Upload
megara
View
60
Download
0
Embed Size (px)
DESCRIPTION
Visible and Near-Infrared Systems and Publicly-Available Products for Studies of Seasonal Snow Cover. Dorothy K. Hall. Cryospheric Sciences Laboratory NASA / GSFC, Greenbelt, Md. NASA Snow Remote Sensing Workshop CIRES - University of Colorado Boulder, Colorado - PowerPoint PPT Presentation
Citation preview
Photo credit: D.K. Hall / NASA
Visible and Near-Infrared Systems and Publicly-Available Products for
Studies of Seasonal Snow Cover
Dorothy K. Hall
Cryospheric Sciences LaboratoryNASA / GSFC, Greenbelt, Md.
NASA Snow Remote Sensing WorkshopCIRES - University of Colorado
Boulder, Colorado14 August 2013
snowcrystals.com
What advances have led to the current state of snow mapping
using visible and near-IR sensors?
Image from TIROS-1, the First Successful Weather Satellite
TIROS=Television and Infrared Observation Satellite
TIROS provided data for the first accurate weather forecasts based on data gathered from space.
TIROS began continuous coverage of the Earth's weather in 1962.
From NASA: http://www.earth.nasa.gov/history/tiros/tiros.html
Snow Mapping from Space in 1962
Issues in the 1960s
•Spatial resolution
•Snow / cloud discrimination
•Snow depth
From: Barnes & Bowley, 1966, WRR, 4(2):257-272.
Snow Extent and Snow Depth Mapping from Space - 1966
Accuracy of snow mapping from TIROS or ESSA data was estimated to be ±32 km in 1966
Apt is automatic picture transmission
NOAA visible climate data record
1973 1998 2007
Nov 1966
Oct 1972
May 1975 1980-81 1990s
May 1999
Feb 1997 Feb 2004
ESSA, NOAA, GOESSeries
Weekly 190 kmdigitized
METEOSAT&
GMS added
Reanalysis of 1966-71
IMS 24 km IMS 4 km7
Slide courtesy of Dave Robinson/Rutgers Univ.
The NESDIS product had not been envisioned at first as a CDR; more standardized mapping was instituted in the 1970s.
•Launch of ERTS-1 (Landsat-1) in 1972 allowed snow-cover mapping at ~80-m spatial resolution once every 18 days, cloud-cover permitting;
Progress in the 1970s
From Colvocoresses, 1976
ERTS-1 (Landsat-1) Image of Rocky Mts., 11 January 1973
Rocky Mts.
Snow mapping was largely accomplished using hard-copy images and manual techniques in the 1970 and early 1980s.
•First NOAA Advanced Very High Resolution Radiometer (AVHRR) was launched in 1978 on TIROS-N;
•NOAA NESDIS snow maps were being produced weekly for the Northern Hemisphere and in an increasingly-consistent manner.
Progress in the 1970s, cont’d.
NOAA Northern Hemisphere Snow Chart
NOAA / NESDIS
NESDIS provided weekly snow maps
such as this one from 1972 – 1997
at~190 km spatial
resolution
•Early Landsat (MSS): • Could not separate snow and most clouds;• Low dynamic range of MSS instrument over snow;• 18-day repeat was not frequent enough;• Cloud cover obscured the surface too often.
•Need more-frequent coverage at good spatial resolution with snow/cloud discrimination;• TM band 5 (1.6 µm)* under development for objectively separating
snow from clouds with an expected launch in 1982 (Barnes and Bowley, 1977).
Issues in the 1970s
*Decline in SWIR reflectance of snow was useful in separating snow and clouds.
•Use of remotely-sensed data from satellites related to water-resources management is still lacking in several respects (V. Salomonson, in 1979):
• Spatial and spectral resolution;• Processing of high volumes of data & high data rate*;• Need 48-hour turnaround time for water-resource
managers;• High data rate conflicts with rapid turnaround of data
and ease of processing.
*We must “learn to drink from a firehose” (Salomonson & Hall 1979) referring to high data volumes to be produced by Landsat-4 TM that would be in the 107 to 108 bits/sec order of magnitude. Data rate today for Landsat-8 is ~4.5 times what it was for Landsat-4.
Issues in the 1970s
•“…it seems possible to simulate discharge in ungaged sites by using Landsat images of the snow cover together with temperature and precipitation data.” (Martinec and Rango, 1979).
Progress in the 1970s, cont’d.
From Rango (1980), IAHS Pub. #129
Landsat-1 image of snow cover in Wyoming, 28 June 1976, showing boundaries of the Dinwoody Creek and Bull Lake
Creek Basins
Using a zoom transfer scope, a
satellite image was superimposed on a
map and the snowline was
drawn onto the map. SCA was determined by planimetering.
