The Effect of Boreal Forest Canopy in Satellite Snow Mapping—A Multisensor Analysis

Cohen, Juval; Lemmetyinen, Juha; Pulliainen, Jouni; Heinilä, Kirsikka; Montomoli, Francesco; Seppänen, Jaakko; Hallikainen, M.

doi:10.1109/tgrs.2015.2444422

Cited by 41 publications

(26 citation statements)

References 67 publications

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“…Forest cover in particular poses a challenge in relating coarse scale passive microwave observations to high resolution SAR. However, new methods for mitigating forest canopy effects in snow parameter retrieval from both radar and passive microwave measurements have been developed using recent experimental data [43].…”

Section: Discussionmentioning

confidence: 99%

Retrieval of Effective Correlation Length and Snow Water Equivalent from Radar and Passive Microwave Measurements

et al. 2018

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Current methods for retrieving SWE (snow water equivalent) from space rely on passive microwave sensors. Observations are limited by poor spatial resolution, ambiguities related to separation of snow microstructural properties from the total snow mass, and signal saturation when snow is deep (~>80 cm). The use of SAR (Synthetic Aperture Radar) at suitable frequencies has been suggested as a potential observation method to overcome the coarse resolution of passive microwave sensors. Nevertheless, suitable sensors operating from space are, up to now, unavailable. Active microwave retrievals suffer, however, from the same difficulties as the passive case in separating impacts of scattering efficiency from those of snow mass. In this study, we explore the potential of applying active (radar) and passive (radiometer) microwave observations in tandem, by using a dataset of co-incident tower-based active and passive microwave observations and detailed in situ data from a test site in Northern Finland. The dataset spans four winter seasons with daily coverage. In order to quantify the temporal variability of snow microstructure, we derive an effective correlation length for the snowpack (treated as a single layer), which matches the simulated microwave response of a semi-empirical radiative transfer model to observations. This effective parameter is derived from radiometer and radar observations at different frequencies and frequency combinations (10.2, 13.3 and 16.7 GHz for radar; 10.65, 18.7 and 37 GHz for radiometer). Under dry snow conditions, correlations are found between the effective correlation length retrieved from active and passive measurements. Consequently, the derived effective correlation length from passive microwave observations is applied to parameterize the retrieval of SWE using radar, improving retrieval skill compared to a case with no prior knowledge of snow-scattering efficiency. The same concept can be applied to future radar satellite mission concepts focused on retrieving SWE, exploiting existing methods for retrieval of snow microstructural parameters, as employed within the ESA (European Space Agency) GlobSnow SWE product. Using radar alone, a seasonally optimized value of effective correlation length to parameterize retrievals of SWE was sufficient to provide an accuracy of <25 mm (unbiased) Root-Mean Square Error using certain frequency combinations. A temporally dynamic value, derived from e.g., physical snow models, is necessary to further improve retrieval skill, in particular for snow regimes with larger temporal variability in snow microstructure and a more pronounced layered structure.

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Section: Discussionmentioning

confidence: 99%

Retrieval of Effective Correlation Length and Snow Water Equivalent from Radar and Passive Microwave Measurements

et al. 2018

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“…Remote sensing of snow cover has also proven to be problematic in the boreal forest zone (e.g. Foster et al, 2005;Heinilä et al, 2014) as the vegetation itself has a larger effect on the measurements and needs to be taken into account (Cohen et al, 2015;Derksen, 2008;Metsämäki et al, 2012). In tundra regions, local scale variability due to wind effects, and a stratigraphically complicated snowpack introduces different scales of variability .…”

Section: Sampling Procedures and Scalementioning

confidence: 99%

Spatial and temporal variation of bulk snow properties in northern boreal and tundra environments based on extensive field measurements

Hannula

Lemmetyinen

Kontu

et al. 2016

Geosci. Instrum. Method. Data Syst.

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Abstract. An extensive in situ data set of snow depth, snow water equivalent (SWE), and snow density collected in support of the European Space Agency (ESA) SnowSAR-2 airborne campaigns in northern Finland during the winter of 2011-2012 is presented (ESA Earth Observation Campaigns data [2000][2001][2002][2003][2004][2005][2006][2007][2008][2009][2010][2011][2012][2013][2014][2015][2016]. The suitability of the in situ measurement protocol to provide an accurate reference for the simultaneous airborne SAR (synthetic aperture radar) data products over different land cover types was analysed in the context of spatial scale, sample spacing, and uncertainty. The analysis was executed by applying autocorrelation analysis and root mean square difference (RMSD) error estimations. The results showed overall higher variability for all the three bulk snow parameters over tundra, open bogs and lakes (due to wind processes); however, snow depth tended to vary over shorter distances in forests (due to snow-vegetation interactions). Sample spacing/sample size had a statistically significant effect on the mean snow depth over all land cover types. Analysis executed for 50, 100, and 200 m transects revealed that in most cases less than five samples were adequate to describe the snow depth mean with RMSD < 5 %, but for land cover with high overall variability an indication of increased sample size of 1.5-3 times larger was gained depending on the scale and the desired maximum RMSD. Errors for most of the land cover types reached ∼ 10 % if only three measurements were considered.The collected measurements, which are available via the ESA website upon registration, compose an exceptionally large manually collected snow data set in Scandinavian taiga and tundra environments. This information represents a valuable contribution to the snow research community and can be applied to various snow studies.

