Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data

Hall, Dorothy K.; Riggs, George A.; Salomonson, V. V.

doi:10.1016/0034-4257(95)00137-p

Cited by 1,090 publications

(718 citation statements)

References 21 publications

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“…Thus, clouds, shadows, and snow cover were not major constraints in the image processing. For the snow mask, we created the Normalized Difference Snow Index (NDSI) [17] image to distinguish snow from other surrounding features. A threshold was applied to the NDSI to filter the non-snow features that may have been misclassified as snow by examining reflectance at other wavelengths.…”

Section: Methodsmentioning

confidence: 99%

“…Overall, 97% of the areas covered with vegetation were at elevations 4600 m (15,092 ft) amsl and below. (14,764) 14.47 4600 (15,092) 3.47 4700 (15,420) 0.82 4800 (15,748) 0.07 4900 (16,076) 0.03 5000 (16,404) 0.01 5200 (17,060) 0.90 In Table 3, about 42.28% of the total vegetation cover was within 0.5 mi (0.80 km) from the river. This percentage was further classified in Table 5.…”

Section: Vegetation Covermentioning

confidence: 98%

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Geographic Layers as Landscape Drivers for the Marco Polo Argali Habitat in the Southeastern Pamir Mountains of Tajikistan

Salas

Valdez

Boykin

2015

IJGI

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Abstract:We described in this report the essential geographic layers used as landscape drivers for the Marco Polo Argali habitat in the eastern Tajik Pamirs. Using remote sensing techniques and geographic information systems (GIS), individual layers were generated in order to acquire more information on argali patterns and habitat suitability and to make the dataset available online. We introduced an improved object-based image analysis in our mapping of the vegetation cover by utilizing spectral, topographic, and texture variables. We exhausted every Landsat image band and texture feature combination to select the best pairing of band-texture components. For vegetation class alone, the producer's accuracy was 90.8% and the user's accuracy was 91.6%.

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Section: Methodsmentioning

confidence: 99%

Section: Vegetation Covermentioning

confidence: 98%

Geographic Layers as Landscape Drivers for the Marco Polo Argali Habitat in the Southeastern Pamir Mountains of Tajikistan

Salas

Valdez

Boykin

2015

IJGI

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“…The suite of MODIS snow cover products, available since 2000, are derived using a fully-automated algorithm that provides good spatial resolution (500 m), cloud detection, and frequent coverage (daily at mid to high latitudes) Hall et al, 1995Hall et al, , 2002Riggs et al, 2006). The MODIS snow-mapping algorithm uses a normalized difference between MODIS band 4 (5.45-5.65 mm) and 6 (1.628-1.652 lm) and many additional spectral and threshold tests.…”

Section: The Moderate Resolution Imaging Spectroradiometer (Modis)mentioning

confidence: 99%

A review of global satellite-derived snow products

Frei

Tedesco

Lee

et al. 2012

Advances in Space Research

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271

184

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Snow cover over the Northern Hemisphere plays a crucial role in the Earth's hydrology and surface energy balance, and modulates feedbacks that control variations of global climate. While many of these variations are associated with exchanges of energy and mass between the land surface and the atmosphere, other expected changes are likely to propagate downstream and affect oceanic processes in coastal zones. For example, a large component of the freshwater flux into the Arctic Ocean comes from snow melt. The timing and magnitude of this flux affects biological and thermodynamic processes in the Arctic Ocean, and potentially across the globe through their impact on North Atlantic Deep Water formation.Several recent global remotely sensed products provide information at unprecedented temporal, spatial, and spectral resolutions. In this article we review the theoretical underpinnings and characteristics of three key products. We also demonstrate the seasonal and spatial patterns of agreement and disagreement amongst them, and discuss current and future directions in their application and development. Though there is general agreement amongst these products, there can be disagreement over certain geographic regions and under conditions of ephemeral, patchy and melting snow.

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“…On MODIS this algorithm will allow mapping of global snow cover automatically at a 500-m scale [Hall et al, 1995a]. Maps can be created daily, cloud cover permitting.…”

Section: Surface Temperaturementioning

confidence: 99%

Measuring snow and glacier ice properties from satellite

König¹,

Winther²,

Isaksson³

2001

Reviews of Geophysics

246

159

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Abstract. Satellite remote sensing is a convenient tool for studying snow and glacier ice, allowing us to conduct research over large and otherwise inaccessible areas. This paper reviews various methods for measuring snow and glacier ice properties with satellite remote sensing. These methods have been improving with the use of new satellite sensors, like the synthetic aperture radar (SAR) during the last decade, leading to the development of new and powerful methods, such as SAR interferometry for glacier velocity, digital elevation model generation of ice sheets, or snow cover mapping. Some methods still try to overcome the limitations of present sensors, but future satellites will have much increased capability, for example, the ability to measure the whole optical spectrum or SAR sensors with multiple polarization or frequencies. Among the methods presented are the satellite-derived determination of surface albedo, snow extent, snow volume, snow grain size, surface temperature, glacier facies, glacier velocities, glacier extent, and ice sheet topography. In this review, emphasis is put on the principles and theory of each satellite remote sensing method. An extensive list of references, with an emphasis on studies from the 1990s, allows the reader to delve into specific topics. INTRODUCTIONThere have been tremendous technological achievements in the twentieth century that enable scientists to undertake research at virtually every spot on Earth. In parallel, advances in space technology during the last decades have provided us with a rapidly increasing number of satellite platforms that can be used to study complex physical processes of the Earth-atmosphere system. The development within this field concerns not only the growing number of satellites but also the rapid progression of sensor capabilities. In the future a major challenge will be connected with combining various sources of information gathered from space, i.e., data assimilation, and to make use of this information in a systematic, repetitive manner to monitor temporal and spatial variability, for example, in climate-change research.In the field of glaciology, satellite remote sensing has proven to be a particularly useful tool because areas of interest are often inaccessible. Further, in many regions at high latitudes, like the Greenland and Antarctic ice sheets, it is only during parts of the year that effective ground-based research can be carried out due to the harsh climate environment and the lack of daylight. Satellite remote sensing often permits real-time, yearround, and long-term studies. Also, the large spatial coverage of satellite remotely sensed data enables monitoring and process studies over large areas. In this way, satellite data help in understanding processes and teleconnections on the regional, continental, or even global scale, for example, global satellite-derived maps of snow cover. Such products are particularly important because they assist interpretation and analysis concerning global change.Also, on smaller scales, sate...

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Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data

Cited by 1,090 publications

References 21 publications

Geographic Layers as Landscape Drivers for the Marco Polo Argali Habitat in the Southeastern Pamir Mountains of Tajikistan

Geographic Layers as Landscape Drivers for the Marco Polo Argali Habitat in the Southeastern Pamir Mountains of Tajikistan

A review of global satellite-derived snow products

Measuring snow and glacier ice properties from satellite

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