Search Results
S. , 2002 : Polarimetric radar remote sensing of ocean surface wind. IEEE Trans. Geosci. Remote Sens. , 40 , 793 – 800 . 10.1109/TGRS.2002.1006350 Fig . 1. Map of CLPX study areas where airborne data were collected. Nominal flight tracks for DC-8 and P-3 aircraft (active and passive microwave, including AIRSAR, PSR, and POLSCAT) and for gamma radiation snow surveys are shown. Fig . 2. High-resolution color-infrared orthoimagery of a portion of the Spring Creek ISA (view to southwest
S. , 2002 : Polarimetric radar remote sensing of ocean surface wind. IEEE Trans. Geosci. Remote Sens. , 40 , 793 – 800 . 10.1109/TGRS.2002.1006350 Fig . 1. Map of CLPX study areas where airborne data were collected. Nominal flight tracks for DC-8 and P-3 aircraft (active and passive microwave, including AIRSAR, PSR, and POLSCAT) and for gamma radiation snow surveys are shown. Fig . 2. High-resolution color-infrared orthoimagery of a portion of the Spring Creek ISA (view to southwest
-airborne: Infrared orthophotography and LiDAR topographic mapping. National Snow and Ice Data Center, Boulder, CO, digital media. [Available online at http://nsidc.org/data/docs/daac/nsidc0157_clpx_lidar/ .] . Nijssen, B. , and Lettenmaier D. , 1999 : A simplified approach for predicting shortwave radiation transfer through boreal forest canopies. J. Geophys. Res. , 104 , 27859 – 27868 . 10.1029/1999JD900377 Nilson, T. , 1971 : A theoretical analysis of the frequency of gaps in plant stands. Agric
-airborne: Infrared orthophotography and LiDAR topographic mapping. National Snow and Ice Data Center, Boulder, CO, digital media. [Available online at http://nsidc.org/data/docs/daac/nsidc0157_clpx_lidar/ .] . Nijssen, B. , and Lettenmaier D. , 1999 : A simplified approach for predicting shortwave radiation transfer through boreal forest canopies. J. Geophys. Res. , 104 , 27859 – 27868 . 10.1029/1999JD900377 Nilson, T. , 1971 : A theoretical analysis of the frequency of gaps in plant stands. Agric
/snow; and H g , L g , and P g are the sensible, latent, and precipitation heat fluxes (W m −2 ), respectively, at the ground/snow surface; σ is the Stefan–Boltzman constant (5.699 × 10 −8 W m −2 K −4 ); I ↓ s and I ↓ ir are the total incoming solar and infrared radiation (W m −2 ), respectively; H f , L f , and P f are the sensible, latent and precipitation heat fluxes (W m −2 ), respectively, at the foliage surface; c p ,veg = 3500 J kg −1 K −1 and κ veg = 0.38 W m −1 K −1 are
/snow; and H g , L g , and P g are the sensible, latent, and precipitation heat fluxes (W m −2 ), respectively, at the ground/snow surface; σ is the Stefan–Boltzman constant (5.699 × 10 −8 W m −2 K −4 ); I ↓ s and I ↓ ir are the total incoming solar and infrared radiation (W m −2 ), respectively; H f , L f , and P f are the sensible, latent and precipitation heat fluxes (W m −2 ), respectively, at the foliage surface; c p ,veg = 3500 J kg −1 K −1 and κ veg = 0.38 W m −1 K −1 are
and Appel 2004 ). Recent progress has reported on how to estimate snow surface grain size and wetness as well ( Green et al. 2002 ; Painter et al. 2003 ). However, snow reflectance in the visible and near-infrared region is insensitive to snow water equivalent (except for shallow snow) because the radiation in these wavelengths does not significantly penetrate. Furthermore, visible and near-infrared sensing requires solar illumination and cannot see through clouds or forest elements. The CLPX
and Appel 2004 ). Recent progress has reported on how to estimate snow surface grain size and wetness as well ( Green et al. 2002 ; Painter et al. 2003 ). However, snow reflectance in the visible and near-infrared region is insensitive to snow water equivalent (except for shallow snow) because the radiation in these wavelengths does not significantly penetrate. Furthermore, visible and near-infrared sensing requires solar illumination and cannot see through clouds or forest elements. The CLPX
