Vegetation Phenology Assessment Using Satellite Radar Remote Sensing: Global Monitoring of Daily and Seasonal Changes in Canopy Structure and Water Status.
Kyle
C.
McDonald, Jet Propulsion Lab, kyle.mcdonald@jpl.nasa.gov
(Presenting)
John
S.
Kimball, University of Montana, johnk@ntsg.umt.edu
Steve
Frolking, University of New Hampshire, steve.frolking@unh.edu
Mark
Fahnestock, University of New Hampshire, mark.fahnestock@unh.edu
Thomas
Milliman, University of New Hampshire, thomas.milliman@unh.edu
Radar backscatter time series can be analyzed to assess temporal changes in canopy structure and water status, including the capacity to identify significant plant stress (e.g., drought) and associated plant physiological constraints to canopy evaporation, ecosystem productivity and terrestrial carbon sequestration of atmospheric CO2. Radar backscatter sensitivity to canopy condition is a function of sensor frequency and polarization, as well as land cover type and vegetation biomass. We applied SeaWinds-on-QuikSCAT Ku-band backscatter data to monitor seasonal changes in vegetation canopy biomass and LAI for a diverse set of global biomes, and North American grassland response to regional drought. We observed strong correlations between LAI and backscatter time series for deciduous vegetation with moderate to high LAI. We also found an overall pattern in backscatter correlation with LAI that was corroborated by radar backscatter model simulations. Site-level regressions between MODIS LAI and backscatter reproduced similar magnitudes, seasonal patterns and interannual variability in radar backscatter for a diverse range of global biomes indicating synergism between visible-IR and radar remote sensing based phenology. Other sites (e.g., evergreen broadleaf and evergreen needleleaf forest) showed reduced growing season variability in canopy characteristics and associated correspondence between SeaWinds and MODIS results. Our initial results indicate that SeaWinds based predictions of growing season initiation precede MODIS LAI based estimates up to several weeks, while both sensors show similar predictions of peak canopy biomass, vegetation senescence and the end of the growing season.
This work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, at the University of Montana, and at the University of New Hampshire under contract with the National Aeronautics and Space Administration.
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