Vegetation biochemical and biophysical parameters related to water are pivotal to understanding the water cycle and its interactions with carbon and energy balance. This study assessed a wide range of methods to estimate foliar water content (FWC, g/cm2), canopy water content (CWC, g/cm2), fuel moisture content (FMC) and interrelated variables leaf mass per area (LMA, g/cm2), foliar biomass (FB, g/m2), and leaf area index (LAI, m2/m2) using multitemporal Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data. Estimations are compared to in-situ measurements stratified by cover type (i.e. grasses, shrubs and forest) made at Stanford University's Jasper Ridge Biological Preserve, California, USA. Curve-fitting techniques, a widely accepted method to retrieve CWC from AVIRIS, proved relatively inaccurate. Standard and recently designed vegetation indexes (VIs) provided higher accuracy; however, the most accurate VI differed by variable and by cover types. To evaluate if a hyperspectral narrow band sensor enhances the retrieval of these variables over multispectral broad bands, AVIRIS was convolved to Moderate Resolution Imaging Spectroradiometer (MODIS) bands. Best band combination indexes out of all possible bands improved the retrievals significantly over VI in the case of FMC, LMA and FB using AVIRIS bands in the longer SWIR wavelength region. AVIRIS PROSAIL and MODIS CWC PROSAIL radiative transfer model inversion had difficulty retrieving three of these variables simultaneously without a precise knowledge of the remaining chemical and physical conditions of the vegetation and soil. The SWIR region must be further investigated for water retrievals given that soil, dry mass and water are interrelated in the spectral signal plus the additional unknown impact of canopy structure upon the spectrum. link to publication
Casas A, Riaño D, Ustin SL, Dennison P, Salas J. 2014. Estimation of water-related biochemical and biophysical vegetation properties using multitemporal airborne hyperspectral data and its comparison to MODIS spectral response. Remote Sensing of Environment 148(0):28-41. doi: http://dx.doi.org/10.1016/j.rse.2014.03.011.
Year Published: 2014