Spatially explicit regionalization of airborne flux measurements using environmental response functions
Stefan Metzger,Wolfgang Junkermann,Matthias Mauder,Klaus Butterbach-Bahl,B. Trancon y Widemann,Frank Neidl,Klaus Schäfer,S. Wieneke,X. H. Zheng,Hans Peter Schmid,Thomas Foken +10 more
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In this article, the authors characterized the sensible (H ) and latent (LE) heat exchange for different land covers in the heterogeneous steppe landscape of the Xilin River catchment, Inner Mongolia, China.Abstract:
The goal of this study is to characterize the sensible ( H ) and latent (LE) heat exchange for different land covers in the heterogeneous steppe landscape of the Xilin River catchment, Inner Mongolia, China. Eddy-covariance flux measurements at 50–100 m above ground were conducted in July 2009 using a weight-shift microlight aircraft. Wavelet decomposition of the turbulence data enables a spatial discretization of 90 m of the flux measurements. For a total of 8446 flux observations during 12 flights, MODIS land surface temperature (LST) and enhanced vegetation index (EVI) in each flux footprint are determined. Boosted regression trees are then used to infer an environmental response function (ERF) between all flux observations ( H , LE) and biophysical (LST, EVI) and meteorological drivers. Numerical tests show that ERF predictions covering the entire Xilin River catchment (a3670 km 2 ) are accurate to ≤18% (1 σ). The predictions are then summarized for each land cover type, providing individual estimates of source strength (36 W m −2 H −2 , 46 W m −2 −2 ) and spatial variability (11 W m −2 H −2 , 14 W m −2 LE −2 ) to a precision of ≤5%. Lastly, ERF predictions of land cover specific Bowen ratios are compared between subsequent flights at different locations in the Xilin River catchment. Agreement of the land cover specific Bowen ratios to within 12 p 9% emphasizes the robustness of the presented approach. This study indicates the potential of ERFs for (i) extending airborne flux measurements to the catchment scale, (ii) assessing the spatial representativeness of long-term tower flux measurements, and (iii) designing, constraining and evaluating flux algorithms for remote sensing and numerical modelling applications.read more
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Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites
Housen Chu,Xiangzhong Luo,Xiangzhong Luo,Zutao Ouyang,W. Stephen Chan,Sigrid Dengel,Sébastien C. Biraud,Margaret S. Torn,Stefan Metzger,Stefan Metzger,Jitendra Kumar,M. Altaf Arain,Timothy J. Arkebauer,Dennis D. Baldocchi,Carl J. Bernacchi,D. P. Billesbach,T. Andrew Black,Peter D. Blanken,Gil Bohrer,Rosvel Bracho,Shannon E. Brown,Nathaniel A. Brunsell,Jiquan Chen,Xingyuan Chen,Kenneth L. Clark,Ankur R. Desai,Tomer Duman,David Durden,Silvano Fares,Inke Forbrich,John A. Gamon,John A. Gamon,Christopher M. Gough,Timothy J. Griffis,Manuel Helbig,Manuel Helbig,David Y. Hollinger,Elyn Humphreys,Hiroki Ikawa,Hiroki Iwata,Yang Ju,John F. Knowles,Sara H. Knox,Hideki Kobayashi,Thomas Kolb,Beverly E. Law,Xuhui Lee,Marcy E. Litvak,Heping Liu,J. William Munger,Asko Noormets,Kimberly A. Novick,Steven F. Oberbauer,Walter C. Oechel,P. Y. Oikawa,Shirley A. Papuga,Elise Pendall,Prajaya Prajapati,John H. Prueger,William L. Quinton,Andrew D. Richardson,Eric S. Russell,Russell L. Scott,Gregory Starr,Ralf M. Staebler,Paul C. Stoy,Ellen Stuart-Haëntjens,Oliver Sonnentag,Ryan C. Sullivan,Andrew E. Suyker,Masahito Ueyama,Rodrigo Vargas,Jeffrey D. Wood,Donatella Zona,Donatella Zona +74 more
TL;DR: In this article, the authors evaluate the representativeness of flux footprints and evaluate potential biases as a consequence of the footprint-to-target-area mismatch, which can be used as a guide to identify site-periods suitable for specific applications.
Journal ArticleDOI
Evaluating Different Machine Learning Methods for Upscaling Evapotranspiration from Flux Towers to the Regional Scale
Tongren Xu,Zhixia Guo,Shaomin Liu,Xinlei He,Yangfanyu Meng,Ziwei Xu,Youlong Xia,Jingfeng Xiao,Yuan Zhang,Yanfei Ma,Lisheng Song +10 more
TL;DR: Five machine learning algorithms are employed for ET upscaling including artificial neural network, Cubist, deep belief network, random forest, and support vector machine to upscale ET from eddy covariance flux tower sites to the regional scale with machinelearning algorithms.
Relationship Between Evapotranspiration and Precipitation Pulses in a Semiarid Rangeland Estimated by Moisture Flux Towers and MODIS Vegetation Indices
Pamela L. Nagler,Edward P. Glenn,H. Kim,W. E. Emmerich,Russell L. Scott,Travis E. Huxman,Alfredo Huete +6 more
TL;DR: In this article, the authors used moisture Bowen ratio flux tower data and the enhanced vegetation index (EVI) from the moderate resolution imaging spectrometer (MODIS) on the Terra satellite to measure and scale evapotranspiration (ET) over sparsely vegetated grassland and shrubland sites in a semiarid watershed in southeastern Arizona from 2000 to 2004.
Journal ArticleDOI
The value of soil respiration measurements for interpreting and modeling terrestrial carbon cycling
Claire L. Phillips,Ben Bond-Lamberty,Ankur R. Desai,Martin Lavoie,Dave Risk,Jianwu Tang,Katherine Todd-Brown,Rodrigo Vargas +7 more
TL;DR: In this article, the authors identify three major challenges in interpreting RS data, and opportunities to utilize it more extensively and creatively: (1) When RS is compared to ecosystem respiration (RECO) measured from EC towers, it is not uncommon to find RS < RECO, which provides an opportunity to utilize RS for EC quality control.
Journal ArticleDOI
Upscaling tower-observed turbulent exchange at fine spatio-temporal resolution using environmental response functions
TL;DR: In this paper, a new approach was developed to project turbulent flux maps at regional scale and hourly temporal resolution using environmental response functions (ERFs), which is based on an approach employed in airborne flux observations, and relates turbulent flux observations to meteorological forcings and surface properties across the flux footprint.
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