Simulating seasonally and spatially varying snow cover brightness temperature using HUT snow emission model and retrieval of a microwave effective grain size

Lemmetyinen, Juha; Derksen, Chris; Toose, Peter; Proksch, Martin; Pulliainen, Jouni; Kontu, Anna; Rautiainen, Kimmo; Seppänen, Jaakko; Hallikainen, M.

doi:10.1016/j.rse.2014.09.016

Cited by 47 publications

(46 citation statements)

References 63 publications

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“…However, using data collected during NoSREx, Leppänen et al (2015) demonstrated that visually established grain sizes E correlated with optical grain sizes measured using an objective measure of SSA. Furthermore, Lemmetyinen et al (2015) showed that an average grain size used to fit emission model predictions captured both the magnitude and the seasonal trend of the visually estimated grain sizes during NoSREx-II. Therefore, the information collected on E can be used at least as an indicator of snow microstructural evolution during the NoSREx campaigns, even if not employed directly in e.g.…”

Section: Comparison To Observationsmentioning

confidence: 99%

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Nordic Snow Radar Experiment

Lemmetyinen

Kontu

Pulliainen

et al. 2016

Geosci. Instrum. Method. Data Syst.

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Abstract. The objective of the Nordic Snow Radar Experiment (NoSREx) campaign was to provide a continuous time series of active and passive microwave observations of snow cover at a representative location of the Arctic boreal forest area, covering a whole winter season. The activity was a part of Phase A studies for the ESA Earth Explorer 7 candidate mission CoReH2O (Cold Regions Hydrology Highresolution Observatory).The NoSREx campaign, conducted at the Finnish Meteorological Institute Arctic Research Centre (FMI-ARC) in Sodankylä, Finland, hosted a frequency scanning scatterometer operating at frequencies from X-to Ku-band. The radar observations were complemented by a microwave dualpolarization radiometer system operating from X-to Wbands. In situ measurements consisted of manual snow pit measurements at the main test site as well as extensive automated measurements on snow, ground and meteorological parameters.This study provides a summary of the obtained data, detailing measurement protocols for each microwave instrument and in situ reference data. A first analysis of the microwave signatures against snow parameters is given, also comparing observed radar backscattering and microwave emission to predictions of an active/passive forward model.All data, including the raw data observations, are available for research purposes through the European Space Agency and the Finnish Meteorological Institute. A consolidated dataset of observations, comprising the key microwave and in situ observations, is provided through the ESA campaign data portal to enable easy access to the data.

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Section: Comparison To Observationsmentioning

confidence: 99%

“…Rather, e.g. Lemmetyinen et al (2015) applied a simplification of the measured snow profiles to either one or two layers. In addition, a third-order fit was applied to the observations of E to reduce uncertainty arising e.g.…”

Section: Manual In Situ Data Collectionmentioning

confidence: 99%

Nordic Snow Radar Experiment

Lemmetyinen

Kontu

Pulliainen

et al. 2016

Geosci. Instrum. Method. Data Syst.

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“…A representative sample of snow grains was manually removed from each layer for the purpose. Despite the acknowledged subjectivity and relatively high uncertainty of the measurement method, the grain size samples collected from the site have been shown to correlate with optical grain size measured using an integrating sphere instrument [46] as well as the microwave effective grain size derived from passive microwave observations by minimizing the simulated brightness temperature from model against the observed brightness temperature at 18 and 37 GHz in a cost function with the grain size as a free parameter in the model [47]. A Gamma Water Instrument (GWI), which measures the extinction of gamma rays emitted by an artificial gamma ray source over a given spectral band, was used to provide continuous information of Snow Water Equivalent.…”

Section: Nosrex Experimental Datasetmentioning

confidence: 99%

Estimating Snow Water Equivalent with Backscattering at X and Ku Band Based on Absorption Loss

et al. 2016

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Snow water equivalent (SWE) is a key parameter in the Earth's energy budget and water cycle. It has been demonstrated that SWE can be retrieved using active microwave remote sensing from space. This necessitates the development of forward models that are capable of simulating the interactions of microwaves and the snow medium. Several proposed models have described snow as a collection of sphere-or ellipsoid-shaped ice particles embedded in air, while the microstructure of snow is, in reality, more complex. Natural snow usually forms a sintered structure following mechanical and thermal metamorphism processes. In this research, the bi-continuous vector radiative transfer (bi-continuous-VRT) model, which firstly constructs snow microstructure more similar to real snow and then simulates the snow backscattering signal, is used as the forward model for SWE estimation. Based on this forward model, a parameterization scheme of snow volume backscattering is proposed. A relationship between snow optical thickness and single scattering albedo at X and Ku bands is established by analyzing the database generated from the bi-continuous-VRT model. A cost function with constraints is used to solve effective albedo and optical thickness, while the absorption part of optical thickness is obtained from these two parameters. SWE is estimated after a correction for physical temperature. The estimated SWE is correlated with the measured SWE with an acceptable accuracy. Validation against two-year measurements, using the SnowScat instrument from the Nordic Snow Radar Experiment (NoSREx), shows that the estimated SWE using the presented algorithm has a root mean square error (RMSE) of 16.59 mm for the winter of 2009

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“…The Sodankylä manual snow survey program aims to study the spatial and temporal variability of snowpack in varying environmental conditions typical to the boreal forest zone, including pine and spruce forests, open bogs and lake ice (e.g. Hannula et al, 2016;Lemmetyinen et al, 2015;Kontu et al, 2014;Kontu and Pulliainen, 2010). The data set is also important as a reference for the development of remote sensing instruments (Lemmetyinen et al, 2016b) and interpretation algorithms and models (Leinss et al, 2015;Schwank et al, 2014;Rautiainen et al, 2014).…”

Section: Leppänen Et Al: Sodankylä Manual Snow Survey Programmentioning

confidence: 99%

Sodankylä manual snow survey program

Leppänen

Kontu

Hannula

et al. 2016

Geosci. Instrum. Method. Data Syst.

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Abstract. The manual snow survey program of the Arctic Research Centre of the Finnish Meteorological Institute (FMI-ARC) consists of numerous observations of natural seasonal taiga snowpack in Sodankylä, northern Finland. The easily accessible measurement areas represent the typical forest and soil types in the boreal forest zone. Systematic snow measurements began in 1909 with snow depth (HS) and snow water equivalent (SWE). In 2006 the manual snow survey program expanded to cover snow macro-and microstructure from regular snow pits at several sites using both traditional and novel measurement techniques. Present-day snow pit measurements include observations of HS, SWE, temperature, density, stratigraphy, grain size, specific surface area (SSA) and liquid water content (LWC). Regular snow pit measurements are performed weekly during the snow season. Extensive time series of manual snow measurements are important for the monitoring of temporal and spatial changes in seasonal snowpack. This snow survey program is an excellent base for the future research of snow properties.

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Simulating seasonally and spatially varying snow cover brightness temperature using HUT snow emission model and retrieval of a microwave effective grain size

Cited by 47 publications

References 63 publications

Nordic Snow Radar Experiment

Nordic Snow Radar Experiment

Estimating Snow Water Equivalent with Backscattering at X and Ku Band Based on Absorption Loss

Sodankylä manual snow survey program

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