A Multi-Stage Framework for Cloud-Free NDVI Generation in Agricultural Monitoring Using Sentinel-2 Imagery

Cloud cover in optical satellite imagery is a major impediment to continuous agricultural monitoring, often disrupting the derivation of Normalized Difference Vegetation Index (NDVI) time series.To address this challenge, this study introduces a multi-stage framework to generate gap-free (cloud-free) NDVI images for agricultural monitoring using Sentinel-2 imagery.The proposed framework comprises three sequential modules: (1) Constrained Cloud-Maximum NDVI Composite (CC-MNC), which produces an initial NDVI composite with minimal cloud contamination; (2) local median-based spatial interpolation, which fills residual small-area gaps by replacing missing pixels with the median NDVI of nearby clear-sky pixels within a local neighborhood; and (3) temporal gap-filling using Gaussian Process Regression (GPR), which estimates NDVI values for larger missing regions or periods affected by persistent cloud cover.The framework was evaluated on five agricultural reference tiles in South Korea during a cloud-prone summer season.The initial Sentinel-2 images had an average cloud cover of 21.37%.After applying CC-MNC, the average cloud coverage in the NDVI composites was reduced to 0.93%.Subsequent spatial gap-filling further decreased the remaining missing data to 0.47%.Finally, the temporal GPR module eliminated all remaining gaps, achieving a fully cloud-free NDVI product (0% cloud cover) with a 100% gap recovery rate.The average processing time to generate each composite was under 10 minutes, demonstrating the method's efficiency for near-real-time applications.These results indicate that the proposed multi-stage approach can effectively provide complete and reliable NDVI maps under persistent cloud conditions.The method is readily applicable to future agricultural satellite missions, such as Compact Advanced Satellite 500-4 (CAS500-4), to enhance operational crop monitoring during cloudy seasons.