.com/likinghua5540/Wuhan-dataset](https://github.com/likinghua5540/Wuhan-dataset)]([https://github.com/likinghua5540/Wuhan-dataset](https://github.com/likinghua5540/Wuhan-dataset)), accessed on 1 May 2025.