Snow Cover Monitoring and Assessment using Remote Sensing Techniques

Satellite remote sensing has revolutionised snow cover monitoring, offering consistent, near‐global observations of spatial extent, duration and fractional snow cover. Optical sensors such as MODIS, Sentinel-2 and Landsat series employ spectral indices, notably the Normalised Difference Snow Index, to discriminate snow from other surfaces. Passive microwave instruments provide complementary daily information on snowpack properties under cloudy and polar conditions, enabling retrievals of snow water equivalent and depth. Advanced algorithms address common issues such as cloud occlusion by implementing multistep gap-filling and spatiotemporal interpolation approaches, while high-resolution operational products integrate multi-sensor data to refine snow distribution maps. These datasets underpin climate monitoring, hydrological modelling and water resource management by informing snowmelt run-off forecasts, assessing seasonal water availability and analysing cryosphere–climate feedbacks. Integration of remote sensing with ground observations and numerical models enhances accuracy and supports diverse applications from alpine hydropower planning to global circulation studies.

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Snow Cover Monitoring and Assessment using Remote Sensing Techniques publication trend

The graph below shows the total number of articles in snow cover monitoring and assessment using remote sensing techniques across all publications each year (not limited to Nature Index journals).

Technical terms

Fractional snow cover: Proportion of snow-covered area within a sensor pixel, expressed as a fraction or percentage, reflecting subpixel variability.

Normalised Difference Snow Index (NDSI): Spectral index calculated from green and shortwave infrared reflectances to distinguish snow from other surfaces based on its unique spectral signature.

Passive microwave remote sensing: Technique using naturally emitted microwave radiation to detect snow properties such as water equivalent and depth, relatively unaffected by cloud and darkness.

Spatiotemporal interpolation: Algorithmic method to fill data gaps by leveraging spatial neighbourhoods and temporal continuity to estimate missing observations.

Quality assurance (QA) data array: Ancillary information in remote sensing products indicating processing flags, sensor status or environmental conditions affecting retrieval confidence.

References

1. MODIS daily cloud-gap-filled fractional snow cover dataset of the Asian Water Tower region (2000–2022). Earth System Science Data (2024).
2. Theia Snow collection: high-resolution operational snow cover maps from Sentinel-2 and Landsat-8 data. Earth System Science Data (2019).
3. Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records. Earth System Science Data (2017).