high spatial

visible infrared

sun glint

cloud removal

cloud detection

Remote Sensing of Environment

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.

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