Cloud cover (CC) significantly impacts the effectiveness of optical remote sensing in Earth observation, particularly in tropical areas. However, pixel-level analyses of CC dynamics and their underlying mechanisms remain inadequate. Here, we examine pixel-based CC patterns in Mainland Southeast Asia (MSEA) based on 281,124 Sentinel-2 cloud scenes during 2017–2023. By integrating correlation analysis, Geographically Weighted Regression (GWR), and Geographical Detector (GD) methods, we then investigated the relationships between CC (246 billion pixels in total) and 20 environmental factors, including vegetation conditions (e.g. NDVI), underlying surfaces (e.g. land cover), and meteorological parameters (e.g. wind). Main conclusions include: (1) The average CC probability of Sentinel-2 pixels across MSEA reaches 0.54 (±0.19), exhibiting a decreasing gradient from northeast to southwest and from coastal zones to inland areas. (2) Probability of CC shows significant national and temporal variations across MSEA, with the lowest values observed in Myanmar of 0.48 (±0.08) and in February of 0.31 (±0.22). (3) Elevation and coastal proximity are the primary drivers of CC, outweighing the influence of land cover. We identified three spatial paradigms of CC variation: ocean-driven (coastal areas), topography-anchored (mountainous areas), and energy-convection (inland areas). Our findings provide practical guidance for optimizing satellite data acquisition and improving remote sensing applications in cloudy and rainy regions.
Cheng et al. (2026) studied this question.