The role of Earth Observation in snowmelt runoff monitoring from high latitude basins: SAR aspects

Caves, R.G.; Turpin, Owen; Nägler, Thomas; Miller, Douglas K.

doi:10.1109/igarss.1998.703675

Cited by 7 publications

(4 citation statements)

References 2 publications

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“…Based on the conclusions of Nagler and Rott [4] and supported by the findings of Caves et al [10] there could be find three situation in each radar images scene:…”

Section: Snow Cover Mapping Using Radar Datamentioning

confidence: 62%

“…Backscatter coefficient thresholds are those proposed by Nagler and Rott [4], Caves et al [10], Pettinato et al [6], Pettina to et al [7], however the representation of the relationship between σ 0 and height using isoliness implifies the definition of new thresholds between classes. In this sense, it is possible to search for specific thresholds within each image thus contributing to the improved discrimination of classes.…”

Section: Surface Temperaturementioning

confidence: 99%

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Snow Cover Area Estimation Using Radar and Optical Satellite Information

Salcedo¹,

Cogliati²

2014

ACS

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Obtaining the seasonal variation of snow cover in areas of the Argentinian Andes is important for hydrological studies and can facilitate proper planning of water resources, with regard to irrigation, supply, flood attenuation and hydroelectricity. Remote sensors that work in the visible and infrared wavelength range are operational tools for monitoring the snow in clear skies. However, microwave satellites are able to obtain data regardless of atmospheric conditions. The advantage of using radar images is that they are very useful to obtain highly accurate parameters such as snow moisture depth, density and water equivalent resulting in improved forecasting models. In this paper, we analyze an ERS-2 image of the Andes mountain range in the northern region of the Neuquén province, Patagonia, Argentina. The objective was to obtain the spatial distribution of wet and dry snow and to compare these results with data from optical sensors (LANDSAT) in order to understand the topographic variables that influence the spatial distribution of wet snow. Optical information from sensors like LANDSAT TM 5 was analyzed to obtain fractional and binary snow indexes during a passage simultaneously with radar data. Surface temperature is used to study the association between the different types of snow altitudinal ranges and surface temperature. In this paper, we selected a scene on October 8th 2005. The entire methodology was systematized in a code implemented in IDL language.

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“…Based on the conclusions of Nagler and Rott [4] and supported by the findings of Caves et al [10] there could be find three situation in each radar images scene:…”

Section: Snow Cover Mapping Using Radar Datamentioning

confidence: 62%

Section: Surface Temperaturementioning

confidence: 99%

Snow Cover Area Estimation Using Radar and Optical Satellite Information

Salcedo¹,

Cogliati²

2014

ACS

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“…This snow mapping algorithm was further refined by Nagler (1996) to improve the accuracy for steep topography. The algorithm has been successfully applied for snow mapping with ERS SAR in northern Scotland and northern Sweden (Caves et al, 1998), and with slight modifications for an agricultural area in the southeastern part of Quebec, Canada (Baghdadi et al, 1997). A slightly different algorithm, also using multi-temporal SAR data, was applied by Koskinen et al (1997) to map snow cover in areas with different levels of forestation in Finland.…”

