Cloud Masking for Landsat 8 and MODIS Terra Over Snow-Covered Terrain: Error Analysis and Spectral Similarity Between Snow and Cloud.

Autor: Stillinger T; Bren School of Environmental Science and Management University of California Santa Barbara CA USA., Roberts DA; Department of Geography University of California Santa Barbara CA USA., Collar NM; Bren School of Environmental Science and Management University of California Santa Barbara CA USA.; Now at Wright Water Engineers Inc. Denver CO USA.; Now at Department of Civil and Environmental Engineering Colorado School of Mines Golden CO USA., Dozier J; Bren School of Environmental Science and Management University of California Santa Barbara CA USA.
Jazyk: angličtina
Zdroj: Water resources research [Water Resour Res] 2019 Jul; Vol. 55 (7), pp. 6169-6184. Date of Electronic Publication: 2019 Jul 29.
DOI: 10.1029/2019WR024932
Abstrakt: Automated, reliable cloud masks over snow-covered terrain would improve the retrieval of snow properties from multispectral satellite sensors. The U.S. Geological Survey and NASA chose the currently operational cloud masks based on global performance across diverse land cover types. This study assesses errors in these cloud masks over snow-covered, midlatitude mountains. We use 26 Landsat 8 scenes with manually delineated cloud, snow, and land cover to assess the performance of two cloud masks: CFMask for the Landsat 8 OLI and the cloud mask that ships with Moderate-Resolution Imaging Spectroradiometer (MODIS) surface reflectance products MOD09GA and MYD09GA. The overall precision and recall of CFMask over snow-covered terrain are 0.70 and 0.86; the MOD09GA cloud mask precision and recall are 0.17 and 0.72. A plausible reason for poorer performance of cloud masks over snow lies in the potential similarity between multispectral signatures of snow and cloud pixels in three situations: (1) Snow at high elevation is bright enough in the "cirrus" bands (Landsat band 9 or MODIS band 26) to be classified as cirrus. (2) Reflectances of "dark" clouds in shortwave infrared (SWIR) bands are bracketed by snow spectra in those wavelengths. (3) Snow as part of a fractional mixture in a pixel with soils sometimes produces "bright SWIR" pixels that look like clouds. Improvement in snow-cloud discrimination in these cases will require more information than just the spectrum of the sensor's bands or will require deployment of a spaceborne imaging spectrometer, which could discriminate between snow and cloud under conditions where a multispectral sensor might not.
(© 2019. The Authors.)
Databáze: MEDLINE
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