Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing.
Mingjuan XieXiaofei MaYuangang WangChaofan LiHaiyang ShiXiuliang YuanOlaf HellwichChunbo ChenWenqiang ZhangChen ZhangQing LingRuixiang GaoYu ZhangFriday Uchenna OchegeAmaury FranklPhilippe De MaeyerNina BuchmannIris FeigenwinterJørgen Eivind OlesenRadoslaw JuszczakAdrien JacototAino KorrensaloAndrea PitaccoAndrej VarlaginAnkit ShekharAnnalea K LohilaArnaud CarraraAurore BrutBart KruijtBenjamin LoubetBernard HeineschBogdan ChojnickiCarole HelfterCaroline VinckeChangliang ShaoChristian BernhoferChristian BrümmerChristian WilleEeva Stiina TuittilaEiko NemitzFranco MeggioGang DongGary LaniganGeorg NiedristGeorg WohlfahrtGuoyi ZhouIgnacio GodedThomas GrünwaldJanusz OlejnikJoachim JansenJohan NeirynckJuha-Pekka TuovinenJunhui ZhangKatja KlumppKim PilegaardLadislav ŠigutLeif KlemedtssonLuca TezzaLukas HörtnaglMarek UrbaniakMarilyn RolandMarius SchmidtMark A SuttonMarkus HehnMatthew SaundersMatthias MauderMika AurelaMika KorkiakoskiMingyuan DuNadia VendrameNatalia KowalskaPaul G LeahyPavel AlekseychikPeili ShiPer WeslienShiping ChenSilvano FaresThomas FriborgTiphaine TallecTomomichi KatoTorsten SachsTrofim MaximovUmberto Morra di CellaUta ModerowYingnian LiYongtao HeYoshiko KosugiGeping LuoPublished in: Scientific data (2023)
Simulating the carbon-water fluxes at more widely distributed meteorological stations based on the sparsely and unevenly distributed eddy covariance flux stations is needed to accurately understand the carbon-water cycle of terrestrial ecosystems. We established a new framework consisting of machine learning, determination coefficient (R 2 ), Euclidean distance, and remote sensing (RS), to simulate the daily net ecosystem carbon dioxide exchange (NEE) and water flux (WF) of the Eurasian meteorological stations using a random forest model or/and RS. The daily NEE and WF datasets with RS-based information (NEE-RS and WF-RS) for 3774 and 4427 meteorological stations during 2002-2020 were produced, respectively. And the daily NEE and WF datasets without RS-based information (NEE-WRS and WF-WRS) for 4667 and 6763 meteorological stations during 1983-2018 were generated, respectively. For each meteorological station, the carbon-water fluxes meet accuracy requirements and have quasi-observational properties. These four carbon-water flux datasets have great potential to improve the assessments of the ecosystem carbon-water dynamics.