The paper reviews the status of glacier mapping with special reference to the Indian Himalaya. The review provides information on various satellite remote sensing data interpretation methods used with special emphasis laid on recent semi-automated algorithms used for glacier and debris-cover mapping, along with their limitations and challenges. Further, the pragmatic solutions on offer are discussed, and the emerging areas of glacier mapping research are highlighted. The review also touches – contribution of Survey of India (SOI) and Geological Survey of India (GSI) in the glacier mapping. Finally, it discusses the wider range of spatial and spectral domains in which remote sensing data helps to inventories glaciers. The review also identifies gaps in using the latest techniques like Unmanned Aerial Vehicles (UAVs) and machine learning algorithms to improvise on the ongoing efforts. At last, the review provides an exhaustive list of references on glacier mapping from the Indian Himalaya as benefit to readers.

This paper reports changes in different glaciers of Bhaga basin located in western Himalaya, from 1979 to 2017. Glacier boundaries were delineated through semi-automated approach using Landsat satellite imagery. The variation of glacier extent in different elevation zones, snout retreat and decadal changes are observed. Results show that the total area of glaciers was 238 ${\text{ km}}^2$ in 1979, which reduced to 230.8 ${\text{ km}}^2$ by 2017 (retreat rate 12 $\mathrm{m}{\text{\hspace{0.17em}yr}}^{-1}$). Glaciers at low elevation and smaller in size are retreating faster. Analysis of Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation data shows decreasing trend of annual precipitation (–2.724 $\text{ mm}{\text{\hspace{0.17em}yr}}^{-1}$, 1951–2015 ) and increasing trend of mean annual temperature (0.021 $°\mathrm{ }\mathrm{C}{\text{\hspace{0.17em}yr}}^{-1}$). The statistical analysis using Mann-Kendall and Sen’s slope tests, applied at different confidence interval demonstrates that climate change corresponds to deglaciation, and topography controls glacier recession in the basin.

Snow and glacier melt provide freshwater to millions of people in the Indus basin. However, the unprecedented increase in demand for freshwater and depleting resources due to climate warming has put the region's water resources at risk. Therefore, quantifying water mass variation and anticipating changes in hydrological regimes that affect downstream freshwater supply are of utmost importance. To address this, we used Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On derived terrestrial water storage anomaly (TWSA) data from April 2002 to May 2023 over the Indus basin. Several gaps in these data, totaling 33 months, significantly impact regional trends and predictions of water mass changes. We apply a machine learning‐based MissForest algorithm to fill these gaps and compare our results with four previous studies. Annual TWSA shows a declining trend (−0.65 cm/yr) before 2015/16, with a significantly higher (−2.16 cm/yr) after 2015/16. Based on the estimate for the annual groundwater storage anomaly (GWSA), a major portion (83.7%) of the basin is experiencing a significant declining trend (>−0.15 cm/yr, p < 0.05). Glaciated region has a less severe decreasing trend (−0.78 cm/yr) compared to the non‐glaciated region (−1.44 cm/yr). Among sub‐basins, the upper Indus shows the lowest decline (−0.42 cm/yr), while Panjnad exhibits the highest (−1.70 cm/yr). Annual precipitation and runoff are decreasing, while temperature shows no trend. However, evapotranspiration is increasing might be due to a significant increase in vegetation (0.23%/yr) over the basin. The trends of hydroclimatic variables, vegetation, and anthropogenic factors, indicate a consistently decreasing GWSA in the region.

High-mountain glaciers can be covered with varying degrees of debris. Debris over glaciers (supraglacial debris) significantly alter glacier melt, velocity, ice geometry, and, thus, the overall response of glaciers towards climate change. The accumulated supraglacial debris impedes the automated delineation of glacier extent owing to its similar reflectance properties with surrounding periglacial debris (debris aside the glaciated area). Here, we propose an automated scheme for supraglacial debris mapping using a synergistic approach of deep learning and multisource remote sensing data. A combination of multisource remote sensing data (visible, near-infrared, shortwave infrared, thermal infrared, microwave, elevation, and surface slope) is used as input to a fully connected feed-forward deep neural network (i.e., deep artificial neural network). The presented deep neural network is designed by choosing the optimum number and size of hidden layers using the hit and trial method. The deep neural network is trained over eight sites spread across the Himalayas and tested over three sites in the Karakoram region. Our results show 96.3% accuracy of the model over test data. The robustness of the proposed scheme is tested over 900 km² and 1710 km² of glacierized regions, representing a high degree of landscape heterogeneity. The study provides proof of the concept that deep neural networks can potentially automate the debris-covered glacier mapping using multisource remote sensing data.