On October 4, 2023, a catastrophic Glacial Lake Outburst Flood (GLOF) originated from South Lhonak Lake (SLL) during a period of heavy rainfall in the Teesta basin, Sikkim Himalaya, resulting in widespread devastation and significant loss of lives. Therefore, this study intended to conduct the reconstruction of this flood event through an integrative approach, combining rainfall-generated runoff simulation and GLOF modelling using the Hydrologic Engineering Center - Hydrologic Modeling System (HEC-HMS) and Hydrologic Engineering Center - River Analysis System (HEC-RAS), respectively. The peak discharge estimated at Singtam from the HEC-HMS simulation, reflecting solely the rainfall effect, was 2409 m3/s. In this contrast, the HEC-RAS modelling, which encompasses both rainfalls-induced runoff and GLOF from SLL, reveals a peak discharge of 6562.95 m3/s. The results were optimised and validated by the flood parameter information of some active gauge sites of Central Water Commission (CWC) falls on the flood path. In summary, such studies improve the understanding by estimating and analysing the quantitative parameters of these events that occur during intense precipitation and sudden breaches of glacial lakes, coincidingly, which often occur in high mountain regions.

Indian Space Research Organization (ISRO) launched its all-weather satellite on September 26, 2016 named as Scatterometer Satellite (SCATSAT-1). It is single band satellite operating at 13.5 GHz frequency. SCATSAT-1 is having the wide application range like ocean exploration, land hydrology, vegetation and cryosphere applications. Many fields like forecasting of weather, early prediction of the cyclones, snow hydrology studies, variations in sea-ice, Crop monitoring etc have become easy to investigate with the help of SCATSAT-1. SCATSAT-1 have advantages over other satellites like day and night operational, cloud penetration, global coverage, daily data availability, and extensive application area. In this work the Uttarakhand area has been explored. The SCATSAT-1 level-4 (L4) product and Moderate Resolution Imaging Spectroradiometer (MODIS) data have been considered for application of Super Resolution Mapping technique (SRM) to generate enhanced resolution results. The results of this study comply with enhancing the accuracy of classified maps using fusion technique as compared to any individual technique itself. The results have been validated using a reference dataset. This study offers cloud-free remote monitoring in all weather conditions using low resolution (LR) SCATSAT-1 data. Such exploration can be utilised in numerous areas like natural disasters, early snow-melt detection, and flood prediction.

As an active microwave remote sensing sensor, Scatterometer measures the earth’s surface in the form of a radar cross-section. In the past few decades, several scatterometers were launched into space and found numerous applications in earth observations. The scatterometer satellite (SCATSAT-1) is one of the ISRO’s (Indian Space Research Organization) scatterometer based on Ku-band (13.5 GHz) which recently completed its mission in 2021. It was launched on 26th September 2016 from the Satish Dhawan Space Centre, Sriharikota, India. Due to the measurements over the land surface and non-land surfaces, the major scientific domains of scatterometer application may include oceanography, hydrology, cryosphere, and agriculture. This article highlights the major milestones of the SCATSAT-1 in the field cryosphere which were achieved in nearly five years of its operation. Due to its measurement over the land surface, snow cover monitoring over the Himalayan region is one of the major applications of SCATSAT-1. It also took measurements over the wide coverage of polar regions which plays an important role in the monitoring of sea-ice extent or tracking large icebergs and hence, the particular interest for climate change research communities. This article also highlights the potential applications of recently launched (26thNovember 2022) oceansat-3 (a successor of OCSCAT and SCATSAT-1) in the field of the cryosphere.

The Scatterometer Satellite (SCATSAT-1), launched by ISRO has been utilized in many remote sensing applications to deliver near-real-time monitoring services. To represent the SCATSAT-1 applicability, the major scientific domains include hydrological studies, cryospheric applications, understanding the oceanic dynamics and agriculture applications. SCATSAT-1 offers various levels of data products with the best spatial resolution of ~2 km (Level-4), enhanced via the Scatterometer image reconstruction (SIR) technique. For the same reason, it may restrict the applicability of SCATSAT-1 in extracting different land-cover types. In this work, super-resolution mapping (SRM) has been utilized to estimate and improve the spatial distribution of land cover classes at the sub-pixel level. To achieve the SRM, the linear spectral mixing (LSM) model has been utilized in snow-cover mapping using SCATSAT-1 and MOD02 (MODIS) based normalized difference snow index (NDSI) dataset. This study has been conducted over a part of the rugged terrain Himalayan region (Himachal Pradesh State, India). Results show that satisfactory thematic maps have been generated via the SRM approach. The use of SRM is, therefore, a promising method to improve the spatial representation of the SCATSAT-1 dataset which may enhance the scope of ISRO’s SCATSAT-1 not only in the cryosphere but also in other scientific domains.

