A New Operational Snow Retrieval Algorithm Applied to Historical AMSR-E Brightness Temperatures

Tedesco, Marco; Jeyaratnam, Jeyavinoth

doi:10.3390/rs8121037

Cited by 54 publications

(36 citation statements)

References 50 publications

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“…Despite this, the retrieval of the water contained in the snowpack, rather than its areal coverage, is much more important to water resources and flood/drought monitoring and prediction (Mernild et al, ; Schneider & Molotch, ; Tedesco & Jeyaratnam, ). However, the retrieval of snow water content (usually termed as SWE) remains very challenging (Tedesco & Jeyaratnam, ) because of the complexities of changes in dialectic properties of snow crystals as they age, the snow accumulation and melt processes that lead to highly variable spatial distributions of snow and meltwater within the snowpack, especially in mountainous regions, and variations in snow consistency in terms of the distribution of snow density and ice layers within the snowpack that may also be contaminated with soot and dust (Molina et al, ). The interaction of snow with vegetation also complicates retrievals.…”

Section: Current Remote Sensing Technologies and Productsmentioning

confidence: 99%

Satellite Remote Sensing for Water Resources Management: Potential for Supporting Sustainable Development in Data‐Poor Regions

Sheffield

Wood

Pan

et al. 2018

Water Resources Research

345

110

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Water resources management (WRM) for sustainable development presents many challenges in areas with sparse in situ monitoring networks. The exponential growth of satellite based information over the past decade provides unprecedented opportunities to support and improve WRM. Furthermore, traditional barriers to the access and usage of satellite data are lowering as technological innovations provide opportunities to manage and deliver this wealth of information to a wider audience. We review data needs for WRM and the role that satellite remote sensing can play to fill gaps and enhance WRM, focusing on the Latin American and Caribbean as an example of a region with potential to further develop its resources and mitigate the impacts of hydrological hazards. We review the state-of-the-art for relevant variables, current satellite missions, and products, how they are being used currently by national agencies across the Latin American and Caribbean region, and the challenges to improving their utility. We discuss the potential of recently launched, upcoming, and proposed missions that are likely to further enhance and transform assessment and monitoring of water resources. Ongoing challenges of accuracy, sampling, and continuity still need to be addressed, and further challenges related to the massive amounts of new data need to be overcome to best leverage the utility of satellite based information for improving WRM.

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Section: Current Remote Sensing Technologies and Productsmentioning

confidence: 99%

Satellite Remote Sensing for Water Resources Management: Potential for Supporting Sustainable Development in Data‐Poor Regions

Sheffield

Wood

Pan

et al. 2018

Water Resources Research

345

110

View full text Add to dashboard Cite

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“…Figure 11a also shows that the bias tends to be bigger at the end of snowy seasons. This is because the presence of liquid water due to the relatively high air temperature in these months makes the retrieval of snow depth impossible [45,52]. Figure 11b shows that the bias between SSM/I and SSMIS for the multichannel algorithm is stable and small (approximately −1 cm).…”

Section: Comparison Between Ssm/i and Ssmis Snow Depthmentioning

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

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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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“…The latest Intergovernmental Panel on Climate Change (IPCC) special report of 2018 stated that the cryosphere is very sensitive to climatic changes, and extreme snow cover changes and melting caused by global warming were threatening natural and human systems (Hoegh-Guldberg et al, 2018). Long-term snow cover records are crucial for climate studies, hydrological applications and weather forecasts over the Northern Hemisphere (Gong et al, 2007;Derksen et al, 2012;Safavi et al, 2017;Tedesco et al, 2016;. A key parameter is the snow water equivalent (SWE), which describes the amount of water stored in the snowpack as a product of snow depth and mean snow density (Dressler et al, 2006;Kelly et al, 2009;Foster et al, 2011;Xiao et al, 2018;Takala et al, 2017;Tedesco et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Long-term snow cover records are crucial for climate studies, hydrological applications and weather forecasts over the Northern Hemisphere (Gong et al, 2007;Derksen et al, 2012;Safavi et al, 2017;Tedesco et al, 2016;. A key parameter is the snow water equivalent (SWE), which describes the amount of water stored in the snowpack as a product of snow depth and mean snow density (Dressler et al, 2006;Kelly et al, 2009;Foster et al, 2011;Xiao et al, 2018;Takala et al, 2017;Tedesco et al, 2016). Fortunately, passive microwave (PMW) signals can penetrate snow cover and provide snow depth estimates through volume scattering of snow particles in dry snow conditions.…”

Section: Introductionmentioning

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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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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A New Operational Snow Retrieval Algorithm Applied to Historical AMSR-E Brightness Temperatures

Cited by 54 publications

References 50 publications

Satellite Remote Sensing for Water Resources Management: Potential for Supporting Sustainable Development in Data‐Poor Regions

Satellite Remote Sensing for Water Resources Management: Potential for Supporting Sustainable Development in Data‐Poor Regions

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

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

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