• Title of article

    Validating an integrated strategy to model net land carbon exchange against aircraft flux measurements

  • Author/Authors

    Maselli، نويسنده , , Fabio and Gioli، نويسنده , , Beniamino and Chiesi، نويسنده , , Marta and Vaccari، نويسنده , , Francesco and Zaldei، نويسنده , , Alessandro and Fibbi، نويسنده , , Luca and Bindi، نويسنده , , Marco and Miglietta، نويسنده , , Franco، نويسنده ,

  • Issue Information
    روزنامه با شماره پیاپی سال 2010
  • Pages
    9
  • From page
    1108
  • To page
    1116
  • Abstract
    Aircraft eddy covariance technique is a modern and powerful means to directly measure net ecosystem exchange (NEE) over relatively large land areas. The NEE measurements taken by a specifically developed aircraft platform (Sky Arrow ERA) over a transect in Central Italy during an 18-month period are used to validate a recently proposed modeling strategy. The strategy is based on the integration of the outputs from a NDVI-driven parametric model, C-Fix, and a model of ecosystem processes, BIOME-BGC, and can simulate both gross and net land carbon fluxes over different spatial and temporal scales. The application of this strategy to 1-km resolution ground and remotely sensed data descriptive of the study area enables the production of NEE estimates comparable to the aircraft measurements. The agreement between the two data series is high, especially when averaging the modeling outputs over areas consistent with the Sky Arrow footprint (i.e. 1–2 pixels apart from the flight line). Fractional forest cover and NDVI are the two model driving variables which explain most spatial variability of the aircraft NEE measurements. The modeling strategy yields the best performances for spring–summer seasons, when vegetation photosynthetic and respiratory processes are higher and easier to simulate. These performances are finally commented with specific emphasis on the contribution brought by remote sensing information.
  • Keywords
    NEE , Aircraft eddy covariance , C-Fix , BIOME-BGC , MODIS
  • Journal title
    Remote Sensing of Environment
  • Serial Year
    2010
  • Journal title
    Remote Sensing of Environment
  • Record number

    1629828