Discrimination between sea ice and clouds over the Chukchi Sea via China’s ultraviolet–visible-infrared observations

• Sea ice and different types of clouds exhibit significant UV reflection characteristics in spaceborne images. • A methodological framework using UV–visible-infrared bands to identify sea ice and classify cloud types is proposed. • Misclassification between sea ice and clouds using the BT differences is refined by the UV-based approach. The distribution of Arctic sea ice is an important direct indicator of climate change, and spaceborne optical remote sensing represents one primary technique for sea ice monitoring due to its high spatiotemporal resolution and wide swath coverage. However, this process is often impeded by heavy cloud cover, which shares similar visual and spectral features with sea ice. To address these limitations, this study proposes a novel methodological framework for discriminating between sea ice and different cloud types (cirrus and cumulus) via the ultraviolet–visible-infrared observations from China’s Haiyang-1C/D (HY-1C/D) satellites, and the ultraviolet (UV) data from the onboard Ultraviolet Imager (UVI) are used to study sea ice and clouds over the Chukchi Sea for the first time. The spectral properties are characterized by the top-of-atmosphere (TOA) reflectance ( ρ TOA ) in both UV and visible and near-infrared (VNIR) wavelengths. This indicates that the 355 nm UV band has the optimal sensitivity to the presence of sea ice and clouds, with cirrus clouds composed of high-altitude ice crystals exhibiting extremely high UV reflectivity. A hybrid threshold is subsequently determined to separate sea ice and cloud pixels. In comparison to the MODIS MOD29 sea ice product, which masks cloud pixels with brightness temperature (BT) differences, this algorithm can effectively reduce the misclassification resulting from surface temperature inversions in polar regions. The ice/cloud identification results have been further applied to sea ice concentration (SIC) estimation, and extensive trials of this UV-based ice/cloud detection approach in the Arctic Passages demonstrates its potential applicability.