Estimating alpine snow depth by combining multifrequency passive radiance observations with ensemble snowpack modeling

Kim, Rhae Sung; Durand, M. T.; Li, Dongyue; Baldo, Elisabeth; Margulis, S. A.; Dumont, Marie; Morin, Samuel

doi:10.1016/j.rse.2019.03.016

Cited by 27 publications

(18 citation statements)

References 74 publications

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“…Many follow-on studies (e.g., [11][12][13][14][15]) showed similarly encouraging results. More recently, Kwon et al [16,17] employed T B assimilation for continental-scale snow storage estimates and Larue et al [18] and Kim et al [19] further improved the method. The focus of this current study was to characterize snow water equivalent (SWE) in the HMA region using T B assimilation methods.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia

et al. 2019

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This study explores the use of a support vector machine (SVM) as the observation operator within a passive microwave brightness temperature data assimilation framework (herein SVM-DA) to enhance the characterization of snow water equivalent (SWE) over High Mountain Asia (HMA). A series of synthetic twin experiments were conducted with the NASA Land Information System (LIS) at a number of locations across HMA. Overall, the SVM-DA framework is effective at improving SWE estimates (~70% reduction in RMSE relative to the Open Loop) for SWE depths less than 200 mm during dry snowpack conditions. The SVM-DA framework also improves SWE estimates in deep, wet snow (~45% reduction in RMSE) when snow liquid water is well estimated by the land surface model, but can lead to model degradation when snow liquid water estimates diverge from values used during SVM training. In particular, two key challenges of using the SVM-DA framework were observed over deep, wet snowpacks. First, variations in snow liquid water content dominate the brightness temperature spectral difference (ΔTB) signal associated with emission from a wet snowpack, which can lead to abrupt changes in SWE during the analysis update. Second, the ensemble of SVM-based predictions can collapse (i.e., yield a near-zero standard deviation across the ensemble) when prior estimates of snow are outside the range of snow inputs used during the SVM training procedure. Such a scenario can lead to the presence of spurious error correlations between SWE and ΔTB, and as a consequence, can result in degraded SWE estimates from the analysis update. These degraded analysis updates can be largely mitigated by applying rule-based approaches. For example, restricting the SWE update when the standard deviation of the predicted ΔTB is greater than 0.05 K helps prevent the occurrence of filter divergence. Similarly, adding a thin layer (i.e., 5 mm) of SWE when the synthetic ΔTB is larger than 5 K can improve SVM-DA performance in the presence of a precipitation dry bias. The study demonstrates that a carefully constructed SVM-DA framework cognizant of the inherent limitations of passive microwave-based SWE estimation holds promise for snow mass data assimilation.

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Section: Introductionmentioning

confidence: 99%

Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia

et al. 2019

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“…Numerous studies have investigated the relationship between common snowpack physical properties (e.g., snow depth and water equivalent) and passive microwave brightness temperature in different frequencies and polarizations (Chang et al, 1987;Dietz et al, 2011;Kim et al, 2019;Xiao et al, 2018). However, this study was the first attempt to directly estimate fractional snow cover using passive microwave data.…”

Section: Discussionmentioning

confidence: 98%

“…Wiesmann and Mätzler (1999) reported that vertical and horizontal polarization are dominated by scattering and snow stratigraphy, respectively. Thus, Kim et al (2019) only assimilated vertical polarization with an ensemble snowpack model to estimate snow depth. In Scenario-3, we evaluated the performance of the retrieval model established by only using the brightness temperature in 19-GHz, 37-GHz and 91-GHz (V 25 polarization).…”

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confidence: 99%

Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America

Xiao¹,

Liang²,

He³

et al. 2020

Preprint

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Abstract. The dynamic characteristics of seasonal snow cover are critical for hydrology management, climate system, and ecosystem function. Although optical satellite remote sensing has proved to be an effective tool for monitoring global and regional variations of snow cover, it is still problematic to accurately capture the snow dynamics characteristics at a finer spatiotemporal resolution, because the observations from optical satellite sensors are seriously affected by clouds and solar illumination. Besides, traditional methods of mapping snow cover from passive microwave data only provide binary information with a 25-km spatial resolution. In this study, we first present an approach to predict fractional snow cover over North America under all-weather conditions, derived from the enhanced resolution passive microwave brightness temperature data (6.25 km). This estimation algorithm used Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products between 2010 and 2017 to create the reference fractional snow cover data as the "true" observations. Further, the influence of many factors, including land cover, topography, and location, were incorporated into the retrieval models. The results show that the proposed retrieval models based on random forest regression technique perform much better using independent test data for all land cover classes, with higher accuracy and no out-of-range estimated values, when compared to the other three approaches (linear regression, artificial neural networks (ANN), and multivariate adaptive regression splines (MARS)). The results of the output evaluated by using independent data indicate that the root-mean-square error (RMSE) of the estimated fractional snow cover ranges from 16.7 % to 19.8 %. In addition, the estimated fractional snow cover is verified in the snow mapping aspect by using snow cover observation data from meteorological stations (more than 0.31 million records). The result shows that the binary snow cover obtained by the proposed retrieval algorithm is in a good agreement with the ground measurements (kappa: 0.67). The accuracy of our algorithm estimation in the snow cover identification shows significant improvement when benchmarked against the Grody’s snow cover mapping algorithm: overall accuracy is increased by 18 % (from 0.71 to 0.84), and omission error is reduced by 71 % (from 0.48 to 0.14). Daily time-series and full space-covered sub-pixel snow cover area data are urgently needed for climate and reanalysis studies. According to our experiment results, we can conclude that it is feasible for estimating fractional snow cover from passive microwave brightness temperature data, and this strategy also has a great advantage in detecting snow cover area.

