Toward Daily Snow Depth Estimation on Arctic Sea Ice During the Whole Winter Season From Passive Microwave Radiometer Data

• Published in: IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-15
• Main Authors: He, Lian, Xue, Binghua, Hui, Fengming, Xu, Shiming, Chen, Zhuoqi, Cheng, Xiao
• Format: Article
• Language: English
• Subjects: Advanced Microwave Scanning Radiometer 2 (AMSR2); Algorithms; Arctic; Arctic sea ice; Brightness temperature; Correlation coefficient; Correlation coefficients; Density; Depth; Freeboard; gradient ratio (GR); Ice; Microwave emission; Microwave FET integrated circuits; Microwave integrated circuits; Microwave radiometers; Microwave radiometry; Predation; Radiometers; Regression analysis; Regression models; Root-mean-square errors; Sea ice; Sea measurements; Seasonal variation; Seasonal variations; Seasons; Snow; Snow accumulation; Snow density; Snow depth; Snowpack; Surface radiation temperature; Vertical polarization; Winter
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2024.3358340

The gradient ratios (GRs), defined as the normalized difference between measured vertically (V) or horizontally (H) polarized brightness temperatures (TBs) at two frequencies, have been commonly used to retrieve snow depth on Arctic sea ice from passive microwave (PMW) radiometer data. In this study, the influences of snow density on the relationship between GR of 6.9 and 18.7 GHz vertically polarized TBs [i.e., GRV(19/7)] and snow depth were investigated through observed data and simulation. The former was based on regression analysis between GRV(19/7) observations from Advanced Microwave Scanning Radiometer 2 (AMSR2) and the altimetric snow depth (ASD) estimates derived by differencing freeboard heights from ICESat-2 and CryoSat-2 while the latter was based on model simulations from the Microwave Emission Model for Layered Snowpacks (MEMLS). An improved snow depth retrieval algorithm is proposed based on a multilinear regression (MR) model with GRV(19/7) from AMSR2 and snow density from the NASA Eulerian Snow On Sea Ice Model (NESOSIM) as predictors and then validated using three airborne snow depth datasets. The validation results show an overall good accuracy of the improved algorithm, with the correlation coefficient ( {r} ) ranging from 0.60 to 0.72 and the root-mean-square error (RMSE) values varying between 6.18 and 7.53 cm. The improved algorithm enables daily snow depth estimation on sea ice over the entire Arctic Ocean during the full winter season (October to April). More importantly, it successfully captures the seasonal variation of snow depth, which is expected to increase throughout the winter season due to snow accumulation.