ABSTRACT. A new surface classification algorithm for monitoring snow and ice masses based on data from the moderate-resolution imaging spectroradiometer (MODIS) is presented. The algorithm is applied to the Greenland ice sheet for the period 2000–05 and exploits the spectral variability of ice and s...
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| Format: | Text |
| Language: | English |
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| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.634.6758 http://www.igsoc.org/annals/46/a46a219.pdf |
| Summary: | ABSTRACT. A new surface classification algorithm for monitoring snow and ice masses based on data from the moderate-resolution imaging spectroradiometer (MODIS) is presented. The algorithm is applied to the Greenland ice sheet for the period 2000–05 and exploits the spectral variability of ice and snow reflectance to determine the surface classes dry snow, wet snow and glacier ice. The result is a monthly glacier surface type (GST) product on a 1 km resolution grid. The GST product is based on a grouped criteria technique with spectral thresholds and normalized indices for the classification on a pixel-by-pixel basis. The GST shows the changing surface classes, revealing the impact of climate variations on the Greenland ice sheet over time. The area of wet snow and glacier ice is combined into the glacier melt area (GMA) product. The GMA is analyzed in relation to the different surface classes in the GST product. The results are validated with data from weather stations and similar types of satellite-derived products. The validation shows that the automated algorithm successfully distinguishes between the different surface types, implying that the product is a promising indicator of climate change impact on the Greenland ice sheet. |
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