Snow Monitoring Using Radar and Optical Satellite Data

Koskinen, J.; Metsämäki, Sari; Grandell, J.; Jänne, Sami; Matikainen, Leena; Hallikainen, M.

doi:10.1016/s0034-4257(99)00010-3

Cited by 38 publications

(17 citation statements)

References 7 publications

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“…Koskinen et al, 1999;Nagler & Rott, 2000). 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.…”

Section: Methodological Foundation Of New Algorithmsmentioning

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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Section: Methodological Foundation Of New Algorithmsmentioning

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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“…Using change detection methods, some repeat-pass spaceborne SAR systems are useful in regular snow cover monitoring, such as the ERS-1/2, JERS-1, and RADARSAT-1/2 (Koskinen et al, 1999;Nagler and Rott, 2000). In addition, SIR-C (Shi et al, 1997), ASAR (Kumar and Venkataraman, 2011) and other SAR data were commonly used in snow cover mapping.…”

Section: Introductionmentioning

confidence: 99%

Snow Cover Mapping and Ice Avalanche Monitoring From the Satellite Data of the Sentinels

Wang¹,

Yang²,

Zhou³

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In order to monitor ice avalanches efficiently under disaster emergency conditions, a snow cover mapping method based on the satellite data of the Sentinels is proposed, in which the coherence and backscattering coefficient image of Synthetic Aperture Radar (SAR) data (Sentinel-1) is combined with the atmospheric correction result of multispectral data (Sentinel-2). The coherence image of the Sentinel-1 data could be segmented by a certain threshold to map snow cover, with the water bodies extracted from the backscattering coefficient image and removed from the coherence segment result. A snow confidence map from Sentinel-2 was used to map the snow cover, in which the confidence values of the snow cover were relatively high. The method can make full use of the acquired SAR image and multispectral image under emergency conditions, and the application potential of Sentinel data in the field of snow cover mapping is exploited. The monitoring frequency can be ensured because the areas obscured by thick clouds are remedied in the monitoring results. The Kappa coefficient of the monitoring results is 0.946, and the data processing time is less than 2 h, which meet the requirements of disaster emergency monitoring.

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“…Several authors have shown the value of combining passive microwave and visible/infrared satellite data to map snow cover and monitor its evolution in time and space [1,[4][5][6][17][18][19][20][21]. Romanov et al [5] have shown that the multisensor technique is as precise as, or better than, IMS products, especially with respect to data consistency throughout the time series.…”

Section: Introductionmentioning

confidence: 99%

A Merging Algorithm for Regional Snow Mapping over Eastern Canada from AVHRR and SSM/I Data

2013

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Abstract:We present an algorithm for regional snow mapping that combines snow maps derived from Advanced Very High Resolution Radiometer (AVHRR) and Special Sensor Microwave/Imager (SSM/I) data. This merging algorithm combines AVHRR's moderate spatial resolution with SSM/I's ability to penetrate clouds and, thus, benefits from the advantages of the two sensors while minimizing their limitations. First, each of the two detection algorithms were upgraded before developing the methodology to merge the snow mapping results obtained using both algorithms. The merging methodology is based on a membership function calculated over a temporal running window of ±4 days from the actual date. The studied algorithms were developed and tested over Eastern Canada for the period from 1988 to 1999. The snow mapping algorithm focused on the spring melt season (1 April to 31 May). The snow maps were validated using snow depth observations from meteorological stations. The overall accuracy of the merging algorithm is about 86%, which is between that of the new versions of the two individual algorithms: AVHRR (90%) and SSM/I (83%). Furthermore, the algorithm was able to locate the end date of the snowmelt season with reasonable accuracy (bias = 0 days; SD = 11 days). Comparison of mapping results with high spatial resolution snow cover from Landsat imagery demonstrates the feasibility of clear-sky snow mapping with relatively good accuracy despite some underestimation of snow extent inherited from the AVHRR algorithm. It was found that the detection limit of the algorithm is 80% snow cover within a 1 × 1 km pixel. OPEN ACCESSRemote Sens. 2013, 5 5464 Keywords: snow cover; regional-scale snow mapping; multisensory snow product; optical and microwave satellite data; AVHRR; SSM/I; data fusion algorithm

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Snow Monitoring Using Radar and Optical Satellite Data

Cited by 38 publications

References 7 publications

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

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

Snow Cover Mapping and Ice Avalanche Monitoring From the Satellite Data of the Sentinels

A Merging Algorithm for Regional Snow Mapping over Eastern Canada from AVHRR and SSM/I Data

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