KappaMask: AI-Based Cloudmask Processor for Sentinel-2

Abstract

The Copernicus Sentinel-2 mission operated by the European Space Agency (ESA) provides comprehensive and continuous multi-spectral observations of all the Earth’s land surface since mid-2015. Clouds and cloud shadows significantly decrease the usability of optical satellite data, especially in agricultural applications; therefore, an accurate and reliable cloud mask is mandatory for effective EO optical data exploitation. During the last few years, image segmentation techniques have developed rapidly with the exploitation of neural network capabilities. With this perspective, the KappaMask processor using U-Net architecture was developed with the ability to generate a classification mask over northern latitudes into the following classes: clear, cloud shadow, semi-transparent cloud (thin clouds), cloud and invalid. For training, a Sentinel-2 dataset covering the Northern European terrestrial area was labelled. KappaMask provides a 10 m classification mask for Sentinel-2 Level-2A (L2A) and Level-1C (L1C) products. The total dice coefficient on the test dataset, which was not seen by the model at any stage, was 80% for KappaMask L2A and 76% for KappaMask L1C for clear, cloud shadow, semi-transparent and cloud classes. A comparison with rule-based cloud mask methods was then performed on the same test dataset, where Sen2Cor reached 59% dice coefficient for clear, cloud shadow, semi-transparent and cloud classes, Fmask reached 61% for clear, cloud shadow and cloud classes and Maja reached 51% for clear and cloud classes. The closest machine learning open-source cloud classification mask, S2cloudless, had a 63% dice coefficient providing only cloud and clear classes, while KappaMask L2A, with a more complex classification schema, outperformed S2cloudless by 17%.

Forecasting localized weather impacts on vegetation as seen from space with meteo-guided video prediction

“…1 compares precision, recall and F1 scores of detecting faulty pixels. Our approach outperforms Sen2Cor [30] (used in EarthNet2021), FMask [40] and KappaMask [11] baselines by a large margin. If using the Sentinel 2 SCL band in addition, to allow for snow masking, precision drops, but recall increases: i.e.…”

“…It requires L1C top-of-atmosphere reflectance from all bands to be produced (EarthNet2021 only containes L2A bottom-ofatmosphere reflectance from four bands). KappaMask [11] is a cloud mask based on deep learning, in table 1 we reported scores from the L2A version, which uses all 13 L2A bands as input.…”

A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research

“…All images were centered at the DE-Hai Eddy Covariance (EC) Tower 176 from the Integrated Carbon Observation System (ICOS) and stored as a ESMC in zarr format, where L is the set of dimensions of , and L = {time, band, x, y}. The ESMC was preprocessed as follows: (1) the cube was cropped to a spatial extent of a 2.56 km radius around the EC tower, (2) the surface reflectance was adjusted to nadir BRDF reflectance using the c -factor approach 177 – 179 , (3) all bands were resampled to 10 m using the nearest neighbour algorithm, (4) clouds and cloud shadows were masked using the KappaMask L2A model 180 , and (5) snow was masked using the Fmask algorithm 181 , 182 . The temporal resolution of the dataset is 1-day and covers the period 2016–2021.…”

CloudSEN12, a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2

“…• CD-FCNN: U-Net with two different SEN2 band combinations: 23 RGBI (B2, B3, B4, and B8) and RGBISWIR (B2, B3, B4, B8, B11, and B12) trained on the Landsat Biome-8 dataset (transfer learning 52,53 from Landsat 8 to Sentinel-2). • KappaMask: U-Net with two distinct settings: 54 all Sentinel-2 L1C bands and all Sentinel-2 L2A bands except the Red Edge 3 band. It was trained using both Sentinel-2 KappaZeta Cloud and Cloud Shadow Masks and the Sentinel-2 Cloud Mask Catalogue (see Table 1).…”