Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4–8 and Sentinel-2 imagery

• Published in: Remote sensing of environment Vol. 231; p. 111205
• Main Authors: Qiu, Shi, Zhu, Zhe, He, Binbin
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 15.09.2019; Elsevier BV
• Subjects: Algorithms; automation; Auxiliary data; Cirrus clouds; Cloud; Cloud shadow; Clouds; data collection; Digital Elevation Models; Genetic transformation; Haze; ice; Image detection; Landsat; Landsat satellites; Morphology; Mountain snow; Mountains; New cloud probability; Polar environments; Polar Regions; probability; Remote sensing; Satellite imagery; Sentinel-2; Shadows; Snow; Spectral-contextual; Surface water; temperature; Polar Regions; Spectral-contextual; Cloud shadow; Auxiliary data; Cloud; Landsat; Sentinel-2; New cloud probability
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2019.05.024

We developed the Function of mask (Fmask) 4.0 algorithm for automated cloud and cloud shadow detection in Landsats 4–8 and Sentinel-2 images. Three major innovative improvements were made as follows: (1) integration of auxiliary data, where Global Surface Water Occurrence (GSWO) data was used to improve the separation of land and water, and a global Digital Elevation Model (DEM) was used to normalize thermal and cirrus bands; (2) development of new cloud probabilities, in which a Haze Optimized Transformation (HOT)-based cloud probability was designed to replace temperature probability for Sentinel-2 images, and cloud probabilities were combined and re-calibrated for different sensors against a global reference dataset; and (3) utilization of spectral-contextual features, where a Spectral-Contextual Snow Index (SCSI) was created for better distinguishing snow/ice from clouds in polar regions, and a morphology-based approach was applied to reduce the commission error in bright land surfaces (e.g., urban/built-up and mountain snow/ice). The Fmask 4.0 algorithm showed higher overall accuracies for Landsats 4–8 imagery than the 3.3 version (Zhu et al., 2015) (92.40% versus 90.73% for Landsats 4–7 and 94.59% versus 93.30% for Landsat 8), and much higher overall accuracies for Sentinel-2 imagery than the 2.5.5 version of the Sen2Cor algorithm (Müller-Wilm et al., 2018) (94.30% versus 87.10%). •Fmask 4.0 cloud and cloud shadow detection in Landsats 4–8 and Sentinel-2•Global water map is integrated to better separate land and water.•DEM is used as auxiliary data to normalize thermal and cirrus bands.•New cloud probabilities are designed for improving cloud detection.•Spectral-contextual features are combined to reduce false positive errors.