Cloud Masking
Cloud masking is the process of identifying and flagging cloud-contaminated pixels in satellite imagery so they can be excluded from analysis. Accurate cloud masks are essential for reliable land surface monitoring, vegetation indices, and change detection.
Cloud masking is a critical preprocessing step in optical remote sensingRemote SensingRemote sensing is the science of collecting data about Earth's surface without direct physical contact, primarily usi... that detects and labels pixels affected by clouds, cloud shadows, and haze, allowing downstream analyses to exclude these contaminated observations. Because optical sensors cannot see through clouds, unmasked cloud pixels introduce errors in vegetation indices, land cover classificationLand Cover ClassificationLand cover classification is the process of categorizing Earth's surface into distinct classes such as forest, cropla..., surface temperature retrieval, and time series analysisTime Series AnalysisTime series analysis in remote sensing involves analyzing sequences of satellite images acquired over time to detect .... Detection methodsTraditional cloud masking algorithms like Fmask and the Sentinel-2 Scene Classification Layer (SCL) use rule-based tests on spectral bands, thermal data, and geometric relationships to classify each pixel as clear, cloud, cloud shadow, snow/ice, or water. Machine learning approaches using random forests and deep learning models are increasingly used for improved accuracy, especially in challenging conditions like thin cirrus clouds, bright surfaces, and cloud edges. Impact on analysisPersistent cloud cover in tropical regions can severely limit the availability of usable optical imagery. Temporal compositing techniques select the best cloud-free pixel from multiple acquisitions over a time window, while SARSARSynthetic Aperture Radar (SAR) is an active remote sensing technology that uses microwave radar pulses to create high... data provides an all-weather alternative when optical imagery is unavailable. Cloud masking is a deceptively challenging problem that directly impacts the quality of all optical remote sensing analyses. Advances in machine learning and multi-sensor approaches continue to improve cloud detection accuracy, expanding the usable fraction of satellite imagerySatellite ImagerySatellite imagery consists of photographs and data captured by Earth observation satellites orbiting the planet. Thes... archives.
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