Section: Introductionmentioning

confidence: 99%

Snowmelt Runoff Modelling by Means of RADARSAT and ERS SAR

Nägler

Rott

Glendinning

2000

Canadian Journal of Remote Sensing

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RESUMEL' article decrit un algorithme pour la cartographie de la neige hum ide, qui se base sur la detection des changements sur des images radar a synthese d' ouverture (SAR) a repetition de passage. La methode est appliquee pour etablir des cartes de la couverture neigeuse de deux bassins versants dans les alpes autrichiennes, d'une topographie escarpe, a l'aide d'images SAR RADARSAT en mode faisceau S7 (angle de visee environ 40°) et d'images SAR du satellite europeen de teledetection (ERS) (angle de visee 19°). Les effets de la variation de I'angle de visee ainsi que les differences par rapport a la cartographie de la couverture neigeuse a l'aide de capteurs optiques sont etudies. II y a en general grande convergence entre les cartes de couverture neigeuse provenant de SAR et celles de Landsat-S Thematic Mapper, des differences sont observees surtout dans les zones acouvert nival fragmente. Les series chronologiques de cartes de couverture neigeuse apartir de RADARSAT et de SAR ERS sont utilisees comme entree pour le calcul de l' ecoulement journalier pendant le printemps et l' ete 1998. Les ecoulements simules et mesures s'accordent bien pendant la periode defonte des neiges, ce qui conjirme le grand potentiel de SAR pour l' application operationnelle dans l' hydrologie de la fonte des neiges. SUMMARY An algorithm for mapping wet snow is described which is based on change detection on repeat pass Synthetic Aperture Radar (SAR) images. The method is applied for producing snow maps in drainage basins in the Austrian Alps with steep topography, using RADARSAT SAR Beam Mode S7 images (mean look angle 40°) and European Remote Sensing Satellite (ERS) SAR images (mean look angle 20°). Ejfects of the different look angles, as well as differences to snow mapping with optical sensors are studied. The agreement between snow maps from SAR and Landsat-S Thematic Mapper is in general good. differences being observed mainly in the areas with broken snow cover. The time series of snow maps from RADARSAT and ERS SAR are used as inputfor calculating daily snowmelt runoff Examples of snowmelt runoff simulations are shown for spring and summer 199B. Simulated and measured runoff agree well during the snowmelt period, confirming the great potential ofSARfor operational applications in snowmelt hydrology,

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“…The importance of wet snow in hydrology is critical to the hydrological cycle, influencing the timing and amount Matthieu Gallet, Abdourrahmane Atto and Emmanuel Trouvé are with LISTIC (EA3703), University Savoie Mont-Blanc, FRANCE. Fatima Karbou with CNRM (UMR 3589), Centre d' Études de la Neige, FRANCE of spring snowmelt runoff [10]. The transformation of wet snow to liquid water can result in rapid and unpredictable fluctuations in river discharge, which has implications for the planning of natural resources and the management of water and energy resources [11] [12] [13].…”

Section: Introductionmentioning

confidence: 99%

Wet Snow Detection From Satellite SAR Images by Machine Learning With Physical Snowpack Model Labeling

Gallet,

Atto,

Karbou

et al. 2024

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

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The detection of wet snow by satellite imaging is currently done in an unsupervised way and lacks quantitative evaluation due to the difficulty of collecting ground truths in extreme environments. In this paper, we propose to take into account information associated with a physical model to label satellite data for the purpose of supervised learning of snow properties using Synthetic Aperture Radar (SAR) imagery. This dataset is constructed from Sentinel-1 SAR images, augmented with topographic information obtained from a Digital Elevation Model (DEM). The labelling of this data is done at the scale of the Northern Alps using the CROCUS physical snow model. Then, we trained, over thirteen combinations of labelled dataset, a wide range of machine learning models to quantitatively identify the most relevant learners for the wet snow detection task. The results demonstrate consistency among the different algorithms, with significant improvement observed when incorporating polarimetric combinations and topographic orientation data in the input of the model. The best algorithmic solution trained on this dataset is evaluated by comparing the obtained wet snow map over a validation area in the French massif of the Grandes Rousses with the existing Copernicus products, Fractional Snow Cover (FSC) and SAR Wet Snow (SWS). We also compare the temporal results obtained at one meteorological station located in the test area. The results show a better representation of wet snow during the melting period using the supervised learning approach, as well as a reduction in areas classified as wet during the winter season.

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The role of Earth Observation in snowmelt runoff monitoring from high latitude basins: SAR aspects

Cited by 7 publications

References 2 publications

Snow Cover Area Estimation Using Radar and Optical Satellite Information

Snow Cover Area Estimation Using Radar and Optical Satellite Information

Snowmelt Runoff Modelling by Means of RADARSAT and ERS SAR

Wet Snow Detection From Satellite SAR Images by Machine Learning With Physical Snowpack Model Labeling

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