BROWSE

Related Researcher

Author's Photo

Cho, Kyung Hwa
Water-Environmental Informatics Lab (WEIL)
Research Interests
  • Water Quality Monitoring and Modeling, Water Treatment Process Modeling

ITEM VIEW & DOWNLOAD

Application of airborne hyperspectral imagery to retrieve spatiotemporal CDOM distribution using machine learning in a reservoir

DC Field Value Language
dc.contributor.author Kim, Jinuk ko
dc.contributor.author Jang, Wonjin ko
dc.contributor.author Kim, Jin Hwi ko
dc.contributor.author Lee, Jiwan ko
dc.contributor.author Cho, Kyung Hwa ko
dc.contributor.author Lee, Yong-Gu ko
dc.contributor.author Chon, Kangmin ko
dc.contributor.author Park, Sanghyun ko
dc.contributor.author Pyo, JongCheol ko
dc.contributor.author Park, Yongeun ko
dc.contributor.author Kim, Seongjoon ko
dc.date.available 2022-11-23T09:54:26Z -
dc.date.created 2022-11-16 ko
dc.date.issued 2022-11 ko
dc.identifier.citation INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, v.114, pp.103053 ko
dc.identifier.issn 1569-8432 ko
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/60037 -
dc.description.abstract Colored dissolved organic matter (CDOM) in inland waters is used as a proxy to estimate dissolved organic carbon (DOC) and may be a key indicator of water quality and nutrient enrichment. CDOM is optically active fraction of DOC so that remote sensing techniques can remotely monitor CDOM with wide spatial coverage. However, to effectively retrieve CDOM using optical algorithms, it may be critical to select the absorption co-efficient at an appropriate wavelength as an output variable and to optimize input reflectance wavelengths. In this study, we constructed a CDOM retrieval model using airborne hyperspectral reflectance data and a machine learning model such as random forest. We evaluated the best combination of input wavelength bands and the CDOM absorption coefficient at various wavelengths. Seven sampling events for airborne hyperspectral imagery and CDOM absorption coefficient data from 350 nm to 440 nm over two years (2016-2017) were used, and the collected data helped train and validate the random forest model in a freshwater reservoir. An absorption co-efficient of 355 nm was selected to best represent the CDOM concentration. The random forest exhibited the best performance for CDOM estimation with an R2 of 0.85, Nash-Sutcliffe efficiency of 0.77, and percent bias of 3.88, by using a combination of three reflectance bands: 475, 497, and 660 nm. The results show that our model can be utilized to construct a CDOM retrieving algorithm and evaluate its spatiotemporal variation across a reservoir. ko
dc.language 영어 ko
dc.publisher ELSEVIER ko
dc.title Application of airborne hyperspectral imagery to retrieve spatiotemporal CDOM distribution using machine learning in a reservoir ko
dc.type ARTICLE ko
dc.identifier.scopusid 2-s2.0-85139853180 ko
dc.identifier.wosid 000876451100001 ko
dc.type.rims ART ko
dc.identifier.doi 10.1016/j.jag.2022.103053 ko
Appears in Collections:
UEE_Journal Papers

find_unist can give you direct access to the published full text of this article. (UNISTARs only)

Show simple item record

qrcode

  • mendeley

    citeulike

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

MENU