Satellite Data



WV-2 [60]

Study Area: Greenland

Automated “spectral-shape” procedure

Accuracy for detecting actively flowing supraglacial streams, particularly in slushy areas where classification performance dramatically improves (85.2% success) versus simple threshold methods (52.9% and 59.4% success for low and moderate thresholds, respectively).

Landsat TM [61]

Study Area: Weissmies Group, Switzerland



ISODATA clustering depicts that ice and snow in cast shadow are partly unmapped. For MXL, regions in the cast shadow without glacier ice and also the mixed pixels with ice/snow and terrain along the glacier outline are mapped as a glacier.


Study Area: Samudra Tapu glacier, Himachal

Supervised MXL

Overall accuracy of classifications obtained with the use of various band combinations has been found to range from 74.72% to 89.35%. The highest overall accuracy (i.e., 89.35%) resulted from a glacier terrain map derived from a band combination having two optical and two thermal bands―IB1, IB3, IB6 and IB8.

ERS-1 SAR [63]

Study Area: Place Glacier Basin, western Canada.

Supervised MXL

Kappa coefficients for two images are 0.49 ± 0.02 & 0.48 ± 0.02, respectively, at the 95% confidence level. This represents a classification accuracy of about 50% for each of the SAR images.

Landsat and ASTER [64]

Study Area: Central Alps,

Morphometric glacial mapping (MGM) method

A glacier mapping using a TM4/TM5-ratio image in combination with an MSI analysis to eliminate misclassified pixels was successfully applied to clean-ice glaciers. MGM was found to be capable to identify supraglacial debris.

Landsat and MODIS [65]

Study Area: Antarctica


The six-band ETM+ sensor discriminated surface features more sensitive than those of the two-band AVHRR and MODIS data, which discriminated Blue Ice Areas (BIAs) from exposed rock and snow. The higher spatial resolution and better spectral signatures of the ETM+ data improved BIA recognition.

Landsat and ASTER [66]

Study Area: Oberaletschgletscher, Swiss Alps


The overall accuracy was 0.64 with a kappa coefficient of 0.26, which is not satisfactory. In comparison with the independent vector debris layer. The overall accuracy is 0.75 and the kappa coefficient is 0.22. The performance of ANN classifier was not convincing.

Landsat and IRS LISS III

Study Area: Alam Chal glacier, Iran [67]

SIRs and K-means

The image of IRS_LISS could not be used for snow mapping due to the fact that its spectral bands were not appropriate for this application.


Study Area: Juneau Icefield, Alaska


Classification of the glacier surface is carried out with an overall accuracy of 93.72%.

WV-2 [58]

Study Area: Larsemann

Hills, East Antarctica


The land-cover map generated from using four CSIR combinations had a K value (0.98) significantly higher than for the land-cover map generated using one CSIR combination (0.92).

Landsat [69]

Study Area: Greater Himalaya Range

Supervised MXL

The overall accuracy of classification performed for the snow- and ice-covered parts of the glaciers was 86.29% with a Kappa coefficient of 0.84.

WV-2 [38]

Study Area: Larsemann

Hills, East Antarctica


The overall accuracy of the WTA method was 97.23% (96.47% for SVM classifier) with a 0.96 kappa coefficient (0.95 with the SVM classifier). The accuracy of the other classifiers was 93.73% to 95.55% with kappa coefficients of 0.91 to 0.93.