Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2

作者：Sergii Skakun, Jan Wevers, Carsten Brockmann, Georgia Doxani, Matej Aleksandrov, Matej Batič, David Frantz, Ferran Gascon, Luis Gómez‐Chova, Olivier Hagolle, Dan López-Puigdollers, Jérôme Louis, Matic Lubej, Gonzalo Mateo‐García, Julien Osman, Devis Peressutti, Bringfried Pflug, Jernej Puc, Rudolf Richter, Jean‐Claude Roger, Pat Scaramuzza, Éric Vermote, Nejc Vesel, A. Zupanc, Lojze Žust · 发表于：Remote Sensing of Environment · 年份：2022 · DOI：10.1016/j.rse.2022.112990 · 被引用次数：289 · 研究领域：Remote Sensing in Agriculture、Atmospheric aerosols and clouds、Atmospheric and Environmental Gas Dynamics

Cloud cover is a major limiting factor in exploiting time-series data acquired by optical spaceborne remote sensing sensors. Multiple methods have been developed to address the problem of cloud detection in satellite imagery and a number of cloud masking algorithms have been developed for optical sensors but very few studies have carried out quantitative intercomparison of state-of-the-art methods in this domain. This paper summarizes results of the first Cloud Masking Intercomparison eXercise (CMIX) conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). CEOS is the forum for space agency coordination and cooperation on Earth observations, with activities organized under working groups. CMIX, as one such activity, is an international collaborative effort aimed at intercomparing cloud detection algorithms for moderate-spatial resolution (10–30 m) spaceborne optical sensors. The focus of CMIX is on open and free imagery acquired by the Landsat 8 (NASA/USGS) and Sentinel-2 (ESA) missions. Ten algorithms developed by nine teams from fourteen different organizations representing universities, research centers and industry, as well as space agencies (CNES, ESA, DLR, and NASA), are evaluated within the CMIX. Those algorithms vary in their approach and concepts utilized which were based on various spectral properties, spatial and temporal features, as well as machine learning methods. Algorithm outputs are evaluated agains...