Estimation of snow depth from passive microwave brightness temperature data in forest regions of northeast China

Che, Tao; Dai, Liyun; Zheng, Xingming; Li, Xiaofeng; Zhao, Kai

doi:10.1016/j.rse.2016.06.005

Cited by 116 publications

(116 citation statements)

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“…Although the soil temperature and the land type were the main causes of errors in the QTP, the instant snow could not be detected for the extremely low scattering of small grain size. Therefore, accurately monitoring the snow depth using passive microwave requires a priori knowledge of snow characteristics Che et al, 2016;Huang et al, 2012;Tedesco and Narvekar, 2010). In this study, 16 % of snow depths greater than 10 cm observed at meteorological stations were misclassified as snow-free grids by AMSR-E.…”

Section: Snow Characteristicsmentioning

confidence: 99%

“…The NASA snow water equivalent product derived from Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) generally tends to underestimate snow depth in North America (Tedesco and Narvekar, 2010) when compared with World Meteorological Organization (WMO) and Snow Data Assimilation System (SNODAS) but overestimate in northwest and northeast of China Che et al, 2016) when compared with meteorological station and field work observations. These authors pointed out that the errors primarily came from the spatiotemporal variability of grain size and forest cover.…”

mentioning

confidence: 99%

“…It was because of the strong influence of grain size that mass investigation of snow characteristics were performed to obtain the a priori information of snow characteristics in northwest and northeast China to improve the simulation of brightness temperature and retrieval accuracy of snow depth. Some research assimilated snow depth observed at stations or built a local empirical relationship between snow depth observations and spectral gradients to improve the snow depth retrieval accuracy in some regions Che et al, 2016Che et al, , 2008Pullianen, 2006). In order to reduce the influence of forest, forest transmissivities at different frequencies were absorbed in the algorithm or the special equations were built between snow depth and brightness temperature difference (TBD) at different types of forest to estimate snow depth more accurately at forest areas (Che et al, 2016;Pullianen, 2006).…”

mentioning

confidence: 99%

“…Some research assimilated snow depth observed at stations or built a local empirical relationship between snow depth observations and spectral gradients to improve the snow depth retrieval accuracy in some regions Che et al, 2016Che et al, , 2008Pullianen, 2006). In order to reduce the influence of forest, forest transmissivities at different frequencies were absorbed in the algorithm or the special equations were built between snow depth and brightness temperature difference (TBD) at different types of forest to estimate snow depth more accurately at forest areas (Che et al, 2016;Pullianen, 2006). However, uncertainties still exist for the snow depth over QTP except regarding grain size and forest.…”

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confidence: 99%

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Evaluation of snow cover and snow depth on the Qinghai–Tibetan Plateau derived from passive microwave remote sensing

et al. 2017

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Abstract. Snow cover on the Qinghai-Tibetan Plateau (QTP) plays a significant role in the global climate system and is an important water resource for rivers in the high-elevation region of Asia. At present, passive microwave (PMW) remote sensing data are the only efficient way to monitor temporal and spatial variations in snow depth at large scale. However, existing snow depth products show the largest uncertainties across the QTP. In this study, MODIS fractional snow cover product, point, line and intense sampling data are synthesized to evaluate the accuracy of snow cover and snow depth derived from PMW remote sensing data and to analyze the possible causes of uncertainties. The results show that the accuracy of snow cover extents varies spatially and depends on the fraction of snow cover. Based on the assumption that grids with MODIS snow cover fraction > 10 % are regarded as snow cover, the overall accuracy in snow cover is 66.7 %, overestimation error is 56.1 %, underestimation error is 21.1 %, commission error is 27.6 % and omission error is 47.4 %. The commission and overestimation errors of snow cover primarily occur in the northwest and southeast areas with low ground temperature. Omission error primarily occurs in cold desert areas with shallow snow, and underestimation error mainly occurs in glacier and lake areas. With the increase of snow cover fraction, the overestimation error decreases and the omission error increases. A comparison between snow depths measured in field experiments, measured at meteorological stations and estimated across the QTP shows that agreement between observation and retrieval improves with an increasing number of observation points in a PMW grid. The misclassification and errors between observed and retrieved snow depth are associated with the relatively coarse resolution of PMW remote sensing, ground temperature, snow characteristics and topography. To accurately understand the variation in snow depth across the QTP, new algorithms should be developed to retrieve snow depth with higher spatial resolution and should consider the variation in brightness temperatures at different frequencies emitted from ground with changing ground features.

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Section: Snow Characteristicsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Evaluation of snow cover and snow depth on the Qinghai–Tibetan Plateau derived from passive microwave remote sensing

et al. 2017

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“…To increase the spatial coverage of snow depth, researchers have used different instruments (e.g., lidar, airborne laser scanning, and unmanned aerial systems) (Hopkinson et al, 2004;Grünewald and Lehning, 2013;Bühler et al, 2016) or developed and/or improved passive microwave snow algorithms (Foster et al, 1997;Derksen et al, 2003;Grippaa et al, 2004;Che et al, 2016). Although snow depth and SWE obtained from passive microwave satellite remote sensing could mitigate regional deficiency of in situ snow depth measurements, they have low spatial resolution (25 km × 25 km), and the accuracy is always affected by underlying surface conditions and algorithms.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal variability of snow depth across the Eurasian continent from 1966 to 2012

et al. 2018

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Abstract. Snow depth is one of the key physical parameters for understanding land surface energy balance, soil thermal regime, water cycle, and assessing water resources from local community to regional industrial water supply. Previous studies by using in situ data are mostly site specific; data from satellite remote sensing may cover a large area or global scale, but uncertainties remain large. The primary objective of this study is to investigate spatial variability and temporal change in snow depth across the Eurasian continent. Data used include long-term ground-based measurements from 1814 stations. Spatially, long-term (1971Spatially, long-term ( -2000 mean annual snow depths of >20 cm were recorded in northeastern European Russia, the Yenisei River basin, Kamchatka Peninsula, and Sakhalin. Annual mean and maximum snow depth increased by 0.2 and 0.6 cm decade −1 from 1966 through 2012. Seasonally, monthly mean snow depth decreased in autumn and increased in winter and spring over the study period. Regionally, snow depth significantly increased in areas north of 50 • N. Compared with air temperature, snowfall had greater influence on snow depth during November through March across the former Soviet Union. This study provides a baseline for snow depth climatology and changes across the Eurasian continent, which would significantly help to better understanding climate system and climate changes on regional, hemispheric, or even global scales.

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Remote Sensing for Snowpack Monitoring and Its Implications

Varade

Manickam

Singh

2021

Geographic Information Science for Land Resource Management

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Estimation of snow depth from passive microwave brightness temperature data in forest regions of northeast China

Cited by 116 publications

References 55 publications

Evaluation of snow cover and snow depth on the Qinghai–Tibetan Plateau derived from passive microwave remote sensing

Evaluation of snow cover and snow depth on the Qinghai–Tibetan Plateau derived from passive microwave remote sensing

Spatiotemporal variability of snow depth across the Eurasian continent from 1966 to 2012

Remote Sensing for Snowpack Monitoring and Its Implications

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