(content link) weather.gov    
NOAA link
National Weather Service
  NWS link
National Operational Hydrologic
Remote Sensing Center

1.6 m Wavelength Important for
Operational Remote Sensing at NOAA

Snow and Cloud Mapping Supports Hydrologic and Meteorologic Forecasting

The National Operational Hydrologic Remote Sensing Center (NOHRSC; National Weather Service, NOAA) uses NOAA Polar Orbiting (AVHRR) and Geostationary (GOES) satellite data to map snow and clouds throughout the United States. Daily map products are generated to support operational hydrologic forecast models and numerical weather prediction models. To map snow and clouds, NOHRSC analysts use a supervised image classification algorithm that uses multi-band (wavelength) satellite data. The reflectance of snow and clouds is similar in the wavelengths measured by GOES channel 1 and AVHRR channels 1 and 2 (Figure 1), therefore discrimination between snow and clouds using these channels is difficult.

Satellite channel wavelengths in microns (m), and typical reflectance spectra for snow and clouds

Figure 1. Satellite channel wavelengths in microns (m), and typical reflectance spectra for snow and clouds.

Snow Can be Distinguished From Cloud at 1.6 m

The 1.6 m wavelength allows significantly improved discrimination between snow and clouds. At 1.6 m, snow has very low reflectance, while the reflectance of clouds remains high (Figure 1). Therefore, both cirrus and optically thick clouds can be directly classified and distinguished from snow at the 1.6 m wavelength (Warren, 1982). This has been clearly demonstrated using the operational Landsat Thematic Mapper satellite, which has a channel centered near 1.6 m (channel 5; 1.57-1.78 m) (Dozier, 1987; Baglio, 1989).

AVHRR Channel 3a Demonstrates Effectiveness of 1.6 m

The NOAA-15 Polar Orbiting Advanced Very High Resolution Radiometer (AVHRR) satellite sensor includes a 1.6 m channel (Ch. 3a) that was turned on for testing between March 20 - April 22, 1999. Analysts at the NOHRSC evaluated the effectiveness of the 1.6 m AVHRR data for mapping snow cover (Figure 2).

NOAA-15 AVHRR imagery of the vicinity of the Snake River Valley, Idaho, March 24, 1999

Figure 2. NOAA-15 AVHRR imagery of the vicinity of the
Snake River Valley, Idaho, March 24, 1999.

AVHRR Chanels 2, 3a, and 5 illustrate the reflectance of clouds and snow in these three wavelength regions (Figure 2). Channel 2 includes portions of the visible and near-infrared spectrum (e.g. Figure 1). The image contains significant cloud cover on the left side of the dotted line. The clouds are transparent in some areas and opaque in others. Clouds and snow have similar reflectance in this wavelength region. For example, the brightness of transparent cloud cover at point A is similar to the brightness of snow at points A' and B. Although the two features can be discriminated visually based on their different textures, their similar brightness in this channel does not readily permit discrimination between the two features using numerical classification techniques.

Channel 3a is the 1.6 m test channel. The low reflectance of snow at this wavelength, indicated in Figure 1, is clearly apparent here. Cloud reflectance remains relatively high. For example, point A is significantly brighter than points A' and B. The darkest areas in the 3a image are unforested, snow-covered areas. The dark areas further north are also snow covered, but are less dark because of increased reflectance of forest cover at 1.6 m. The large difference in snow and cloud reflectance at 1.6 m even permits identification of snow beneath thin transparent clouds, as evident in the small dark area directly below point A.

Channel 5 lies in the thermal infrared portion of the spectrum, and brightness in this channel is related to the temperature of the cloud and land surfaces. In this case, the more opaque clouds have much cooler temperatures than the land surface, while the more transparent cloud and snow temperatures are similar (e.g. at A and A'). The ability to discriminate between clouds and snow using this channel is complicated by the variable temperatures of both clouds and snow, and their often similar temperatures.

The relative abundance of snow, clouds, and forest cover were determined using the1.6 m 3a channel and linear spectral unmixing techniques (Figure 3a, c, and e) using AVHRR Channels 1,2, 3a, 4, and 5. The relative abundance of each feature in a pixel is depicted as

Relative abundance and threshold classification of (a,b) snow cover, (c,d) forest cover, and (e,f) cloud cover determined using linear spectral unmixing

Figure 3. Relative abundance and threshold classification of (a,b) snow cover, (c,d) forest cover, and (e,f) cloud cover determined using linear spectral unmixing.

Shades of gray, with darker shades indicating less abundance and lighter shades indicating more abundance. Images (a) and (c) indicate that both forest and snow cover contribute to the reflectance of individual pixels in the northern part of the image. Images (a) and (e) illustrate areas with transparent clouds, where the land surface features (e.g. snow) contribute to the pixel reflectance. These relative abundance images were classified using a simple threshold (Figure 3 b, d, and f) to illustrate the benefit of the 1.6 m channel for snow/cloud discrimination.

AVHRR 1.6 m Channel Improves Snow and Cloud Classification

Under normal AVHRR operations (without the 1.6 m channel), snow and cloud classification is based on information illustrated by channels 2 and 5 in Figure 2. Classification is based on all five channels, but channels 1 and 2 are highly correlated with each other, as are channels 4 and 5. AVHRR channel 3 (3.55 - 3.93 m) adds little or no useful information for snow/cloud discrimination. The different reflectance characteristics between snow and clouds at 1.6 m significantly improve snow and cloud discrimination (Table 1).

Table 1. Assessment of AVHRR Channel 3a for operational snow and cloud mapping tasks.

AVHRR with Normal Ch. 3 AVHRR with 1.6 m Channel
Snow/Opaque Cloud Discrimination Fair Improved
Snow/Tranparent Cloud Discrimination Poor Improved
Identification of Snow beneath Transparent Cloud Poor Improved

Baglio, J.V., and Holroyd, E.W., 1989. Methods for operational snow cover area mapping using the advanced very high resolution radiometer: San Juan Mountains Test Study, Research Technical Report, U.S. Geological Survey, Sioux Falls and U.S. Bureau of Reclamation, Denver.

Dozier, J., 1989. "Remote sensing of snow in visible and near-infrared wavelengths," Theory and Applications of Optical Remote Sensing, G. Asrar, ed., John Wiley and Sons, New York.

Warren, S., 1982. Optical properties of snow, Reviews of Geophysics and Space Physics, 20, 67.

Mission Statement  |  Contact

National Weather Service
National Operational Hydrologic Remote Sensing Center
Office of Water Prediction
1735 Lake Drive W.
Chanhassen, MN 55317

NOHRSC homepage
Contact NOHRSC
Information Quality
Page last modified: Aug 08, 2011 - cloud
About Us
Privacy Policy
Career Opportunities