Service measured the above-canopy radiation as well as other meteorological components at the LSOS ( Elder et al. 2009b ). Spatial variations in the longwave radiation emitted were compared between the tree trunks, snow, and ground foliage using a thermal infrared imaging radiometer (8–12 μ m). The measurements of the hyperspectral hemispherical directional reflectance factor (HDRF) of the snow surface (wavelength range 0.35–2.5 μ m), using a field spectroradiometer, provided calibration data for
Service measured the above-canopy radiation as well as other meteorological components at the LSOS ( Elder et al. 2009b ). Spatial variations in the longwave radiation emitted were compared between the tree trunks, snow, and ground foliage using a thermal infrared imaging radiometer (8–12 μ m). The measurements of the hyperspectral hemispherical directional reflectance factor (HDRF) of the snow surface (wavelength range 0.35–2.5 μ m), using a field spectroradiometer, provided calibration data for
the snow cover energy and mass balance (e.g., Anderson 1976 ; Male and Granger 1981 ; Marks et al. 1992 ; Marks and Dozier 1992 ; Harding and Pomeroy 1996 ; Marsh and Pomeroy 1996 ; Pomeroy et al. 1998 ; Hedstrom and Pomeroy 1998 ; Marks et al. 1998 ; Luce et al. 1998 , 1999 ; Tarboton et al. 2000 ; Marks and Winstral 2001 ; Tribbeck et al. 2004 ; Pomeroy et al. 2002 , 2003 ; Essery et al. 1998 ). All these studies show that radiation and turbulent fluxes dominate the snow cover
the snow cover energy and mass balance (e.g., Anderson 1976 ; Male and Granger 1981 ; Marks et al. 1992 ; Marks and Dozier 1992 ; Harding and Pomeroy 1996 ; Marsh and Pomeroy 1996 ; Pomeroy et al. 1998 ; Hedstrom and Pomeroy 1998 ; Marks et al. 1998 ; Luce et al. 1998 , 1999 ; Tarboton et al. 2000 ; Marks and Winstral 2001 ; Tribbeck et al. 2004 ; Pomeroy et al. 2002 , 2003 ; Essery et al. 1998 ). All these studies show that radiation and turbulent fluxes dominate the snow cover
1. Introduction Snow, because of its unique properties such as high albedo and low thermal conductivity, affects land surface radiation budgets and water balance ( Yang et al. 1999 ). Significant gains have been made in snow cover mapping using remotely sensed data in recent decades, but the presence of forests continues to present challenges ( Simpson et al. 1998 ; Hall et al. 1998 ; Hall et al. 2002 ; Dozier and Painter 2004 ). An understanding of the manner in which forest canopies
1. Introduction Snow, because of its unique properties such as high albedo and low thermal conductivity, affects land surface radiation budgets and water balance ( Yang et al. 1999 ). Significant gains have been made in snow cover mapping using remotely sensed data in recent decades, but the presence of forests continues to present challenges ( Simpson et al. 1998 ; Hall et al. 1998 ; Hall et al. 2002 ; Dozier and Painter 2004 ). An understanding of the manner in which forest canopies
ice as compared with snow-free areas. Microwave brightness temperature measured by spaceborne sensors over snow-covered areas originates from radiation emitted from the underlying surface, the snowpack, the vegetation, and the atmosphere. In theory, the dielectric constant of frozen water is altered relative to that of water in its liquid form, and the effect of snow on the emissivity can be used in algorithms to estimate snow water equivalence (SWE) from spaceborne emissions, typically at 18
ice as compared with snow-free areas. Microwave brightness temperature measured by spaceborne sensors over snow-covered areas originates from radiation emitted from the underlying surface, the snowpack, the vegetation, and the atmosphere. In theory, the dielectric constant of frozen water is altered relative to that of water in its liquid form, and the effect of snow on the emissivity can be used in algorithms to estimate snow water equivalence (SWE) from spaceborne emissions, typically at 18
