GEOG 4750 Glacial processes, measurements & models

Transcription

1 GEOG 4750 Glacial processes, measurements & models Lecture 13 Remote sensing of glaciers (1) Overview and snow cover Dr. Hester Jiskoot READ König et al., 2001: Measuring snow and glacier properties from satellite Pages 1-12 REMOTE SENSING OF THE CRYOSPHERE What? Sea-ice Glaciers and ice sheets Snow cover Remote Large areas Changing ~120km MODIS true color image Antarctica's B-15A iceberg 9 Oct 2003 Why? Monitoring of Status Changes Process studies (understanding) Verification of models Early warnings Predictions Break-up B-15A (270 x 40km) broke off the Ross ice shelf in March 2000, trapped sea ice and blocked the shipping route to McMurdo station By November 5, 2005, the berg had dwindled to 110 x 20 km Amery Ice Shelf, Antarctica loose tooth (Dec 2005) WHAT? Surface albedo Thickness Water content Melt onset SNOW COVER WHY? Surface energy balance Glacier mass balance Water availability -runoff -flooding -water management Climate change 1

2 WHICH SATELLITES ARE SUITABLE FOR WHICH STUDIES? Effect of image resolution on snow mapping of N and S Dakota on 7 Feb 1998 Spectrum determines which spectral bands suitable for certain studies Resolution determines smallest unit that you can capture Revisit and size of image determines how often (days) you have coverage of target Ground coverage determined how far N or S the images reach - usually poles not covered Table 1: König et al., 2001 Spectral reflectance curves for snow and ice THE SOLAR SPECTRUM reflection emission Zeng et al., 1984 Clark, 1999 SPECTRAL RANGE (nm) BANDS RESOLUTION (meters) REPEAT (days) Satellite image data: wavelength bands ASTER & LANDSAT tm ASTER Modis Landsat7 AHRR SPOT GOES PAN 6 4+PAN <16 <3 < <3 15 min SMMR SSM/I SAR 400nm-3cm Microwave Radar (1-100 cm) <6 <1 24 ASTER spectral bands, compared to Landsat ETM+. From: Kääb et al. 2002: Glacier monitoring from ASTER imagery. EARSeL Proceedings. LIS-SIG Workshop. Berne. 2

3 Satellite image data: wavelength bands MODIS Spectral reflectance is dynamic, not static Clark, 1999 Effects changes in the snow/ice Impurities visible spectrum ( μm) Grain size: near & middle IR ( μm) Liquid water: increases effective grain size Density: independent Procedure to convert satellite data to albedo (snow reflectance) 1) Take raw data numbers (DN=0-255) & convert to spectral reflectance Lλ For DN=0 and DN=255 the Lλ is known from the satellite characteristics (metadata) Linear interpolation to find Lλ values for the rest of the DN values 2) Atmospheric correction by radiative transfer models 3) Anisotropy corrections 4) Narrowband to broadband extrapolation (entire spectrum) MODIS MODERATE RESOLUTION IMAGING SPECTRORADIOMETER Onboard Terra Satellite 36 bands ( μm) Resolution: 500m and 0.05º (~5.6km) Overpass: 1-2 days Measures: Global vegetation Land surface changes Albedo Temperature Snow & ice cover GALLERY OF THE DAY MODIS TRUE COLOR IMAGES OF ICELAND 9 Sept Jan 2004 MODIS AUTOMATED SNOW-MAPPING ALGORITHMS Use: - Band 4/6 (normalised difference snow index: NDSI) - Band 1/2 (normalised difference vegetation index: NDVI) - Band 2 NDSI > 0.4 snow Reflectance in Band 2 should be >11% Band 6 stays high for clouds If NDVI maps forest then map snow for NDSI < 0.4 as well. Reflectance in Band 4 should then be >10% in the forest 3

4 MODIS MOSAICS Max snow cover in period Jan Average since million km million km million km million km million km 2 Problems with MODIS satellite data Atmospheric corrections algorithms Grid size sub pixel snow Darkness need passive microwave Albedo narrowband to broadband reflectance anisotropy (need bidirectional reflectance correction) Clouds obscuring or false snow (due to ice in high clouds: Summer) Errors of omission snow-free snow cloud snow melted image image image MODIS true color image & corresponding false color image of Lake Erie 8 February 2004 Ice/Cloud/Snow? Blue = ice Black/blue = water Turquoise = snow/cloud Green = ground w. thin snow cover MODIS calibrated radiance data product (MOD02HKM), the geolocation product (MOD03), and the cloud mask product (MOD35_L2) inputs and MODIS snow algorithm output (MOD10_L2) White=snow Pink=cloud Grey=no data Green=land Blue=water Animation of: Continental snow cover in the Northern Hemisphere for winter from MODIS-derived 8-day composite snow maps (resolution ~5 km). CLOUD AND SNOW Sierra Nevada Mountains (CA), derived from MODIS-derived daily snow maps (500 m resolution), for the winter and spring of Snow can cover >50% of the N-Hemisphere land surface 4

5 Kryuchevsky Volcanic Group, Kamchatka, Russia Snow depths & snow water equivalent (SWE) Volume scattering by snow reduces the microwave radiation from the underlying ground Active and passive microwave sensors SMMR, SSM/I Projected on EASE-Grid 25x25km resolution Landsat image (+ eruption) Radar image Snow melt initiation Error estimates Based on comparison with ground measurements and image comparison Passive microwave sensors (SMMR, SSM/I) algorithms based on diurnal difference in brightness temperature Links to papers don t work: use the following page Snow cover area: Modis, AVHRR, GOES-8 etc. 5-10% Passive microwave <1% SWE = snow water equivalent: SAR and SMM/I large errors with point measurements, but only 10-20mm w.e. when area-averaged 5