Affiliations 

  • 1 College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
  • 2 College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China. Electronic address: zhangfei3s@zjnu.edu.cn
  • 3 Department of Earth Sciences, The University of Memphis, Memphis, TN 38152, USA
  • 4 GeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, 11800, USM, Penang, Malaysia
  • 5 Xinjiang Institute of Technology, Aksu 843000, China
Mar Pollut Bull, 2023 Nov;196:115653.
PMID: 37879130 DOI: 10.1016/j.marpolbul.2023.115653

Abstract

Chromophoric dissolved organic matter (CDOM) occupies a critical part in biogeochemistry and energy flux of aquatic ecosystems. CDOM research spans in many fields, including chemistry, marine environment, biomass cycling, physics, hydrology, and climate change. In recent years, a series of remarkable research milestone have been achieved. On the basis of reviewing the research process of CDOM, combined with a bibliometric analysis, this study aims to provide a comprehensive review of the development and applications of remote sensing in monitoring CDOM from 2003 to 2022. The findings show that remote sensing data plays an important role in CDOM research as proven with the increasing number of publications since 2003, particularly in China and the United States. Primary research areas have gradually changed from studying absorption and fluorescence properties to optimization of remote sensing inversion models in recent years. Since the composition of oceanic and freshwater bodies differs significantly, it is important to choose the appropriate inversion method for different types of water body. At present, the monitoring of CDOM mainly relies on a single sensor, but the fusion of images from different sensors can be considered a major research direction due to the complex characteristics of CDOM. Therefore, in the future, the characteristics of CDOM will be studied in depth inn combination with multi-source data and other application models, where inversion algorithms will be optimized, inversion algorithms with low dependence on measured data will be developed, and a transportable inversion model will be built to break the regional limitations of the model and to promote the development of CDOM research in a deeper and more comprehensive direction.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.