Sunglint reflection facilitates performance of spaceborne UV sensor in oil spill detection

Abstract

Ultraviolet Imager (UVI) onboard Haiyang-1C/D (HY-1C/D) satellites has been providing ultraviolet (UV) data to detect marine oil spills since 2018. Although the scale effect of UV remote sensing has been preliminarily interpreted, the application characteristics of spaceborne UV sensors with medium spatial resolution in oil spill detection deserve further investigation, especially the role of sunglint in the process of detection. In this study, the performance of the UVI is thoroughly assessed by the following aspects: image features of oils under sunglint, sunglint requirement for spaceborne UV detection of oils, and the stability of the UVI signal. The results indicate that in UVI images, it is sunglint reflection that determines the image features of spilled oils, and the appearance of sunglint can strengthen the contrast between oils and seawater. Besides, the required sunglint strength in spaceborne UV detection has been deduced to be 10−3 - 10−4 sr-1, which is higher than that in the VNIR wavelengths. Moreover, uncertainties in the UVI signal can meet the demand to discriminate between oils and seawater. The above results can confirm the capability of the UVI and the critical role of sunglint in spaceborne UV detection of marine oil spills, and provide new reference for spaceborne UV remote sensing.

Hyperspectral Marine Oil Spill Monitoring Using a Dual-Branch Spatial–Spectral Fusion Model

“…Remote sensing, with its advantages of multi-source data, rapid data acquisition, and wide coverage, has become indispensable in emergency response and damage assessment related to marine oil spills [15][16][17][18][19][20][21][22][23]. In recent years, optical remote sensing techniques have been widely applied for monitoring oil spills on the sea surface [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38]. Common optical remote sensing techniques include multispectral, hyperspectral, thermal infrared, ultraviolet, polarimetric, and LiDAR remote sensing.…”