Retrieval of subpixel snow-covered area and grain size from imaging spectrometer data

• Published in: Remote sensing of environment Vol. 85; no. 1; pp. 64 - 77
• Main Authors: Painter, Thomas H., Dozier, Jeff, Roberts, Dar A., Davis, Robert E., Green, Robert O.
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 25.04.2003; Elsevier Science
• Subjects: Albedo; AVIRIS; Earth, ocean, space; Exact sciences and technology; External geophysics; Geophysics. Techniques, methods, instrumentation and models; Imaging spectrometry; Mapping; Snow; Snow. Ice. Glaciers; California; United States; North America; America; Imaging spectrometry; AVIRIS; Mapping; Albedo; Snow; Grain size; Infrared spectrometry; Image analysis; Snow cover; Algorithm; Space remote sensing
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/S0034-4257(02)00187-6

We describe and validate an automated model that retrieves subpixel snow-covered area and effective grain size from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. The model analyzes multiple endmember spectral mixtures with a spectral library of snow, vegetation, rock, and soil. We derive snow spectral endmembers of varying grain size from a radiative transfer model; spectra for vegetation, rock, and soil were collected in the field and laboratory. For three AVIRIS images of Mammoth Mountain, California that span common snow conditions for winter through spring, we validate the estimates of snow-covered area with fine-resolution aerial photographs and validate the estimates of grain size with stereological analysis of snow samples collected within 2 h of the AVIRIS overpasses. The RMS error for snow-covered area retrieved from AVIRIS for the combined set of three images was 4%. The RMS error for snow grain size retrieved from a 3×3 window of AVIRIS data for the combined set of three images is 48 μm, and the RMS error for reflectance integrated over the solar spectrum and over all hemispherical reflectance angles is 0.018.