Abstract
Abstract. The dynamic characteristics of seasonal snow cover are critical for hydrology management, the climate system, and the ecosystem functions. Optical satellite remote sensing has proven to be an effective tool for monitoring global and regional variations in snow cover. However, accurately capturing the characteristics of snow dynamics at a finer spatiotemporal resolution continues to be problematic as observations from optical satellite sensors are greatly impacted by clouds and solar illumination. Traditional methods of mapping snow cover from passive microwave data only provide binary information at a spatial resolution of 25 km. This innovative study applies the random forest regression technique to enhanced-resolution passive microwave brightness temperature data (6.25 km) to estimate fractional snow cover over North America in winter months (January and February). Many influential factors, including land cover, topography, and location information, were incorporated into the retrieval models. Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products between 2008 and 2017 were used to create the reference fractional snow cover data as the “true” observations in this study. Although overestimating and underestimating around two extreme values of fractional snow cover, the proposed retrieval algorithm outperformed the other three approaches (linear regression, artificial neural networks, and multivariate adaptive regression splines) using independent test data for all land cover classes with higher accuracy and no out-of-range estimated values. The method enabled the evaluation of the estimated fractional snow cover using independent datasets, in which the root mean square error of evaluation results ranged from 0.189 to 0.221. The snow cover detection capability of the proposed algorithm was validated using meteorological station observations with more than 310 000 records. We found that binary snow cover obtained from the estimated fractional snow cover was in good agreement with ground measurements (kappa: 0.67). There was significant improvement in the accuracy of snow cover identification using our algorithm; the overall accuracy increased by 18 % (from 0.71 to 0.84), and the omission error was reduced by 71 % (from 0.48 to 0.14) when the threshold of fractional snow cover was 0.3. The experimental results show that passive microwave brightness temperature data may potentially be used to estimate fractional snow cover directly in that this retrieval strategy offers a competitive advantage in snow cover detection.
Highlights
Snow cover is a critical indicator of climate change, playing a vital role in the global energy budget (Flanner et al, 2011), water cycle (Gao et al, 2019), and atmospheric circulation (Henderson et al, 2018)
We evaluated the results from 24 random forest fractional snow cover retrieval models to better understand which input variables have a good relationship with fractional snow cover and the combination of the variables that can improve retrieval model performance
The data used for variable sensitivity tests in this part spanned only 2 years (2014–2015) as the 91 GHz H polarization data were missing over the area south of 50◦ N for 2016– 2017
Summary
Snow cover is a critical indicator of climate change, playing a vital role in the global energy budget (Flanner et al, 2011), water cycle (Gao et al, 2019), and atmospheric circulation (Henderson et al, 2018). Snow cover directly modulates the release of carbon and methane from underlying soil (Zhang, 2005; Zona et al, 2016) and influences permafrost conditions and active layer dynamics (Zona et al, 2016). X. Xiao et al.: Estimating fractional snow cover from passive microwave data climate predictions, and water resources management (Barnett et al, 2005; Bormann et al, 2018)
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