Introduction 

Snow has a crucial contribution to Earth’s climate and helps to maintain the Earth’s temperature. When snow melts, it aids in providing water to people for their livelihood and affects the survival of animals and plants (National Snow and Ice Data Center). Approximately 1.2 billion people - constituting one-sixth of the global population - depend on snowmelt water for both agricultural activities and human consumption (Barnett et al., 2005). Glacier melt serves as a lifeline for drinking water, agriculture, and innovative water storage solutions (National Snow and Ice Data Center). Furthermore, sea ice plays a crucial role in shaping global climate patterns and ocean circulation and covers up to 7% of the Earth’s surface (Thomas and Dieckmann, 2008). It serves as a habitat for diverse marine organisms and sustains livelihoods of communities dependent on fisheries. River ice is an important element of the cryosphere, particularly in the Northern Hemisphere, where it covers approximately 29% of total river length. The duration and breakup of river ice play a crucial role in the timing and intensity of extreme hydrological events including low flows and floods (World Meteorological Organization).

Monitoring snow and ice is important for understanding various spatial snowpack properties, including snow water equivalent (SWE), snowmelt timing, snowmelt volume, and avalanche hazard. These parameters offer insights into Earth's climate system and its impacts on ecosystems and human societies. Through the analysis of these, scientists can better understand regional climate variations and make predictions about long-term climate trends. This can be achieved by utilizing remote sensing technologies such as LiDAR, optical sensors, and Synthetic Aperture Radar (SAR), which provide data about snow and ice cover on the Earth's surface. 

Remote sensing techniques: LiDAR, optical sensor, and SAR 

In this section, we explore the functionalities and applications of three sensor instruments: LiDAR, optical sensors, and synthetic-aperture radar (SAR).  

• LIDAR, which stands for Light Detection and Ranging, is a laser altimeter system that calculates distances by measuring the time it takes for pulses of light to travel. The information obtained from LIDAR systems offers distinctive insights into the vertical composition of land covers (Fouladinejad et al., 2019).  
• Optical remote sensing, on the other hand, uses visible, near-infrared and short-wave infrared sensors. This technology captures images of the Earth's surface by detecting the solar radiation reflected from targets on the ground (CRISP National University of Singapore, 2001).  
• Synthetic-aperture radar (SAR) is a radar technology utilized for generating two-dimensional images or three-dimensional reconstructions of various objects, including landscapes (Kirscht and Rinke, 1998). While optical/multispectral sensors are susceptible to cloud cover and darkness, which can hinder their effectiveness in monitoring snow cover, spaceborne synthetic aperture radar (SAR) data provides a useful alternative as it is not affected by these conditions (König et al., 2001). SAR data can be collected both day and night and is not reliant on illumination or cloud cover thereby making it a valuable tool for monitoring snow cover extent. Figure 1 illustrates the different Sensor Instruments - LiDAR, SAR, and Optical in observing snow and ice. 

Table 1: Differences Between LiDAR, Optical Sensor, and SAR (Tsai et al., 2019) (Space Based Infrastructure Monitoring) (National Ecological Observatory Network) (Tshabalala et al., 2020) (WMO Oscar)

Missions 

Various missions have utilized these sensor instruments. The LiDAR instrument, Advanced Topographic Laser Altimeter System (ATLAS), on board Ice, Cloud and Land Elevation Satellite (ICESat-2) provides a spatial resolution of 66 m horizontally and 10 m vertically. A future mission satellite called Multi-footprint Observation LiDAR and Imager on the ISS (ISS MOLI) will have MOLI LiDAR aboard, offering a horizontal spatial resolution of 5 m and a revisit time of approximately one year (WMO Oscar, 2024). Optical/multispectral sensors like Operational Land Imager (OLI) and Thermal Infra-Red Sensor (TIRS) onboard Landsat-8 offer spatial resolutions of 15-30 m and 100 m respectively. While, OLI has a higher spatial resolution, its temporal resolution is 16 days in daylight which is more than TIRS (8 days). Moderate-resolution Imaging Spectro-radiometer (MODIS), which is a successor of Visible/Infrared Imager Radiometer Suite (VIIRS), provides a spatial resolution of 250 to 500 m (WMO Oscar, 2024). SAR offers higher spatial resolution in comparison to optical/multispectral sensors which means they can capture detailed images of a particular area. SAR-C instrument aboard Sentinel-1 satellite has a spatial resolution of 4 x 5 m2 while PALSAR-2 offers a spatial resolution between 3 to 10 m in stripmap mode (WMO Oscar, 2024). Figure 2 displays a comparison between Synthetic Aperture Radar (SAR) and Optical Satellites.

