Combination of snow process model priors and site representativeness evaluation to improve the global snow depth retrieval based on passive microwaves

The spatiotemporal distribution of snow depth (SD) has a significant impact on the energy and water balances of the Earth's system. However, passive microwave remote sensing widely used for SD estimation has large uncertainties due to the variations in snow physical properties. In this study, w...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2023-01, Vol.61, p.1-1
Main Authors: Pan, Jinmei, Yang, Jianwei, Jiang, Lingmei, Xiong, Chuan, Pan, Fangbo, Gao, Xiaowen, Shi, Jiancheng, Chang, Sheng
Format: Article
Language:English
Subjects:
Algorithms
Brightness temperature
Grain size
Machine learning
Methods
Microbalances
Microwave measurement
Microwave radiation
Microwave theory and techniques
Microwaves
Mountains
Particle measurements
Passive microwave remote sensing
Physical properties
Pollution measurement
Remote sensing
Root-mean-square errors
Scattering
Snow
Snow density
Snow depth
snow process model
Snow-water equivalent
Spatial distribution
Surface radiation temperature
Temperature measurement
Temporal distribution
Thermal analysis
Uncertainty
Water depth
Weather stations
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Summary:The spatiotemporal distribution of snow depth (SD) has a significant impact on the energy and water balances of the Earth's system. However, passive microwave remote sensing widely used for SD estimation has large uncertainties due to the variations in snow physical properties. In this study, we demonstrate a new method to minimize these uncertainties and to increase the accuracy of SD estimation. Our method is based on the synergy between the passive microwave AMSR-2 brightness temperature (TB) and a physical snow process model (SNTHERM) to estimate snow grain size, snow density and first-guess SD as priors. On one hand, we used TB from three frequencies and removed non-representative ground measurements at the stations to improve deep snow estimation. Then, we applied a machine learning (ML) algorithm based on both the AMSR-2 TB and the SNTHERM simulations to retrieve the global SD. The results showed that the root-mean-squared error (RMSE) of the retrieved SD was 12.4 cm at the meteorological stations. Independent validations showed that our method significantly reduced the SD and snow water equivalent (SWE) underestimation in the mountains compared to the current satellite products.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2023.3276651