Introduction to GlobSnow Snow Extent products with considerations for accuracy assessment

Metsämäki, Sari; Pulliainen, Jouni; Salminen, Miia; Luojus, Kari; Wiesmann, Andreas; Solberg, Rune; Böttcher, Kristin; Hiltunen, Mwaba; Ripper, Elisabeth

doi:10.1016/j.rse.2014.09.018

Cited by 98 publications

(81 citation statements)

References 39 publications

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“…The product is generated using the SNOMAP algorithm [166,167]. During the Globsnow-1/2 project [10], ESA also developed its SCA and SWE products [248] based on ERS-2/ATSR-2 (1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003) and ENVISAT-AATSR (2002-2012 data. For more snow product information and data access, we suggest that readers explore what is offered by the National Snow and Ice Data Center (NSIDC) [249].…”

Section: Operational Snow Productsmentioning

confidence: 99%

The Potential of Earth Observation for the Analysis of Cold Region Land Surface Dynamics in Europe—A Review

Kuenzer

Dietz

et al. 2017

Remote Sensing

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Abstract:Cold regions affect global, regional and local climate; oftentimes they are relevant for water supply, host valuable ecosystems, and support human livelihood. They are thus eminently important for human society. In the context of ongoing climate change, monitoring and understanding cold region land surface dynamics is essential for environmental scientists, stakeholders and decision makers. However, the definition of cold regions remains inexplicit, and no up-to-date cold region maps or overarching spatial analyses exist. For example, Europe has densely populated cold regions, but hardly an article exists that provides a solid overview of Earth Observation (EO) based applications assessing cold region land surface dynamics in Europe. With this review article we aim at closing this gap by providing an overview of EO-based techniques for cold region observation in Europe, focusing on the dynamics of glaciers and snow. We present a novel spatial delineation of cold regions for Europe before analyzing the benefits and limitations of different EO sensor types and data processing methods for EO based cold region research. Furthermore, we identify research gaps and discuss challenges for future studies.

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Section: Operational Snow Productsmentioning

confidence: 99%

The Potential of Earth Observation for the Analysis of Cold Region Land Surface Dynamics in Europe—A Review

Kuenzer

Dietz

et al. 2017

Remote Sensing

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“…This might be partially explained by the imperfect approximation of the coarse-resolution reference FSC. The direct aggregation of the high-resolution binary snow cover would persistently generate FSC estimates higher than those derived from coarse-resolution imagery [51]. The overall accuracy is 0.85 for the FY-2E VISSR and 0.84 for the FY-2F VISSR snow cover maps compared with the MODIS FSC; these values would be different from the evaluation results if the sparse ground station observations were used as the reference.…”

Section: Results In Tibetan Plateaumentioning

confidence: 79%

Fractional Snow Cover Mapping from FY-2 VISSR Imagery of China

Wang

Jiang

et al. 2017

Remote Sensing

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Abstract:Daily fractional snow cover (FSC) products derived from optical sensors onboard low Earth orbit (LEO) satellites are often discontinuous, primarily due to prevalent cloud cover. To map the daily cloud-reduced FSC over China, we utilized clear-sky multichannel observations from the first-generation Chinese geostationary orbit (GEO) satellites (namely, the FY-2 series) by taking advantage of their high temporal resolution. The method proposed in this study combines a newly developed binary snow cover detection algorithm designed for the Visible and Infrared Spin Scan Radiometer (VISSR) onboard FY-2F with a simple linear spectral mixture technique applied to the visible (VIS) band. This method relies upon full snow cover and snow-free end-members to estimate the daily FSC. The FY-2E/F VISSR FSC maps of China were compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) FSC data based on the multiple end-member spectral mixture analysis (MESMA), and with Landsat-8 Operational Land Imager (OLI) FSC maps based on the SNOWMAP approach. The FY-2E/F VISSR FSC maps, which demonstrate a lower cloud coverage, exhibit the root mean squared errors (RMSEs) of 0.20/0.19 compared with the MODIS FSC data. When validated against the Landsat-8 OLI FSC data, the FY-2E/F VISSR FSC maps, which display overall accuracies that can reach 0.92, have an RMSE of 0.18~0.29 with R 2 values ranging from 0.46 to 0.80.

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“…This result is, however, sensitive to the data set used. A comparison of the NorESM results with the European Space Agency's GlobSnow data set (Metsämäki et al, 2015) suggested that NorESM overestimates rather than underestimates the NH winter snow cover, although this data set does not cover regions/months with very low solar elevations.…”

Section: Comparison With Observationsmentioning

confidence: 99%

Effects of snow grain shape on climate simulations: sensitivity tests with the Norwegian Earth System Model

et al. 2017

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Abstract. Snow consists of non-spherical grains of various shapes and sizes. Still, in radiative transfer calculations, snow grains are often treated as spherical. This also applies to the computation of snow albedo in the Snow, Ice, and Aerosol Radiation (SNICAR) model and in the Los Alamos sea ice model, version 4 (CICE4), both of which are employed in the Community Earth System Model and in the Norwegian Earth System Model (NorESM). In this study, we evaluate the effect of snow grain shape on climate simulated by NorESM in a slab ocean configuration of the model. An experiment with spherical snow grains (SPH) is compared with another (NONSPH) in which the snow shortwave single-scattering properties are based on a combination of three non-spherical snow grain shapes optimized using measurements of angular scattering by blowing snow. The key difference between these treatments is that the asymmetry parameter is smaller in the non-spherical case (0.77-0.78 in the visible region) than in the spherical case ( ≈ 0.89). Therefore, for the same effective snow grain size (or equivalently, the same specific projected area), the snow broadband albedo is higher when assuming non-spherical rather than spherical snow grains, typically by 0.02-0.03. Considering the spherical case as the baseline, this results in an instantaneous negative change in net shortwave radiation with a global-mean top-of-the-model value of ca. −0.22 W m −2 . Although this global-mean radiative effect is rather modest, the impacts on the climate simulated by NorESM are substantial. The global annual-mean 2 m air temperature in NONSPH is 1.17 K lower than in SPH, with substantially larger differences at high latitudes. The climatic response is amplified by strong snow and sea ice feedbacks. It is further demonstrated that the effect of snow grain shape could be largely offset by adjusting the snow grain size. When assuming non-spherical snow grains with the parameterized grain size increased by ca. 70 %, the climatic differences to the SPH experiment become very small. Finally, the impact of assumed snow grain shape on the radiative effects of absorbing aerosols in snow is discussed.

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Introduction to GlobSnow Snow Extent products with considerations for accuracy assessment

Cited by 98 publications

References 39 publications

The Potential of Earth Observation for the Analysis of Cold Region Land Surface Dynamics in Europe—A Review

The Potential of Earth Observation for the Analysis of Cold Region Land Surface Dynamics in Europe—A Review

Fractional Snow Cover Mapping from FY-2 VISSR Imagery of China

Effects of snow grain shape on climate simulations: sensitivity tests with the Norwegian Earth System Model

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