Landsat-1-Derived Snow Cover Estimates vs. Measured Runoff in the Wind River Range, Wyo., 1973 – 1974
From Rango and Salomonson (1975)
•Image saturation in snow was improved with the larger dynamic range of the TM instruments (vs. MSS) allowed great strides to be made in mapping snow cover and albedo using Landsat data in the 1980s (e.g., Dozier, 1984 & Dozier & Marks, 1987);
•Rosenthal and Dozier (1996) present a decision-tree classification model using Landsat TM data to map fractional snow cover in the Sierra Nevada Mts;
•Landsat-7 ETM+ and the MODerate-resolution Imaging Spectroradiometer (MODIS) were launched in 1999.
Progress in the 1980s and 1990s
NOAA visible climate data record
1973 1998 2007
Nov 1966
Oct 1972
May 1975 1980-81 1990s
May 1999
Feb 1997 Feb 2004
ESSA, NOAA, GOESSeries
Weekly 190 kmdigitized
METEOSAT&
GMS added
Reanalysis of 1966-71
IMS 24 km IMS 4 km18
Slide courtesy of Dave Robinson/Rutgers Univ.
www.natice.noaa.gov/ims/
NOAA Interactive Multisensor Snow and Ice Mapping System (IMS)
NESDIS and the National Ice
Center (NIC) have provided daily
snow maps such as this one from
1997 – present at25 km and 4
km (since 2004) spatial resolution
www.natice.noaa.gov/ims/
NOAA Interactive Multisensor Snow and Ice Mapping System (IMS) 1 January 2013
http://climate.rutgers.edu/snowcover/index.php
Daily Snow Map from Rutgers University Global Snow Lab Climate Data Record of Snow-Cover Extent
1 January 2013
NESDIS & NIC maps were elevated to the status of a snow-cover extent Climate Data Record by RUCL
D. Robinson / Rutgers Univ.
Northern Hemisphere monthly average snow extents since 1966*
*compared the long term average
Source: Rutgers University Global Snow Lab
Publicly-Available Satellite Snow-Cover Products
National Ice Center (NIC) snow-cover map
Northeastern U.S. MODIS swath fractional
snow-cover map, MOD10_L2
MODIS monthly snow-cover map, MOD10CM
Feb. 2004
28 Dec. 2010
IMS12 Feb. 2012 Daily, weekly and monthly snow cover and SWE maps
from GlobSnow
NOHRSC modeled SWE map
1 Jan 2013
MODIS 500- Cloud-Gap-Filled Snow Map
Suomi NPP Visible Infrared Imaging Radiometer Suite (VIIRS)* Snow-Cover Products
•Higher spatial resolution than MODIS (375 m);•Wider swath (3000 km vs. 2330 km), no orbit gaps compared to MODIS;•Primarily for operational user community;• Need NRT turnaround for weather forecasting.
•Expected continuity with MODIS to make a Climate-Data Record (CDR) of snow-cover extent.
*Launched in October 2011
VIIRS Binary Snow-Cover Map*12 February 2012
*VIIRS binary snow-cover algorithm is based on the MODIS snow-cover algorithm.
cloud
cloud
cloud
snow
From R. Solberg et al. http://www.globsnow.info/workshops/innsbruck/1t/SolbergSE.pdf
GlobSnow
MEaSUREs Snow Product: blended MODIS, IMS and passive microwave
Slide courtesy of Dave Robinson/Rutgers Univ.
Other results from blended snow maps include those of Romanov, 2000; Hall et al., 2007; Gao et al., 2010; Foster et al., 2011;
First successful weather satellite
VIIRS “Blue Marble”
April 1, 1960
Conclusion•Major advances in mapping snow-cover extent and snow albedo have been enabled by technological and computational advances;
•Many early (1960s & 1970s) issues with mapping snow were addressed by the launch of new instruments (e.g., Landsat-1 MSS in 1972 and AVHRR in 1978);
•Increasingly-sophisticated instruments launched in the 1980s permitted refinement of snow-mapping capabilities (e.g., Landsat-4 and -5 TM in 1982 & 1984, and an improved AVHRR in 1981);
•MODIS and VIIRS, first launched in 1999 and 2011, respectively, allow daily snow mapping for research and operational purposes;
•Blended products, such as GlobSnow, map snow cover and SWE through clouds;
•Snow maps from vis/near-IR sensors have improved dramatically in the last ~40 years leading to great advances; far fewer advances in mapping of snow through clouds and SWE have been possible using passive-MW sensors.