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Im, Jungho
Intelligent Remote sensing and geospatial Information Science Lab.
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dc.citation.endPage 8626 -
dc.citation.startPage 8614 -
dc.citation.title IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING -
dc.citation.volume 14 -
dc.contributor.author Park, Haemi -
dc.contributor.author Lee, Junghee -
dc.contributor.author Yoo, Cheolhee -
dc.contributor.author Sim, Seongmun -
dc.contributor.author Im, Jungho -
dc.date.accessioned 2023-12-21T15:20:39Z -
dc.date.available 2023-12-21T15:20:39Z -
dc.date.created 2021-09-27 -
dc.date.issued 2021-08 -
dc.description.abstract Near-surface relative humidity (RHns) is an essential meteorological parameter for water, carbon, and climate studies. However, spatially continuous RHns estimation is difficult due to the spatial discontinuity of in situ observations and the cloud contamination of satellite-based data. This article proposed machine learning-based models to estimate spatially continuous daily RHns at 1 km resolution over Japan and South Korea under all sky conditions and examined the spatiotemporal patterns of RHns. All sky estimation of RHns using machine learning has been rarely conducted, and it can be an alternative to the currently available RHns data mostly from numerical models, which have relatively low spatial resolution. We combined two schemes for clear sky conditions (scheme A, which uses satellite and reanalysis data) and cloudy sky conditions (scheme B, which uses reanalysis data solely). The relatively small numbers of data in extremely low and high RHns conditions (i.e., <30% or >70%, respectively) were augmented by applying an oversampling method to avoid biased training. The machine learning models based on random forest (RF) and XGBoost were trained and validated using 94 in situ observation sites from meteorological administrations of both countries from 2012 to 2017. The results showed that XGBoost produced slightly better performance than RF, and the spatially continuous RHns model combined based on XGBoost yielded the coefficient of determination of 0.72 and a root-mean-square error of 10.61%. Spatiotemporal patterns of the estimated RHns agreed with in situ observations, reflecting the effect of topography on RHns. We expect that the proposed RHns model could be used in various environmental studies that require RHns under all sky conditions as input data. -
dc.identifier.bibliographicCitation IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, v.14, pp.8614 - 8626 -
dc.identifier.doi 10.1109/JSTARS.2021.3103754 -
dc.identifier.issn 1939-1404 -
dc.identifier.scopusid 2-s2.0-85114737235 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/53999 -
dc.identifier.url https://ieeexplore.ieee.org/document/9511166 -
dc.identifier.wosid 000694698900009 -
dc.language 영어 -
dc.publisher IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC -
dc.title Estimation of Spatially Continuous Near-Surface Relative Humidity Over Japan and South Korea -
dc.type Article -
dc.description.isOpenAccess TRUE -
dc.relation.journalWebOfScienceCategory Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology -
dc.relation.journalResearchArea Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor extreme gradient boosting -
dc.subject.keywordAuthor spatially continuous near-surface relative humidity -
dc.subject.keywordAuthor MODIS -
dc.subject.keywordAuthor Humidity -
dc.subject.keywordAuthor Data models -
dc.subject.keywordAuthor Ocean temperature -
dc.subject.keywordAuthor Land surface -
dc.subject.keywordAuthor Atmospheric modeling -
dc.subject.keywordAuthor Estimation -
dc.subject.keywordAuthor East Asia -
dc.subject.keywordPlus MACHINE LEARNING ALGORITHMS -
dc.subject.keywordPlus CLASSIFICATION PERFORMANCE -
dc.subject.keywordPlus CONIFEROUS FOREST -
dc.subject.keywordPlus AIR HUMIDITY -
dc.subject.keywordPlus TEMPERATURE -
dc.subject.keywordPlus RESOLUTION -
dc.subject.keywordPlus WATER -
dc.subject.keywordPlus PRECIPITATION -
dc.subject.keywordPlus DROUGHT -
dc.subject.keywordPlus EVAPOTRANSPIRATION -

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