Estimating ultraviolet reflectance from visible bands in ocean colour remote sensing

Huizeng Liu, Xianqiang He, Qingquan Li, Susanne Kratzer, Junjie Wang, Tiezhu Shi, Zhongwen Hu, Chao Yang, Shuibo Hu, Qiming ZHOU, Guofeng Wu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In recent years, ultraviolet (UV) bands have received increasing attention from the ocean colour remote sensing community, as they may contribute to improving atmospheric correction and inherent optical properties (IOPs) retrieval. However, most ocean colour satellite sensors do not have UV bands, and the accurate retrieval of UV remote sensing reflectance (Rrs) from UV satellite data is still a challenge. In order to address this problem, this study proposes a hybrid approach for estimating UV Rrs from the visible bands. The approach was implemented with two popular ocean colour satellite sensors, i.e. GCOM-C SGLI and Sentinel-3 OLCI. In situ Rrs collected globally and simulated Rrs spectra were used to develop UV Rrs retrieval models, and UV Rrs values at 360, 380 and 400 nm were estimated from visible Rrs spectra. The performances of the established models were evaluated using in situ Rrs and satellite data, and applied to a semi-analytical algorithm for IOPs retrieval. The results showed that: (i) UV Rrs retrieval models had low uncertainties with mean absolute percentage differences (MAPD) less than 5%; (ii) the model assessment with in situ Rrs showed high accuracy (r = 0.92–1.00 and MAPD = 1.11%–10.95%) in both clear open ocean and optically complex waters; (iii) the model assessment with satellite data indicated that model-estimated UV Rrs were more consistent with in situ values than satellite-derived UV Rrs; and (iv) model-estimated UV Rrs may improve the decomposition accuracy of absorption coefficients in semi-analytical IOPs algorithm. Thus, the proposed method has great potentials for reconstructing UV Rrs data and improving IOPs retrieval for historical satellite sensors, and might also be useful for UV-based atmospheric correction algorithms.

Original languageEnglish
Article number112404
JournalRemote Sensing of Environment
Volume258
DOIs
Publication statusPublished - 1 Jun 2021

Scopus Subject Areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences

User-Defined Keywords

  • Colour index
  • Inherent optical properties
  • Machine learning
  • Ocean colour remote sensing
  • Remote sensing reflectance
  • Ultraviolet

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