AMSR2 snow depth downscaling algorithm based on a multifactor approach over the Tibetan Plateau, China

Wang, Yunlong; Huang, Xiaodong; Wang, Jianshun; Zhou, Minqiang; Liang, Tiangang

doi:10.1016/j.rse.2019.111268

Cited by 42 publications

(32 citation statements)

References 62 publications

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“…The daily snow depths at 25 km resolution use an algorithm that combines brightness temperature data from passive microwave sensors (SMMR, SSM/I, and AMSR‐E) based on a modified Chang's algorithm (Chang et al., 1987). Results suggest that this product is better than other snow depth datasets on the Tibetan Plateau (Y. Wang et al., 2019).…”

Section: Methodsmentioning

confidence: 82%

Assessment of Temperature Changes on the Tibetan Plateau During 1980–2018

Peng

Frauenfeld

Jin

et al. 2021

Earth and Space Science

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Section: Methodsmentioning

confidence: 82%

Assessment of Temperature Changes on the Tibetan Plateau During 1980–2018

Peng

Frauenfeld

Jin

et al. 2021

Earth and Space Science

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“…However, previous studies have shown that these two algorithms perform worse in the Tibetan Plateau because of snow heterogeneity [24,25]. According to statistics, the average snow depth of the ground observations is around 5 cm so it is difficult to monitor the snow cover accurately by using remote sensing because the snow is shallow in the Tibetan Plateau [53]. The strong solar radiation, wind-blown snow, and the rugged terrain are the main reasons causing the snow to be patchy [10,35].…”

Section: Discussionmentioning

confidence: 99%

MODIS Fractional Snow Cover Mapping Using Machine Learning Technology in a Mountainous Area

Liu

Huang

et al. 2020

Remote Sensing

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To improve the poor accuracy of the MODIS (Moderate Resolution Imaging Spectroradiometer) daily fractional snow cover product over the complex terrain of the Tibetan Plateau (RMSE = 0.30), unmanned aerial vehicle and machine learning technologies are employed to map the fractional snow cover based on MODIS over this terrain. Three machine learning models, including random forest, support vector machine, and back-propagation artificial neural network models, are trained and compared in this study. The results indicate that compared with the MODIS daily fractional snow cover product, the introduction of a highly accurate snow map acquired by unmanned aerial vehicles as a reference into machine learning models can significantly improve the MODIS fractional snow cover mapping accuracy. The random forest model shows the best accuracy among the three machine learning models, with an RMSE (root-mean-square error) of 0.23, especially over forestland and shrubland, with RMSEs of 0.13 and 0.18, respectively. Although the accuracy of the support vector machine and back-propagation artificial neural network models are worse over forestland and shrubland, their average errors are still better than that of MOD10A1. Different fractional snow cover gradients also affect the accuracy of the machine learning algorithms. Nevertheless, the random forest model remains stable in different fractional snow cover gradients and is, therefore, the best machine learning algorithm for MODIS fractional snow cover mapping in Tibetan Plateau areas with complex terrain and severely fragmented snow cover.

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“…However the computational cost related to complex atmospheric physics schemes limits the production of a product with a long time series for the entire Northern Hemisphere [40]. The statistical downscaling snow depth is also appropriate only for specific small areas [25]. Previous studies have demonstrated the potential for using multiple snow depth products ensembles to improve the accuracy of snow depth datasets [8,10] and constrain uncertainty [9,11].…”

Section: Introductionmentioning

confidence: 99%

Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere

Che

Dai

et al. 2021

Remote Sensing

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In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth datasets and in situ observations. A total of 29,565 in situ observations were used to train and optimize the machine learning algorithm. A total of five gridded snow depth datasets—Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth, Global Snow Monitoring for Climate Research (GlobSnow) snow depth, Long time series of daily snow depth over the Northern Hemisphere (NHSD) snow depth, ERA-Interim snow depth and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) snow depth—were used as input variables. The first three snow depth datasets are retrieved from passive microwave brightness temperature or assimilation with in situ observations, while the last two are snow depth datasets obtained from meteorological reanalysis data with a land surface model and data assimilation system. Then, three machine learning methods, i.e., Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), were used to produce a fused snow depth dataset from 2002 to 2004. The RFR model performed best and was thus used to produce a new snow depth product from the fusion of the five snow depth datasets and auxiliary data over the Northern Hemisphere from 2002 to 2011. The fused snow-depth product was verified at five well-known snow observation sites. The R2 of Sodankylä, Old Aspen, and Reynolds Mountains East were 0.88, 0.69, and 0.63, respectively. At the Swamp Angel Study Plot and Weissfluhjoch observation sites, which have an average snow depth exceeding 200 cm, the fused snow depth did not perform well. The spatial patterns of the average snow depth were analyzed seasonally, and the average snow depths of autumn, winter, and spring were 5.7, 25.8, and 21.5 cm, respectively. In the future, random forest regression will be used to produce a long time series of a fused snow depth dataset over the Northern Hemisphere or other specific regions.

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AMSR2 snow depth downscaling algorithm based on a multifactor approach over the Tibetan Plateau, China

Cited by 42 publications

References 62 publications

Assessment of Temperature Changes on the Tibetan Plateau During 1980–2018

Assessment of Temperature Changes on the Tibetan Plateau During 1980–2018

MODIS Fractional Snow Cover Mapping Using Machine Learning Technology in a Mountainous Area

Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere

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