Research Conducted 

Numerous scholars (Deems et al., 2013; Elder et al., 1991; Luce et al., 1998; Miller et al., 2003; Hopkinson et al., 2004; etc) have conducted research to explore the capabilities of different remote sensing technologies in monitoring snow and ice characteristics. LiDAR technology has been studied for its applications in snow observation. A study by Deems et al. (2013) called "LiDAR measurement of snow depth: a review" highlights the importance of understanding spatial snowpack properties, such as snow water equivalent (SWE), for accurate assessment of snowmelt timing (Luce et al., 1998), snowmelt volume (Elder et al., 1991), and avalanche hazard (Conway and Abrahamson, 1984). By utilizing LiDAR data collected during snow-free and snow-covered periods, researchers can map snow depth with sub-decimeter precision (Miller et al., 2003; Hopkinson et al., 2004; Deems et al., 2006). Another study conducted by Kirchner et al., 2014 in the southern Sierra Nevada, California () utilized LiDAR to reveal the snow accumulation patterns across different elevations which is crucial for understanding how water moves in and through mountainous areas and for predicting snowmelt and water runoff. 

Optical remote sensing, particularly through sensors like the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), has been utilized to study  glacier characteristics. A study by Racoviteanu et al., (2008) called "Optical Remote Sensing of Glacier Characteristics: A Review with Focus on the Himalaya"  emphasizes the advantages of optical remote sensing in estimating glacier parameters such as ice extent, volume, and surface elevation in a fast, semi-automated, and cost-effective manner over large areas. It highlighted the importance of continuous optical sensor monitoring in poorly surveyed regions, such as the Himalayas to help our understanding of glacier changes and mass balance. 

Synthetic Aperture Radar (SAR) has been researched for its effectiveness in monitoring snow cover extent (SCE). The study titled "Remote Sensing of Snow Cover Using Spaceborne SAR: A Review" focuses on SAR's ability to overcome limitations posed by cloud cover and polar darkness, and its capability to distinguish between wet and dry snow cover by continuously tracking of snow cover (Tsai et al., 2019).  

SAR data from satellites, such as by Sentinel-1 is utilized to monitor river ice dynamics by detecting and classifying various ice formations. SAR's sensitivity to the physical structure of river ice allows for differentiation between ice types. This helps in early detection and tracking of ice accumulation and jamming which is crucial for flood risk assessment and mitigation (United Nations Office for Disaster Risk Reduction). 

Figure 3 illustrates different snow types and the snow line in deep blue font, highlights the significance of snow in black font, presents characteristics related to synthetic aperture radar (SAR) in italics, indicates factors influencing snow in green font, and highlights parameters of the snowpack in red font. 

Table 2: Advantages and disadvantages of the remote sensing techniques, the properties of snow monitored by them, and the models that can be used to derive these properties. 

Conclusion 

Remote sensing techniques, including LiDAR, optical/multispectral sensors, and Synthetic Aperture Radar (SAR), play an important role in observing and studying snow and ice by providing valuable insights into environmental, climate, and ecosystem dynamics. LiDAR's high precision in snow depth measurement, optical sensors' ability to capture detailed images, and SAR's all-weather capabilities contribute to comprehensive monitoring. However, there are limitations despite the trend towards free and open access to remote sensing data, there are still restrictions to accessing and analyzing the data (Pope et al., 2014). The future potential of remote sensing of snow and ice lies in fostering open science practices and integrating local knowledge to enhance the reliability and applicability of remote sensing (Huggel et al., 2020).