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

Dai, Liyun; Che, Tao; Ding, Yongjian; Hao, Xiaohua

doi:10.5194/tc-11-1933-2017

Cited by 120 publications

(74 citation statements)

References 65 publications

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“…Additionally, the large uncertainties in snow depth retrieval are associated with forest cover in Northeast China, which agrees with the studies by (Cai et al, 2017;Roy et al, 2014;. The RMSE in the QTP and South China is also large due to patchy, shallow and wet snow (Dai et al, 2017(Dai et al, , 2018Yang et al, 2015). Figure 5c shows that it tends to overestimate snow depth over shallow snow areas, especially in the QTP and South China.…”

Section: Rf Model Training and Validationsupporting

confidence: 84%

“…Along with the seasonal evolution, the snow particle grows (~2 mm), and the snowpack becomes denser (200~400 kg m -3 ), which causes stronger scattering effects. In situ measurements show that the snow cover is shallow in the QTP, even less than 5 cm, which results in patchy snow cover (Dai et al, 2017). However, the snow depth was overestimated, which may be due to the following reasons.…”

Section: Rf Model Training and Validationmentioning

confidence: 99%

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Real-Time Snow Depth Estimation and Historical Data Reconstruction Over China Based on a Random Forest Machine Learning Approach

Yang¹,

Jiang²,

Luojus³

et al. 2019

Preprint

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Abstract. Snow depth data time series are valuable for climatological and hydrological applications. Passive microwave (PMW) sensors are advantageous for estimating spatially and temporally continuous snow depth. However, PMW estimate accuracy has several problems, which results in poor performances of traditional snow depth estimation algorithms. Machine learning (ML) is a common method used in many research fields, and its early application in remote sensing is promising. In this study, we propose a new and accurate approach based on the ML technique to estimate real-time snow depth and reconstruct historical snow depth from 1987–2018. First, we trained the random forest (RF) model with advanced microwave scanning radiometer 2 (AMSR2) brightness temperatures (TB) at 10.65, 18.7, 36.5 and 89 GHz, land cover fraction (forest, shrub, grass, farm and barren), geolocation (latitude and longitude) and station observation from 2014–2015. Then, the trained RF model was used to retrieve a reference dataset with 2012–2018 AMSR2 TB data as the accurate snow depth. With this reference snow depth dataset, we developed the pixel-based algorithm for the Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMI/S). Finally, the pixel-based method was used to reconstruct a consistent 31-year daily snow depth dataset for 1987–2018. We validated the trained RF model using the weather station observations and AMSR2 TB during 2012–2013. The results showed that the RF model root mean square error (RMSE) and bias were 4.5 cm and 0.04 cm, respectively. The pixel-based algorithm’s accuracy was evaluated against the field sampling experiments dataset (January–March, 2018) and station observations in 2017–2018, and the RMSEs were 2.0 cm and 5.1 cm, respectively. The pixel-based method performs better than the previous regression method fitted in China (RMSEs are 4.7 cm and 8.4 cm, respectively). The high accuracy of the pixel-based method can be attributed to the spatial dynamic retrieval coefficients and accurate snow depth estimates of the RF model. Additionally, the 1987–2018 long-term snow depth dataset was analyzed in terms of temporal and spatial variations. On the spatial scale, daily maximum snow depth tends to occur in Xinjiang and the Himalayas during 1992–2018. However, the daily mean snow depth in Northeast China is the largest. For the temporal characteristics, the February mean snow depth is the thickest during snowy winter seasons. Interestingly, the January mean snow depth represents the annual mean snow depth, which plays an important role in snow depth prediction and hydrological management. In conclusion, through step-by-step validation using in situ observations, our pixel-based approach is available in real-time snow depth retrievals and historical data reconstruction.

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Section: Rf Model Training and Validationsupporting

confidence: 84%

Section: Rf Model Training and Validationmentioning

confidence: 99%

Real-Time Snow Depth Estimation and Historical Data Reconstruction Over China Based on a Random Forest Machine Learning Approach

Yang¹,

Jiang²,

Luojus³

et al. 2019

Preprint

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“…Several studies pointed out that passive microwave remote sensing has a limited ability to detect wet snow during the snow melt season, which may underestimate the D d [87][88][89][90]. Meanwhile, misclassification and error in deriving snow cover were attributed to relatively coarse spatial resolution, as well as the complexity of snow characteristics and topography [91][92][93][94]. Combined with optical remote sensing, passive microwave remote sensing and a land surface model can effectively improve the monitoring accuracy of snow phenology and snow depth [95].…”

Section: Limitation and Outlookmentioning

confidence: 99%

Changes in Snow Phenology from 1979 to 2016 over the Tianshan Mountains, Central Asia

