This study investigates the change in multiple glacier parameters of three valley-type glaciers (Sakchum (SC), Chhota Shigri (CS), and Bara Shigri (BS)) located in Chandra basin, Himachal Himalaya, sharing the same climatic regime, and assesses the control of nonclimatic factors on wholesome glacier response. Multitemporal satellite remote sensing data from Landsat-TM/ETM/OLI (1993-2014), and Terra-ASTER (2002-2014) along with an SRTM digital elevation model were used for extraction of the glacier parameters. Results show that while SC and BS retreated (SC: 10.65 ±2.52 m/y; BS: 15.51 ±2.52 m/y) and lost area (SC: 0.49 ±0.0032 km 2 , BS: 1.18 ±0.0032 km 2), the CS remained relatively stable (retreat rate: 4.06 ±2.52 m/y, area loss: 0.19 ±0.0032 km 2) during 1993-2014. However, results of surface ice velocities (SIV) change (SC: 24.41%, CS: 21.60%, and BS: 28.49%) and surface elevation change (SC:-1.22 m/y, CS:-0.91 m/y and BS:-1.21 m/y) suggest a comparable slowing down and surface lowering from 2002 to 2014. Debris cover also varied substantially (SC: 30.25%, CS: 11.96%, BS: 19.61%) on these glaciers. Results reveal that higher retreat/deglaciation of glaciers was associated with higher altitudinal range, slow SIV in lower ablation zones (LAZ), and glacier hypsometry. Debris cover on glaciers was found to be controlled by slope, higher deglaciation rates, higher SIV in the upper ablation zone (UAZ) coupled with lower SIV in LAZ and surface lowering. Glacier SIV was primarily governed by slope gradient, differential surface lowering, and size of accumulation zone (ACZ). The SIV results confirm the presence of stagnant zones in the lower ablations of SC (<1 km upstream) and BS (<6 km upstream) and a major SIV dropdown in the UAZ (SC: 34.48%, BS: 37.35%), indicating the expansion of stagnant zones farther up. Glacier surface lowering seemed to be influenced by ice-flux, changing spatial distribution of debris cover, presence of supraglacial lakes, and ice cliffs bordering them. Melting around supraglacial lakes and backwasting of ice cliffs may be the prime reasons behind intense mass loss observed in the stagnant zone of the Bara Shigri glacier, where surface lowering was ~32% higher than glacier-wide thinning. Differential surface lowering in glaciers reduced the surface gradient and hence the SIV. Also, rapid surface gradient changes and SIV dropdown in the Sakchum glacier indicate possible development of supraglacial lakes on it in the future. Interestingly, despite close proximity, SC, CS, and BS demonstrate three distinct ablation patterns, reemphasizing the role of nonclimatic factors. Overall, the study states that mutual 3 variation of glacier parameters together with nonclimatic factors exercise a great control on glacier response.

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The spectral characterization of geographic landscapes is vital for their accurate mapping using remote sensing data. This can be done through spectral profiling, as demonstrated here, to characterize the surface facies of the Gangotri and neighbouring glaciers, central Himalaya. The satellite-derived reflectance curves were compared with the in-situ and published (validation) data. The study attempts to understand the influence of certain parameters such as the satellite sensor’s radiometric resolution, timing of data acquisition (seasonality), and surface morphology on glacier/snow–ice facies identification. Results show that the first two parameters complement each other in identifying the snow–ice facies accurately. High radiometric resolution (HRR) data concurred closely with the validation dataset and had higher mean entropy values over the glaciated areas than low radiometric resolution (LRR) ablation data. Presence of seasonal snow and degree of surface melting show considerable influence on satellite-derived reflectances of glacier facies. Our findings assert the usage of HRR ablation data in appraising the interannual and seasonal variability of glacier facies. While HRR post-ablation data overestimates the reflectance of snow–ice facies, LRR post-ablation data have limitations in their discrimination. Certain morphology and resultant features, such as crevasses and shadows, induce underestimation of the satellite-derived reflectances, creating confusion among the snow and ice facies. This spectral confusion can, however, be resolved by the use of ancillary data. Elevation, temperature, and band ratios/spectral indices are helpful in segregating snow–ice facies, while slope, band ratios, temperature, and texture measures effectively discriminate the other facies.

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