• Title of article

    Monitoring water stress in Mediterranean semi-natural vegetation with satellite and meteorological data

  • Author/Authors

    Moreno، نويسنده , , A. and Maselli، نويسنده , , F. and Chiesi، نويسنده , , M. and Genesio، نويسنده , , L. and Vaccari، نويسنده , , F. and Seufert، نويسنده , , G. and Gilabert، نويسنده , , M.A.، نويسنده ,

  • Issue Information
    روزنامه با شماره پیاپی سال 2014
  • Pages
    10
  • From page
    246
  • To page
    255
  • Abstract
    In arid and semi-arid environments, the characterization of the inter-annual variations of the light use efficiency ɛ due to water stress still relies mostly on meteorological data. Thus the GPP estimation based on procedures exclusively driven by remote sensing data has not found yet a widespread use. In this work, the potential to characterize the water stress in semi-natural vegetation of three spectral indices (NDWI, SIWSI and NDI7) – from MODIS broad spectral bands – has been analyzed in comparison to a meteorological factor (Cws). The study comprises 70 sites (belonging to 7 different ecosystems) uniformly distributed over Tuscany, and three eddy covariance tower sites. An operational methodology, which combines meteorological and MODIS data, to characterize the inter-annual variations of ɛ due to summer water stress is proposed. Its main advantage is that it relies on existing series of meteorological data characterizing each site and allows calculating a typical Cws profile that can be “updated” ( C w s * ) for the actual conditions using MODIS spectral indices. The results confirm that the modified C w s * can be used as a proxy of water stress that does not require concurrent information on meteorological data.
  • Keywords
    Light use efficiency , Water stress , Satellite data , Semi-natural vegetation
  • Journal title
    International Journal of Applied Earth Observation and Geoinformation
  • Serial Year
    2014
  • Journal title
    International Journal of Applied Earth Observation and Geoinformation
  • Record number

    2379452