Image-based fusion is a state-of-the-art process to extract vital information by combining the two or more images acquired from different satellite sensors. Recently launched (September 26, 2016) Indian Space Research Organization’s (ISRO) Ku-band (13.5 GHz)-based Scatterometer Satellite (SCATSAT-1) as an active microwave sensor can offer the day–night, all-weather monitoring services, which are not possible with the optical-based visible and infrared remote sensing satellites. Therefore, the fusion of optical and microwave data offers the cloud-free detection of Earth surface transitions and helps in emergency response to natural hazards, security, and defense. The objectives of the proposed framework are: 1) nearest neighbor-based fusion (NNF) of ISRO’s SCATSAT-1 and National Aeronautics and Space Administration’s (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) optical data; 2) generation of thematic maps using artificial neural network (ANN)-based classification of the fused data; 3) detection of spatiotemporal variations via postclassification comparison (PCC)-based change detection; 4) cross-referencing with well-defined fusion methods, i.e., Gram–Schmidt (GS), Brovey transformation (BT), and Ehlers; and 5) impact analysis of clouds on the input dataset and fusion methods. This study has been conducted over the Western Himalayas to estimate the snow cover changes under cloudy conditions with two datasets, i.e., winter and monsoon. The experimental outcomes confirm the efficacy of the proposed framework in the effective removal of clouds, generation of classified maps, and change maps. The present study includes an exhaustive list of applicative situations for cloud-free monitoring using freely and daily-based SCATSAT-1 and MODIS datasets.

The antarctic region is an essential component of Earth's climate system and heat balance. Therefore, continuous monitoring of various cryospheric parameters (Sea-Ice Extent - SIE, and Snow Water Equivalent - SWE, among others) over Antarctica is essential to understand the state of global climate processes. This study presents a framework to detect and analyse the SIE and SWE using enhanced resolution data products from ISRO’ s (Indian Space Research Organisation) Scatterometer Satellite (SCATSAT -1) level-4 (operating at Ku-band, 13.5 GHz) data products. This framework is based on Artificial Neural Network (ANN) and Post-Classification Comparison (PCC) to detect the multitemporal variations using SCATSAT-1 images (sigma-naught, gamma-naught, and brightness temperature). For validation purposes, the classified maps and change maps are compared with Advanced Microwave Scanning Radiometer (AMSR2) derived SIE and SWE polar gridded datasets. Statistical analysis has confirmed the effectiveness (more than 88.75% accuracy) of the proposed framework with SCATSAT-1. We conclude that the ANN-PCC is a powerful technique to estimate the spatiotemporal dynamics of surface melting and freezing over Antarctica.

The concerns about global warming and climate change have produced widespread scientific interest in the response of glaciers. One of the important indicators of climate change is glacier change. The glaciers in Indian Himalaya have been retreating which can have several effects. Therefore, there is a need of precise and timely mapping and monitoring of the glacier changes. The advent of high spatial resolution images that provide more detailed information has now allowed analyzing glacier changes at a local level. However, change detection using high spatial resolution images faces additional challenges due to, for example, small spurious changes, etc. Fortunately, these effects are reduced by using object-based approaches rather than pixel-based approaches. In this study, object-based approach has been explored to detect the glacier surface changes from high spatial resolution images of WorldView-2 and Linear Imaging Self-Scanning System (LISS) IV which has not been done till date in Indian Himalaya.

Debris cover over glaciers affects the rate of ablation and is considered as an indicator of glacier health. It also affects the ability to map glacier bodies and thereby has a bearing on accuracy of modeling used for climate prediction, runoff estimation, etc. Therefore, characterization and mapping of the debris over glacier is very important. This study deals with debris cover studies over Bara Shigri glacier in the Himalayas, using optical-thermal (ASTER) and radar (TerraSAR-X) data. It is observed that the supraglacial debris in the upper part of ablation area is characterized by a higher temperature and a higher radar backscatter. However, towards the terminus, the debris cover widens and becomes finer grained and muddy with variable water content and is studded with numerous small water bodies.