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“…Recent radar-based SWE retrieval algorithms for use in forested regions have been developed through the CoReH20 framework of [7] (e.g., [18,21,22,32]). These models rely on parameters linked to biomass, such as the FF and two-way transmissivity to scale backscatter from the sub-canopy environment.…”

Section: Swe Retrievals In Forested Landscapesmentioning

confidence: 99%

“…Globally, boreal forest covers 1.1 billion ha and is the Earth's largest terrestrial ecosystem [15]. Given the northern locale and the intersection of the boreal zone with snow-covered landscapes [17], it is important that the estimation of the SWE accounts for forest attenuation of the snow backscatter signal.Recent studies on SWE retrieval from boreal environments typically rely on ancillary data, allometric relationships, or forest modeling to delineate forested areas and provide the necessary parameters, such as the forest cover fraction (FF) and transmissivity [18][19][20][21][22]. However, ancillary data becomes outdated quickly, allometric relationships tend to be regionally specific, and forest models add complexity to SWE retrieval.…”

mentioning

confidence: 99%

“…Recent studies on SWE retrieval from boreal environments typically rely on ancillary data, allometric relationships, or forest modeling to delineate forested areas and provide the necessary parameters, such as the forest cover fraction (FF) and transmissivity [18][19][20][21][22]. However, ancillary data becomes outdated quickly, allometric relationships tend to be regionally specific, and forest models add complexity to SWE retrieval.…”

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confidence: 99%

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Observations of a Coniferous Forest at 9.6 and 17.2 GHz: Implications for SWE Retrievals

Thompson

Kelly

2018

Remote Sensing

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UWScat, a ground-based Ku-and X-band scatterometer, was used to compare forested and non-forested landscapes in a terrestrial snow accumulation environment as part of the NASA SnowEx17 field campaign. Field observations from Trail Valley Creek, Northwest Territories; Tobermory, Ontario; and the Canadian Snow and Ice Experiment (CASIX) campaign in Churchill, Manitoba, were also included. Limited sensitivity to snow was observed at 9.6 GHz, while the forest canopy attenuated the signal from sub-canopy snow at 17.2 GHz. Forested landscapes were distinguishable using the volume scattering component of the Freeman-Durden three-component decomposition model by applying a threshold in which values ≥50% indicated forested landscape. It is suggested that the volume scattering component of the decomposition can be used in current snow water equivalent (SWE) retrieval algorithms in place of the forest cover fraction (FF), which is an optical surrogate for microwave scattering and relies on ancillary data. The performance of the volume scattering component of the decomposition was similar to that of FF when used in a retrieval scheme. The primary benefit of this method is that it provides a current, real-time estimate of the forest state, it automatically accounts for the incidence angle and canopy structure, and it provides coincident information on the forest canopy without the use of ancillary data or modeling, which is especially important in remote regions. Additionally, it enables the estimation of forest canopy transmissivity without ancillary data. This study also demonstrates the use of these frequencies in a forest canopy application, and the use of the Freeman-Durden three-component decomposition on scatterometer observations in a terrestrial snow accumulation environment. of radar response [7][8][9][10]. Synthetic aperture radar (SAR) imaging can provide wintertime landscape observations at spatial resolutions <100 m, which makes it an attractive technique for snow mapping.Current methods for estimating the SWE at Ku-and X-band frequencies focus on moderate-to-shallow snow and include that proposed by [7] for the CoReH20 mission and that proposed by [11], which uses an interferometric approach; other recent work has been done by [12][13][14]. However, these studies have excluded forested regions and, therefore, have neglected the sub-canopy SWE. This is unsurprising, because forested regions are among the most challenging environments for remote sensing of snow [15]. Of particular relevance in Canada, the boreal forest, dominated by coniferous species and situated in the circumpolar northern high latitudes, covers 270 million ha, or about 30% of the landscape [16]. Globally, boreal forest covers 1.1 billion ha and is the Earth's largest terrestrial ecosystem [15]. Given the northern locale and the intersection of the boreal zone with snow-covered landscapes [17], it is important that the estimation of the SWE accounts for forest attenuation of the snow backscatter signal.Recent studies on SWE retrieval from borea...

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The Effect of Boreal Forest Canopy in Satellite Snow Mapping—A Multisensor Analysis

Cited by 41 publications

References 67 publications

Retrieval of Effective Correlation Length and Snow Water Equivalent from Radar and Passive Microwave Measurements

Retrieval of Effective Correlation Length and Snow Water Equivalent from Radar and Passive Microwave Measurements

Spatial and temporal variation of bulk snow properties in northern boreal and tundra environments based on extensive field measurements

Observations of a Coniferous Forest at 9.6 and 17.2 GHz: Implications for SWE Retrievals

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