AI 中文总结
针对遥感中NDVI时间序列重建的成对数据获取难题，提出GloSSR框架，结合双向Transformer与ConvLSTM等技术，在MODIS数据上实现了更优的重建效果，可用于大规模环境监测。
AI 中文摘要
在遥感领域，准确高效地重建受云污染和噪声损坏的归一化植被指数（NDVI）时间序列仍是一项挑战。深度学习为建模复杂的时空依赖关系提供了有前景的解决方案，但其应用常受限于难以获取相同时空位置下成对的晴空与退化NDVI数据。为解决该问题，我们提出GloSSR，这是一个用于NDVI重建的全球尺度自监督时空框架。该框架通过用真实云污染模式人为损坏相对干净的NDVI观测值来构建监督信号，生成与现实退化高度相似的自监督训练对；还引入端到端时空学习网络，通过结合ConvLSTM架构的双向Transformer，联合捕捉长程时间依赖与短期时空关联；融入基于时间-通道注意力的重建模块以增强有效特征，同时设计时空先验约束，在优化过程中保留精细结构与长期物候趋势。对MODIS NDVI数据的大量评估表明，该框架在人工和现实场景中均有效：在人工退化像素重建实验中，GloSSR始终优于对比方法；基于真实观测的时间序列分析进一步显示，该框架可准确表征植被动态并捕捉关键物候状态；长期植被趋势分析及向AVHRR数据的迁移性分析验证了其可扩展性，体现了其在大规模环境监测中的广泛适用性。
英文摘要
Accurate and efficient reconstruction of cloud-contaminated and noise-corrupted NDVI time series remains a challenge in remote sensing. Deep learning provides a promising solution for modeling complex spatiotemporal dependencies; however, its application is often limited by the difficulty of obtaining paired clear-sky and degraded NDVI data for identical spatiotemporal locations. To address this issue, we propose GloSSR, a Global-scale Self-supervised Spatiotemporal framework for NDVI Reconstruction. The framework constructs supervisory signals by artificially degrading relatively clean NDVI observations with realistic cloud contamination patterns, producing self-supervised training pairs that closely mimic real-world degradation. It further introduces an end-to-end spatiotemporal learning network that jointly captures long-range temporal dependencies and short-term spatiotemporal correlation through a bidirectional Transformer with a ConvLSTM architecture. A temporal-channel attention-based reconstruction module is incorporated to enhance informative features, while a spatiotemporal prior constraint is designed to preserve both fine-scale structures and long-term phenological trends during optimization. Extensive evaluations on MODIS NDVI data demonstrate the effectiveness of the proposed framework across both artificial and real-world scenarios. In artificial degraded-pixel reconstruction experiments, GloSSR consistently outperforms the comparison methods. Time-series analyses based on real observations further demonstrate that the proposed framework can accurately characterize vegetation dynamics and capture the key phenological states. Long-term vegetation trend analysis and the transferability analysis to AVHRR data validate the scalability of the framework and illustrate its broad applicability for large-scale environmental monitoring.
