CLOUD-RESILIENT OPTICAL REMOTE SENSING TECHNIQUES: A REVIEW OF GAP-FILLING AND TEMPORAL FUSION METHODS

Optical remote sensing has been a cornerstone in monitoring Earth's surface processes due to its ability to capture multispectral information with high spatial resolution. However, persistent cloud cover remains a critical limitation, particularly in tropical, monsoonal, and mountainous regions, leading to data gaps, reduced temporal consistency, and compromised analysis quality. This review presents a comprehensive synthesis of cloud-resilient techniques developed to mitigate the impact of cloud contamination in optical satellite imagery. The study classifies and evaluates a range of gap-filling and temporal fusion methods, including spatial and temporal interpolation, multi-date compositing, data fusion frameworks, and emerging deep learning approaches. Particular emphasis is given to models such as STARFM, ESTARFM, and spatiotemporal CNN-LSTM hybrids that enable integration of high-resolution and highfrequency datasets. The role of auxiliary datasets, such as synthetic aperture radar (SAR), historical vegetation indices, and meteorological parameters, is also discussed in enhancing cloud resilience. Additionally, the review explores operational tools and platforms including Google Earth Engine, Fmask, and machine learning libraries that have advanced the automation of these processes. While progress has been significant, challenges persist in terms of model generalization, validation under extreme cloud conditions, and balancing spatial-temporal trade-offs. The paper concludes by identifying key research gaps and proposing future directions for the development of robust, scalable, and accessible cloud-resilient remote sensing frameworks. These advancements are crucial for ensuring continuous environmental monitoring and informed decision-making, particularly in regions where cloud-free observations remain sparse.