'The AMSR2 Satellite-Based Microwave Snow Algorithm (SMSA): A New Algorithm for Estimating Global Snow Accumulation

Kelly, Richard; Li, Qinghuan; Saberi, Nastaran

doi:10.1109/igarss.2019.8898525

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…We also assess five gridded products that incorporate information from passive-microwave brightness temperatures in order to fully or partially constrain surface SWE. The JAXA-AMSR2 product is a standalone passive microwave product that estimates SWE using a retrieval algorithm based only on time varying microwave brightness temperatures and other timeinvariant ancillary data (Kelly et al, 2019). The remaining four Earth Observation (EO) products (GlobSnow v2 and v3 and Snow_cci v1 and v2) are related with a shared development history and their SWE output has strong similarities to one another (hereafter we refer to them collectively as GS/CCI products).…”

Section: Product Namementioning

confidence: 99%

Benchmarking of SWE products based on outcomes of the SnowPEx+ Intercomparison Project

Mudryk,

Mortimer,

Derksen

et al. 2024

Preprint

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Abstract. We assess and rank 23 gridded snow water equivalent (SWE) products by implementing a novel evaluation strategy using a new suite of reference data from two cross-validated sources and a series of product inter-comparisons. The new reference data combines in situ measurements from both snow courses and airborne gamma measurements. Compared to previous evaluations of gridded products, we have substantially increased the spatial coverage and sample size across North America, and we are able to evaluate product performance across both mountain and non-mountain regions. The evaluation strategy we use ranks overall relative product performance while still accounting for individual differences in ability to represent SWE climatology, variability, and trends. Assessing these gridded products fills an important gap in the literature since individual gridded products are frequently chosen without prior justification as the basis for evaluating land surface and climate model outputs, along with other climate applications. The top performing products across the range of tests performed are ERA5-Land followed by the Crocus snow model. Our evaluation indicates that accurate representation of hemispheric SWE varies tremendously across the range of products. While most products are able to represent SWE reasonably well across Northern Hemisphere non-mountainous regions, the ability to accurately represent SWE in mountain regions and to accurately represent historical trends are much more variable. Finally, we demonstrate that for the ensemble of products evaluated here, attempts to assimilate surface snow observations and/or satellite measurements lead to a deleterious influence on regional snow mass trends, which is an important consideration for how such gridded products are produced and applied in the future.

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Section: Product Namementioning

confidence: 99%

Benchmarking of SWE products based on outcomes of the SnowPEx+ Intercomparison Project

Mudryk,

Mortimer,

Derksen

et al. 2024

Preprint

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“…In forested regions, ΔTbs decrease with increasing air temperatures that are below freezing. Typical global PM snow retrieval algorithms (see Kelly et al 2003;Kelly 2009;Takala et al 2011;Pulliainen and Grandeil 1999;Luojus et al 2013;Xue and Forman 2015;Li and Kelly 2017;Kelly, Li, and Saberi 2019) ignore the influence of this temperature-transmissivity relationship on ΔTb. Therefore, ΔTb variations responding to the temperature-transmissivity relationship add uncertainty to SD and SWE retrievals.…”

Section: Introductionmentioning

confidence: 99%

The influence of tree transmissivity variations in winter on satellite snow parameter observations

Kelly

Lemmetyinen

et al. 2021

International Journal of Digital Earth

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The ability to correct for the influence of forest cover is crucial for retrieval of surface geophysical parameters such as snow cover and soil properties from microwave remote sensing. Existing correction approaches to brightness temperatures for northern boreal forest regions consider forest transmissivity constant during wintertime. However, due to biophysical protection mechanisms, below freezing air temperatures freeze the water content of northern tree species only gradually. As a consequence, the permittivity of many northern tree species decreases with the decrease of air temperature under sub-zero temperature conditions. This results in a monotonic increase of the tree vegetation transmissivity, as the permittivity contrast to the surrounding air decreases. The influence of this tree temperature-transmissivity relationship on the performance of the frequency difference passive microwave snow retrieval algorithms has not been considered. Using ground-based observations and an analytical model simulation based on Mätzler's approach (1994), the influence of the temperaturetransmissivity relationship on the snow retrieval algorithms, based on the spectral difference of two microwave channels, is characterized. A simple approximation approach is then developed to successfully characterize this influence (the RMSE between the analytical model simulation and the approximation approach estimation is below 0.3 K). The approximation is applied to spaceborne observations, and demonstrates the capacity to reduce the influence of the forest temperature-transmissivity relationship on passive microwave frequency difference brightness temperature.

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Machine Learning-Based Estimation of High-Resolution Snow Depth in Alaska Using Passive Microwave Remote Sensing Data

Tanniru

Ramsankaran

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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'The AMSR2 Satellite-Based Microwave Snow Algorithm (SMSA): A New Algorithm for Estimating Global Snow Accumulation

Cited by 4 publications

References 9 publications

Benchmarking of SWE products based on outcomes of the SnowPEx+ Intercomparison Project

Benchmarking of SWE products based on outcomes of the SnowPEx+ Intercomparison Project

The influence of tree transmissivity variations in winter on satellite snow parameter observations

Machine Learning-Based Estimation of High-Resolution Snow Depth in Alaska Using Passive Microwave Remote Sensing Data

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