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An artificial intelligence approach to predict gross primary productivity in the forests of south korea using satellite remote sensing data
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2022-04-06
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TitleTitleTitleTitleTitleTitleTitleTitleTitleTitleTitleTitle ³ó¸²»ýÅ°è Áö¼Ó°¡´É¼º Á¦°í¸¦ À§ÇÑ ³ó¸²±â»óÇÐÀÇ µµÀü°ú °úÁ¦Àå±â °üÃø ¿¡µð Ç÷°½º ÀÚ·áÀÇ ¿¬¼Ó¼º È®º¸¿¡ ´ëÇÏ¿© : °³È¸·Î ¹× ºÀÆóȸ·Î ±âüºÐ¼®±âÀÇ ¾ß¿Ü »óÈ£ ºñ±³Anticipating global terrestrial ecosystem state change using fluxnet.The influence of tree structural and species diversity on temperate forest productivity and stability in korea.Impact of leaf area index from various sources on estimating gross primary production in temperate forests using the jules land surface model.Fluxnet-ch4 synthesis activity : Objectives, observations, and future directions.Gap-filling approaches for eddy covariance methane fluxes : A comparison of three machine learning algorithms and traditional method with principal component analysis.Modification of the moving point test method for nighttime eddy co2 flux filtering on hilly and complex terrains.New Gap-filling strategies for long-period flux data gaps using a data-driven approach.Making full use of hyperspectral data for gross primary productivity estimation with multivariate regression : Mechanistic insights from observations and process-based simulations.An Approximate Estimation of snow Weight Using KMA Weather Station Data and Snow Density Formulae.Simulation and Analysis of Solar Radiation Change Resulted from Solar-sharing for Agricultural Solar Photovoltaic SystemConstruction of NCAM-LAMP Precipitaion and Soil Moisture Database to Support Landslide PredictionDatabase Construction of High-resolution Daily Meteorological and Climatological Data Using NCAM-LAMP : Sunshine Hour DataGap-filling approaches for eddy covariance methane fluxes: A comparison of three machine learning algorithms and a traditional method with principal component analysis.An artificial intelligence approach to predict gross primary productivity in the forests of south korea using satellite remote sensing data
AuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthors ±è±¤¼ö, ¹®°æȯ, õÁ¤È­, ±èÁØȯ, °­¹Î¼®, ±è´ëÁØ°­¹Î¼®, ±èÁØ, ¾çÇö¿µ, ÀÓÁ¾È¯, õÁ¤È­, ¹®¹Î±ÔYu R, Ruddell BL, Kang M, Kim J and Childers DPark J, Kim HS, Jo HK and Jung IBLee H, Park J, Cho S, Lee M, and Kim HSKnox SH, Jackson RB, Poulter B, McNicol G, Fluet-Chouinard E, Zhang Z, Hugelius G, Bousquet P, Canadell JG and Saunois MKim Y, Johnson MS, Knox SH, Black TA, Dalmagro HJ, Kang M, Kim J and Baldocchi DKang M, Kim J, Malla Thakuri B, Chun J and Cho CKang M, Ichii K, Kim J, Indrawati YM, Park J, Moon M, Lim J-H and Chun J-HDechant B, Ryu Y and Kang MJ J, L SJ, C WSI Lee, JY Choi, SJ Sung, SJ Lee, J Lee, W ChoiY So, SJ Lee, SW Choi, SJ LeeS Lee, SJ Lee, K JSKim Y, Johnson MS, Knox SH, Black TA, Dalmagro HJ, Kang M, Kim J and Baldocchi DLee B, Kim N, Kim E-S, Jang K, Kang M, Lim J-H, Cho J and Lee Y
PublicationPublicationPublicationPublicationPublicationPublicationPublicationPublicationPublicationPublicationPublicationPublication 2018201820182018201820182018201920192019201920192019201920192019201920192019201920192019202020202020202020202020
JournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournal Atmosphere 9The Journal of Engineering Geology 28Korean Journal of Agricultural and Forest MeteorologyKorean Journal of Agricultural and Forest MeteorologyKorean Journal of Agricultural and Forest Meteorology Korean Journal of Agricultural and Forest Meteorology Korean Journal of Agricultural and Forest Meteorology Korean Journal of Agricultural and Forest Meteorology 21Korean Journal of Agricultural and Forest MeteorologyGlobal change biology, 25Forests, 10Agricultural and Forest Meteorology 276Bulletin of the American Meteorological Society, 100 Global change biologyMethodsXAtmosphereRemote Sensing of Environment 234Korean Journal of Agricultural and Forest Meteorology The Korean Society of Agricultural Engineers 62Korean Journal of Agricultural and Forest Meteorology 22Korean Journal of Agricultural and Forest Meteorology 22Global change biology 26Forests
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Development of land surface albedo algorithm for the gk-2a/ami instrument.
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TitleTitleTitleTitleTitleTitleTitleTitleTitleTitleTitleTitle ³ó¸²»ýÅ°è Áö¼Ó°¡´É¼º Á¦°í¸¦ À§ÇÑ ³ó¸²±â»óÇÐÀÇ µµÀü°ú °úÁ¦Àå±â °üÃø ¿¡µð Ç÷°½º ÀÚ·áÀÇ ¿¬¼Ó¼º È®º¸¿¡ ´ëÇÏ¿© : °³È¸·Î ¹× ºÀÆóȸ·Î ±âüºÐ¼®±âÀÇ ¾ß¿Ü »óÈ£ ºñ±³Anticipating global terrestrial ecosystem state change using fluxne..
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Gap-filling approaches for eddy covariance methane fluxes: A comparison of three machine learning algorithms and a traditional method with principal component analysis.
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TitleTitleTitleTitleTitleTitleTitleTitleTitleTitleTitle ³ó¸²»ýÅ°è Áö¼Ó°¡´É¼º Á¦°í¸¦ À§ÇÑ ³ó¸²±â»óÇÐÀÇ µµÀü°ú °úÁ¦Àå±â °üÃø ¿¡µð Ç÷°½º ÀÚ·áÀÇ ¿¬¼Ó¼º È®º¸¿¡ ´ëÇÏ¿© : °³È¸·Î ¹× ºÀÆóȸ·Î ±âüºÐ¼®±âÀÇ ¾ß¿Ü »óÈ£ ºñ±³Anticipating global terrestrial ecosystem state change using fluxnet.The..