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“…Two field work campaigns were carried out in the northern and southern branches of the MRB during the snow accumulation (10-17 December 2013) and ablation (15)(16)(17)(18)(19)(20)(21)(22) March 2014) periods. The field work included the acquisition of reflectance spectra and collection of morphological and physical parameters, and other environmental variables [49,50].…”

Section: Field Workmentioning

confidence: 99%

“…Transient snowline altitude and glacier elevation can be extracted by combining optical and synthetic aperture radar (SAR) imagery as well as DEM data [18]. Passive microwave-based models can penetrate clouds and provide measurements in shadowed regions and hence, are useful for inversion of the snow depth [19,20], snow water equivalent (SWE) [21], and snow cover storage [22]. Based on this, the composite snow cover products ESA GlobSnow SWE dataset [23] and snow data assimilation system (SNODAS) [24] were produced.Microscopically, SAP mainly refers to physical metamorphism as a variation in the grain size and particle structure [1][2][3][4].…”

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confidence: 99%

An Integrated Shadow-Adjusted Snow-Aging Index for Alpine Regions

Liu

et al. 2020

Remote Sensing

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Detecting the variations in snow cover aging over undulating alpine regions is challenging owing to the complex snow-aging process and shadow effect from steep slopes. This study proposes a novel snow-cover status index, namely shadow-adjusted snow-aging index (SASAI), portraying the integrated aging process within the Manas River Basin in northwest China. The Environment Satellites HJ-1A/B optical images and in-field measurements were used during the snow ablation and accumulation periods. The in-field measurements provide a reference for building a candidate library of snow-aging indicators. The representative aging samples for training and validation were obtained using the proposed time-gap searching method combined with the target zones established based on the altitude of snowline. An analytic hierarchy process was used to determine the snow-aging index (SAI) using multiple optimal snow-aging indicators. After correction by the extreme value optimization algorithm, the SASAI was finally corrected for the effects of shading and assessed. This study provides both a flexible algorithm that indicates the characteristics of snow aging and speculation on the causes of the aging process. The separability of the SAI/SASAI and adaptability of this algorithm on multiperiod remote sensing images further demonstrates the applicability of the SASAI to all the alpine regions. cloud removal techniques, and spatiotemporal data fusion techniques [8][9][10][11]. For example, moderate resolution imaging spectroradiometer (MODIS) data are broadly employed as a reliable data source for detecting the extent of snow cover owing to its high spatial, time, and spectral resolutions [12,13]. Its sensitivity to factors such as aerosol optical properties are also explored in alpine areas [14]. Further, the "subpixel snow-cover information", "empirical relationship assumptions" [15,16], and "spectral unmixing" [17] models have been extensively applied for the inversion of the fractional snow cover. Transient snowline altitude and glacier elevation can be extracted by combining optical and synthetic aperture radar (SAR) imagery as well as DEM data [18]. Passive microwave-based models can penetrate clouds and provide measurements in shadowed regions and hence, are useful for inversion of the snow depth [19,20], snow water equivalent (SWE) [21], and snow cover storage [22]. Based on this, the composite snow cover products ESA GlobSnow SWE dataset [23] and snow data assimilation system (SNODAS) [24] were produced.Microscopically, SAP mainly refers to physical metamorphism as a variation in the grain size and particle structure [1][2][3][4]. The accumulation of pollution in snow, increasing liquid water content, and surface roughness are also typical symptoms of SAP (these also influence snow metamorphism) [5,6]. Extensive research has been conducted on retrieving the snow grain size using "scaled band area" algorithm and the MODIS snow-covered area grain size (MODSCAG) model [15]. On the basis of the quasi-crystalline approximation (QCA) th...

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Estimating alpine snow depth by combining multifrequency passive radiance observations with ensemble snowpack modeling

Cited by 27 publications

References 74 publications

Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia

Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia

Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America

An Integrated Shadow-Adjusted Snow-Aging Index for Alpine Regions

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