A prototype MODIS-SSM/I snow-mapping algorithm

Tait, Andrew; Barton, Jonathan S.; Hall, Dorothy K.

doi:10.1080/01431160010031261

Cited by 9 publications

(7 citation statements)

References 21 publications

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“…These are not hindered by clouds and can be used to obtain snow cover maps with reduced cloud cover (e.g. Robinson et al, 1984;Romanov et al, 2000;Tait et al, 2001). For the purpose of intra-day detection of snow cover, it would be very interesting to have microwave sensors available on board of the Meteosat satellites, as is being investigated for the future Meteosat Third Generation mission.…”

Section: Discussionmentioning

confidence: 98%

Operational snow mapping using multitemporal Meteosat SEVIRI imagery

Wildt

Seiz

Gruen

2007

Remote Sensing of Environment

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Section: Discussionmentioning

confidence: 98%

Operational snow mapping using multitemporal Meteosat SEVIRI imagery

Wildt

Seiz

Gruen

2007

Remote Sensing of Environment

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“…After this initial assignment, dense coniferous forests and water bodies are also corrected because they may fall into this NDSI range as well. Some snow may actually have an NDSI of less than 0.4 and these errors are corrected using a method combining NDSI and NDVI to distinguish between snow-covered and snow-free forests (Tiat et al, 2001;Klein and Barnett, 2003).…”

Section: Normalized Difference Snow Index (Ndsi) To Create Modis Dailmentioning

confidence: 99%

“…The scoring algorithm is used to select an observation for 1 day (Tiat et al, 2001). The object of the scoring is to select the observation closest to local noon time (highest solar elevation angle), nearest to nadir, and with greatest coverage that was mapped into the grid cell in order to prevent viewing angle problems (Riggs et al, 2006).…”

Section: Normalized Difference Snow Index (Ndsi) To Create Modis Dailmentioning

confidence: 99%

“…The object of the scoring is to select the observation closest to local noon time (highest solar elevation angle), nearest to nadir, and with greatest coverage that was mapped into the grid cell in order to prevent viewing angle problems (Riggs et al, 2006). No information on the snow cover is obtained when it is dark because the visible spectrum is required (Tiat et al, 2001).…”

Section: Normalized Difference Snow Index (Ndsi) To Create Modis Dailmentioning

confidence: 99%

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Using MODIS snow cover and precipitation data to model water runoff for the Mokelumne River Basin in the Sierra Nevada, California (2000–2009)

Powell

Blesius

Davis

et al. 2011

Global and Planetary Change

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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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A prototype MODIS-SSM/I snow-mapping algorithm

Cited by 9 publications

References 21 publications

Operational snow mapping using multitemporal Meteosat SEVIRI imagery

Operational snow mapping using multitemporal Meteosat SEVIRI imagery

Using MODIS snow cover and precipitation data to model water runoff for the Mokelumne River Basin in the Sierra Nevada, California (2000–2009)

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

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