• Title of article

    Satellite-based estimation of surface vapor pressure deficits using MODIS land surface temperature data

  • Author/Authors

    Hashimoto، نويسنده , , Hirofumi and Dungan، نويسنده , , Jennifer L. and White، نويسنده , , Michael A. and Yang، نويسنده , , Feihua and Michaelis، نويسنده , , Andrew R. and Running، نويسنده , , Steven W. and Nemani، نويسنده , , Ramakrishna R.، نويسنده ,

  • Issue Information
    روزنامه با شماره پیاپی سال 2008
  • Pages
    14
  • From page
    142
  • To page
    155
  • Abstract
    Vapor Pressure Deficit (VPD) is a principle mediator of global terrestrial CO2 uptake and water vapor loss through plant stomata. As such, methods to estimate VPD accurately and efficiently are critical for ecosystem and climate modeling efforts. Based on prior work relating energy partitioning, remotely sensed land surface temperature (LST), and VPD, we developed simple linear models to predict VPD using saturated vapor pressure calculated from MODIS LST at a number of different temporal and spatial resolutions. We developed and assessed the LST–VPD models using three data sets: (1) instantaneous and daytime average ground-based VPD and radiometric temperature from the Soil Moisture Experiments in 2002 (SMEX02); (2) daytime average VPD from AmeriFlux eddy covariance flux tower observations; and (3) estimated daytime average VPD from Global Surface Summary of Day (GSSD) observations. We estimated model parameters for VPD estimation both regionally (MOD11 A2) and globally (MOD11 C2) with RMSE values ranging from .32 to .38 kPa. VPD was overestimated along coastlines and underestimated in arid regions with low vegetation cover. Also, residuals were larger with higher VPDs because of the non-linear function of saturation vapor pressure with LST. Linear relationships were seen at multiple scales and appear useful for estimation purposes within a range of 0 to 2.5 kPa.
  • Keywords
    VDP , MODIS , Land surface temperature
  • Journal title
    Remote Sensing of Environment
  • Serial Year
    2008
  • Journal title
    Remote Sensing of Environment
  • Record number

    1575272