Passive Microwave Remote Sensing of Snow Properties

Passive microwave remote sensing exploits the natural thermal emission of the Earth’s surface at microwave frequencies to infer key characteristics of snowpacks, such as depth, water equivalent and grain size. Brightness temperatures measured by spaceborne radiometers at multiple frequencies are sensitive to microwave scattering and absorption by snow layers, providing all-weather, day-night observations over broad areas. Retrieval algorithms range from empirical regression models to physically based radiative transfer schemes, often incorporating ancillary information on land surface temperature, ground emissivity and snow microstructure. These approaches enable regular monitoring of snow water equivalent at continental and global scales, informing hydrological forecasting, climate modelling and water resource management. Challenges persist in accounting for complex snow metamorphism, sensor inter-calibration, coarse spatial resolution and heterogeneity in surface emissivity, especially over mountainous or patchy snow cover. Ongoing research seeks to refine emission models, integrate machine learning methods and combine passive microwave data with optical and in-situ observations to improve accuracy and spatial detail.

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Passive Microwave Remote Sensing of Snow Properties publication trend

The graph below shows the total number of articles in passive microwave remote sensing of snow properties across all publications each year (not limited to Nature Index journals).

Technical terms

Brightness temperature: The apparent radiometric temperature of a surface measured by a microwave radiometer, reflecting emission and scattering within the snowpack.

Snow water equivalent (SWE): The depth of water produced by melting a unit depth of snow, indicating the total liquid water content of the snowpack.

Emissivity: The efficiency with which a surface emits microwave radiation relative to a blackbody, influenced by moisture, temperature and surface roughness.

Data assimilation: A statistical technique that merges observational data with model outputs to produce a more accurate estimate of a state variable, such as SWE.

References

1. Machine Learning-Based Estimation of High-Resolution Snow Depth in Alaska Using Passive Microwave Remote Sensing Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2023).
2. GlobSnow v3.0 Northern Hemisphere snow water equivalent dataset. Scientific Data (2021).
3. Estimation of Snow Depth over the Qinghai-Tibetan Plateau Based on AMSR-E and MODIS Data. Remote Sensing (2018).