Development of a tundra-specific snow water equivalent retrieval algorithm for satellite passive microwave data

Derksen, Chris; Toose, Peter; Rees, Andrew; Wang, L.; English, Michael; Walker, Anne; Sturm, Matthew

doi:10.1016/j.rse.2010.02.019

Cited by 98 publications

(90 citation statements)

References 29 publications

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“…Many studies from Canada's IPY activities combined satellite observations with field measurements and model simulations (i.e. Derksen et al 2010;Langlois et al 2010Langlois et al , 2011 to produce time series that, when combined with existing datasets from other sources, could be used to more clearly identify and explain trends in cryospheric components.…”

Section: Approachmentioning

confidence: 99%

“…Further uncertainties are introduced by the vertically heterogeneous Arctic snowpack with dense, fine-grained wind slabs overlying loose large grained depth hoar, and by a high fraction of lakes which complicate microwave emission in some regions (Derksen et al 2009). To develop new algorithms that address these challenges, three airborne campaigns were conducted across the Canadian sub-Arctic between February and April 2008 (see Derksen et al 2010 andLanglois et al 2011). The resulting measurements allowed better parameterization of radiative transfer properties of the taiga forest canopy, including the generation of new values for microwave transmissivity and single scattering albedo of the forest (Langlois et al 2010(Langlois et al , 2011.…”

Section: Snow Covermentioning

confidence: 99%

“…For tundra regions, the IPY airborne campaigns provided the necessary high resolution airborne measurements that, when combined with intensive field observations of snow distribution and physical properties, allowed development of the first tundra-specific passive microwave SWE retrieval algorithm (as described in Derksen et al 2010). This new passive microwave derived SWE time series (derived from Special Sensor Microwave/Imager measurements) was one of four SWE datasets compiled for the 1980-2011 period.…”

Section: Snow Covermentioning

confidence: 99%

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Variability and change in the Canadian cryosphere

et al. 2012

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During the International Polar Year (IPY), comprehensive observational research programs were undertaken to increase our understanding of the Canadian polar cryosphere response to a changing climate. Cryospheric components considered were snow, permafrost, sea ice, freshwater ice, glaciers and ice shelves. Enhancement of conventional observing systems and retrieval algorithms for satellite measurements facilitated development of a snapshot of current cryospheric conditions, providing a baseline against which future change can be assessed. Key findings include: 1. surface air temperatures across the Canadian Arctic exhibit a warming trend in all seasons over the past 40 years. A consistent pan-cryospheric response to these warming temperatures is evident through the analysis of multi-decadal datasets; 2. in recent years (including the IPY period) a higher rate of change was observed compared to previous decades including warming permafrost, reduction in snow cover extent and duration, reduction in summer sea ice extent, increased mass loss from glaciers, and thinning and break-up of the remaining Canadian ice shelves. These changes illustrate both a reduction in the spatial extent and mass of the cryosphere and an increase in the temporal persistence of melt related parameters. The observed changes in the cryosphere have important implications for human activity including the close ties of northerners to the land, access to northern regions for natural resource development, and the integrity of northern infrastructure.

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Section: Approachmentioning

confidence: 99%

Section: Snow Covermentioning

confidence: 99%

Section: Snow Covermentioning

confidence: 99%

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Variability and change in the Canadian cryosphere

et al. 2012

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“…As a consequence, the signal from the underlying soil does not reach the sensor and the measured brightness temperature is mainly associated with the emission from the wet surface of the snowpack. In wet snow conditions, therefore, the retrieval of snow depth, and consequently SWE, is not possible [13][14][15][16][17][18][19].…”

Section: Considerations On the Retrieval Of Swe From Microwave Brightmentioning

confidence: 99%

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

Tedesco

Jeyaratnam

2016

Remote Sensing

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Snow is a key element of the water and energy cycles and the knowledge of spatio-temporal distribution of snow depth and snow water equivalent (SWE) is fundamental for hydrological and climatological applications. SWE and snow depth estimates can be obtained from spaceborne microwave brightness temperatures at global scale and high temporal resolution (daily). In this regard, the data recorded by the Advanced Microwave Scanning Radiometer-Earth Orbiting System (EOS) (AMSR-E) onboard the National Aeronautics and Space Administration's (NASA) AQUA spacecraft have been used to generate operational estimates of SWE and snow depth, complementing estimates generated with other microwave sensors flying on other platforms. In this study, we report the results concerning the development and assessment of a new operational algorithm applied to historical AMSR-E data. The new algorithm here proposed makes use of climatological data, electromagnetic modeling and artificial neural networks for estimating snow depth as well as a spatio-temporal dynamic density scheme to convert snow depth to SWE. The outputs of the new algorithm are compared with those of the current AMSR-E operational algorithm as well as in-situ measurements and other operational snow products, specifically the Canadian Meteorological Center (CMC) and GlobSnow datasets. Our results show that the AMSR-E algorithm here proposed generally performs better than the operational one and addresses some major issues identified in the spatial distribution of snow depth fields associated with the evolution of effective grain size.

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“…In this method, the TBD between 18 and 36 GHz increases with the snow depth and SWE [26]. This method has been used to derive the snow depth or SWE on global and regional scales [11,27,28]. Considering the influence of snow properties, Che et al [11] modified the Chang algorithm to develop a snow depth retrieval algorithm for China.…”

Section: Inter-sensor Comparison In Snow Cover Area and Snow Depthmentioning

confidence: 99%

Inter-Calibrating SMMR, SSM/I and SSMI/S Data to Improve the Consistency of Snow-Depth Products in China

2015

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Long-term snow depth/snow water equivalent (SWE) products derived from passive microwave remote sensing data are fundamental for climatological and hydrological studies. However, the temporal continuity of the products is affected by the updating or replacement of passive microwave sensors or satellite platforms. In this study, we inter-calibrated brightness temperature (Tb) data obtained from the Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager/Sounder (SSMI/S). Then, we evaluated the consistency of the snow cover area (SCA) and snow depth derived from the Scanning Multichannel Microwave Radiometer (SMMR), SSM/I and SSMI/S. The results indicated that (1) the spatial pattern of the SCA derived from the SMMR and SSM/I data was more consistent after calibration than before; (2) the relative biases in the SCA and snow depth in China between the SSM/I and SSMI/S data decreased from 42.42% to 1.65% and from 66.18% to −1.5%, respectively; and (3) the SCA and snow depth derived from the SSM/I data carried on F08, F11 and F13 were highly consistent. To obtain consistent snow depth and SCA products, inter-sensor calibrations between SMMR, SSM/I and SSMI/S are important. In OPEN ACCESSRemote Sens. 2015, 7 7213 consideration of the snow data product continuation, we suggest that the brightness temperature data from all sensors be calibrated based on SSMI/S.

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Development of a tundra-specific snow water equivalent retrieval algorithm for satellite passive microwave data

Cited by 98 publications

References 29 publications

Variability and change in the Canadian cryosphere

Variability and change in the Canadian cryosphere

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

Inter-Calibrating SMMR, SSM/I and SSMI/S Data to Improve the Consistency of Snow-Depth Products in China

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