A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENµS, LANDSAT and SENTINEL-2 images

By O. Hagolle, M. Huc, V. David, Gérard Dedieu. 2010

438 citations recorded by the source corpus.

Subfields: Ecology, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/f290239242d976254d5bb8b874e5e0582b21bbbe

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In Ecology

In Global and Planetary Change

Citations in the corpus

Estimating smallholder crops production at village level from Sentinel-2 time series in Mali's cotton belt (2018, methodology): “The MACCS algorithm uses the multi-temporal dimension of the Sentinel-2 time series to detect clouds and shadows, correct atmospheric perturbations and retrieve aerosol optical thickness (Hagolle et al., 2015,2010).”
LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands (2011, methodology): “The multi-temporal cloud detection (MTCD) to be applied for VENμS images (which might be applied for Sentinel-2) method has a low amount of false detections and is tuned to identify even thin clouds (Hagolle et al., 2010).”
Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications (2017, methodology): “…single index (Bolton et al., 2015; Hayes and Sader, 2001; Jin and Sader, 2005; Linke et al., 2009; Marzen et al., 2011; Parker et al., 2015), while others used multiple indices to detect change (Fraser et al., 2009; Hagolle et al., 2010; Neigh et al., 2014; Vorovencii, 2014; Zanotta et al., 2015).”
Retrieval of the canopy chlorophyll content from Sentinel-2 spectral bands to estimate nitrogen uptake in intensive winter wheat cropping systems (2018, methodology): “…reflectance time series for the 2016 and 2017 winter wheat growing seasons were processed using the MACCS (Multi-sensor Atmospheric Correction and Cloud Screening) algorithm implemented in the Sentinel-2 for Agriculture system (Hagolle et al., 2010; Hagolle et al., 2015a; Hagolle et al., 2015b).”
Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas (2017, background): “The Theia website provides S2A images that are corrected for atmospheric effects and ortho-rectified [42,43].”
Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change (2014, methodology): “Most multitemporal algorithms assume that there is no snow in the image and the pixels that are brighter than the reference values are only due to clouds (Goodwin et al., 2013; Hagolle et al., 2010; Jin et al., 2013; Wang et al., 1999).”
Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas (2016, methodology): “Missing information, mainly due to the cloud coverage and the acquisition system, is detected by using Hagolle et al. (2010, 2015) method.”