CGSpaceA Repository of Agricultural Research Outputs
    View Item 
    •   CGSpace Home
    • International Livestock Research Institute (ILRI)
    • ILRI Programs
    • Former ILRI programs
    • ILRI program on livestock systems and environment
    • ILRI LSE program outputs
    • View Item
       
    • CGSpace Home
    • International Livestock Research Institute (ILRI)
    • ILRI Programs
    • Former ILRI programs
    • ILRI program on livestock systems and environment
    • ILRI LSE program outputs
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Spatially explicit regionalization of airborne flux measurements using environmental response functions

    Thumbnail
    
    Authors
    Metzger, S.
    Junkermann, W.
    Mauder, M.
    Butterbach-Bahl, Klaus
    Trancon y Widemann, B.
    Neidl, F.
    Schafer, K.
    Wieneke, S.
    Zheng, X.H.
    Schmid, H.P.
    Foken, T.
    Date
    2013-04
    Language
    en
    Type
    Journal Article
    Accessibility
    Limited Access
    Metadata
    Show full item record
    Share
    Citation
    Metzger, S., Junkermann, W., Mauder, M., Butterbach-Bahl, K., Trancón y Widemann, B., Neidl, F., Schäfer, K., Wieneke, S., Zheng, X.H., Schmid, H.P. and Foken, T. 2013. Spatially explicit regionalization of airborne flux measurements using environmental response functions. Biogeosciences 10: 2193 - 2217
    Permanent link to cite or share this item: http://hdl.handle.net/10568/34446
    DOI: https://dx.doi.org/10.5194/bg-10-2193-2013
    Abstract/Description
    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 50–100m above ground were conducted in July 2009 using a weight-shift microlight aircraft. Wavelet decomposition of the turbulence data enables a spatial discretization of 90m 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 (≈3670 km2) are accurate to ≤18% (1σ). The predictions are then summarized for each land cover type, providing individual estimates of source strength (36Wm−2 < H < 364Wm−2, 46Wm−2 < LE < 425Wm−2) and spatial variability (11Wm−2 < σ H <169Wm−2, 14Wm−2 < σLE < 152Wm−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±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.
    AGROVOC Keywords
    ENVIRONMENT; LAND MANAGEMENT
    Subjects
    ENVIRONMENT; NRM;
    Collections
    • ILRI LSE program outputs [296]

    AboutSend Feedback
     

    My Account

    LoginRegister

    Browse

    All of CGSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesBy AGROVOC keywordBy ILRI subjectBy CPWF subjectBy CCAFS subjectBy CIFOR subjectBy IWMI subjectBy RegionBy CountryBy SubregionBy CRP subjectBy River basinBy Output typeBy CTA subjectBy WLE subjectBy Bioversity subjectBy CIAT subjectBy CIP subjectBy animal breedBy CGIAR System subjectThis CollectionBy Issue DateAuthorsTitlesBy AGROVOC keywordBy ILRI subjectBy CPWF subjectBy CCAFS subjectBy CIFOR subjectBy IWMI subjectBy RegionBy CountryBy SubregionBy CRP subjectBy River basinBy Output typeBy CTA subjectBy WLE subjectBy Bioversity subjectBy CIAT subjectBy CIP subjectBy animal breedBy CGIAR System subject

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    AboutSend Feedback