Yang

Ahmad

et al. 2019

Remote Sensing

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Snowmelt from the Tianshan Mountains (TS) is a major contributor to the water resources of the Central Asian region. Thus, changes in snow phenology over the TS have significant implications for regional water supplies and ecosystem services. However, the characteristics of changes in snow phenology and their influences on the climate are poorly understood throughout the entire TS due to the lack of in situ observations, limitations of optical remote sensing due to clouds, and decentralized political landscapes. Using passive microwave remote sensing snow data from 1979 to 2016 across the TS, this study investigates the spatiotemporal variations of snow phenology and their attributes and implications. The results show that the mean snow onset day (Do), snow end day (De), snow cover duration days (Dd), and maximum snow depth (SDmax) from 1979 to 2016 were the 78.2nd day of hydrological year (DOY), 222.4th DOY, 146.2 days, and 16.1 cm over the TS, respectively. Dd exhibited a spatial distribution of days with a temperature of <0 °C derived from meteorological station observations. Anomalies of snow phenology displayed the regional diversities over the TS, with shortened Dd in high-altitude regions and the Fergana Valley but increased Dd in the Ili Valley and upper reaches of the Chu and Aksu Rivers. Increased SDmax was exhibited in the central part of the TS, and decreased SDmax was observed in the western and eastern parts of the TS. Changes in Dd were dominated by earlier De, which was caused by increased melt-season temperatures (Tm). Earlier De with increased accumulation of seasonal precipitation (Pa) influenced the hydrological processes in the snowmelt recharge basin, increasing runoff and earlier peak runoff in the spring, which intensified the regional water crisis.

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“…Lastly, the Tb and Tb biases in QTP are smaller and more stable than observations in XJ and NE, except at a high frequency, 85 or 91 GHz. The main reason is that the air temperature creates a slight variation due to the high elevation [13,50,51]. For the 19 GHz channel, the differences mostly range from -2 to +2 K, and from -5 to +5 K in NE.…”

Section: Comparison Between Ssm/i and Ssmis Brightness Temperaturementioning

confidence: 99%

“…The second focus point is the effect of snow microstructure on its microwave signature. Snow layers have typically distinct grain size, grain type, density, hardness, and wetness, which affects the scattering of microwave radiation from dry snow [5,6,[11][12][13]. Even small changes in snow microstructure, such as snow grain, modify the measured Tb notably [60].…”

Section: The Biases In Different Climatological Snow Classesmentioning

confidence: 99%

The Consistency of SSM/I vs. SSMIS and the Influence on Snow Cover Detection and Snow Depth Estimation over China

et al. 2019

Self Cite

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The long-term variations in snow depth are important in hydrological, meteorological, and ecological implications and climatological studies. The series of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) instruments onboard the Defense Meteorological Satellite Program (DMSP) platforms has provided a consistent 30+ year data record of global observations that is well-suited for the estimation of snow cover, snow depth, and snow water equivalent (SWE). To maximize the use of this continuous microwave observation dataset in long-term snow analysis and obtain an objective result, consistency among the SSM/I and SSMIS sensors is required. In this paper, we evaluated the consistency between the SSM/I and SSMIS concerning the observed brightness temperature (Tb) and the retrieved snow cover area and snow depth from January 2007 to December 2008, where the F13 SSM/I and the F17 SSMIS overlapped. Results showed that Tb bias at 19 GHz spans from −2 to −3 K in snow winter seasons, and from −4 to −5 K in non-snow seasons. There is a slight Tb bias at 37 GHz from −2 to 2 K, regardless of season. For 85 (91) GHz, the bias presents some uncertainty from the scattering effect of the snowpack and atmospheric emission. The overall consistency between SSM/I and SSMIS with respect to snow cover detection is between 80% and 100%, which will result in a maximum snow cover area difference of 25 × 10 4 km 2 in China. The inconsistency in Tb between SSM/I and SSMIS can result in a −2 and −0.67 cm snow depth bias for the dual-channel and multichannel algorithms, respectively. SSMIS tends to yield lower snow depth estimates than SSM/I. Moreover, there are notable bias differences between SSM/I-and SSMIS-estimated snow depths in the tundra and taiga snow classes. Our results indicate the importance of considering the Tb bias in microwave snow cover detection and snow depth retrieval and point out that, due to the sensitivity of bias to seasons, it is better to do the intercalibration with a focus on snow-covered winter seasons. Otherwise, the bias in summer will disturb the calibration coefficients and introduce more error into the snow retrievals if the seasonal difference is not carefully evaluated and separated.

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

Cited by 120 publications

References 65 publications

Real-Time Snow Depth Estimation and Historical Data Reconstruction Over China Based on a Random Forest Machine Learning Approach

Real-Time Snow Depth Estimation and Historical Data Reconstruction Over China Based on a Random Forest Machine Learning Approach

Changes in Snow Phenology from 1979 to 2016 over the Tianshan Mountains, Central Asia

The Consistency of SSM/I vs. SSMIS and the Influence on Snow Cover Detection and Snow Depth Estimation over China

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