Mapping of snow covered area with Radarsat in Norway

Malnes, Eirik; Guneriussen, T.

doi:10.1109/igarss.2002.1025145

Cited by 33 publications

(23 citation statements)

References 6 publications

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“…where x is the difference of the backscattering coefficient and a is a steepness factor (Malnes & Guneriussen, 2002). A steepness factor of 0.75 was found adequate for the current study.…”

Section: Creation Of the Wet Snow Mapsmentioning

confidence: 90%

“…Under conditions where the ground has started to dry, there can be significant errors (Luojus, Pulliainen, Metsämäki, Anttila, & Hallikainen, 2007). An alternative is to use a soft threshold in Nagler and Rott's methodology instead of the hard −3 dB threshold, to account for the bigger uncertainty of certain types of land cover (Malnes & Guneriussen, 2002;Longépé, Allain, Ferro-Famil, Pottier, & Durand, 2009).…”

Section: Introductionmentioning

confidence: 99%

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Monitoring snow wetness in an Alpine Basin using combined C-band SAR and MODIS data

Rondeau-Genesse

Trudel

Leconte

2016

Remote Sensing of Environment

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“…where x is the difference of the backscattering coefficient and a is a steepness factor (Malnes & Guneriussen, 2002). A steepness factor of 0.75 was found adequate for the current study.…”

Section: Creation Of the Wet Snow Mapsmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Monitoring snow wetness in an Alpine Basin using combined C-band SAR and MODIS data

Rondeau-Genesse

Trudel

Leconte

2016

Remote Sensing of Environment

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“…Wet snow is detected by utilising the high absorption, and, therefore, low backscatter, of wet snow and then comparing the backscatter with the corresponding pixel of a reference image acquired during dry-snow or snow-free conditions. Dry snow could then be postulated above the mean-wet-snow elevation zone (Malnes & Guneriussen, 2002;Malnes et al, 2004). This methodology has been further improved by taking into account in situ air temperature measurements from meteorological station networks, which were used to derive an interpolated temperature map based on standard 6°C per km altitude lapse rate and a digital elevation model.…”

Section: Methodological Foundation Of New Algorithmsmentioning

confidence: 99%

“…In recent years, various SAR (synthetic aperture radar) algorithms have been tested and improvements undertaken (Malnes & Guneriussen, 2002;Malnes et al, 2004). A new generation of 'multi-source' algorithms have been developed: a time-series multisensor retrieval algorithm for fractional snow cover, FSC , and a time-series multiparameter algorithm for snow surface wetness, SSW (Solberg et al, 2004).…”

mentioning

confidence: 99%

The development of new algorithms for remote sensing of snow conditions based on data from the catchment of Øvre Heimdalsvatn and the vicinity

et al. 2010

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The catchment of Øvre Heimdalsvatn and the surrounding area was established as a site for snow remote sensing algorithm development, calibration and validation in 1997. Information on snow cover and snowmelt are important for understanding the timing and scale of many lake ecosystem processes. Field campaigns combined with data from airborne sensors and spaceborne high-resolution sensors have been used as reference data in experiments over many years. Several satellite sensors have been utilised in the development of new algorithms, including Terra MODIS and Envisat ASAR. The experiments have been motivated by operational prospects for snow hydrology, meteorology and climate monitoring by satellite-based remote sensing techniques. This has resulted in new time-series multi-sensor approaches for monitoring of snow cover area (SCA) and snow surface wetness (SSW). The idea was to analyse, on a daily basis, a time series of optical and radar satellite data in multi-sensor models. The SCA algorithm analyses each optical and synthetic aperture radar (SAR) image individually and combines them into a day product based on a set of confidence functions. The SSW algorithm combines information about the development of the snow surface temperature and the snow grain size (SGS) in a time-series analysis. The snow cover algorithm is being evaluated for application in a global climate monitoring system for snow variables. The successful development of these algorithms has led to operational applications of snow monitoring in Norway and Sweden, as well as enabling the prediction of the spring snowmelt flood and thus the initiation of many lake production processes.

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“…The work behind this paper has partly been funded by the European Commission projects EnviSnow and EuroClim and the Norwegian Research Council project SnowMan digital elevation models (DEM) and the wet snow line to postulate dry snow pixels above the wet snow [5]. The methodology has been further improved by taking into account in-situ air temperature measurements and deriving interpolated temperature fields based on standard 6ºC/km height laps rate.…”

Section: A Parameter Retrieval Algorithmsmentioning

confidence: 99%