Derivation of Snow Water Equivalent in Boreal Forests Using Microwave Radiometry
ABSTRACT. Efforts have been made by several investigators to produce a reliable global microwave snow algorithm to estimate snow depth or snow water equivalent (snow volume) and snow extent. Complications arise when trying to apply a global algorithm to specific regions where the climate, snowpack s...
| Main Authors: | , , , |
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| Format: | Text |
| Language: | English |
| Published: |
1991
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| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.112.5205 http://pubs.aina.ucalgary.ca/arctic/arctic44-s-147.pdf |
| Summary: | ABSTRACT. Efforts have been made by several investigators to produce a reliable global microwave snow algorithm to estimate snow depth or snow water equivalent (snow volume) and snow extent. Complications arise when trying to apply a global algorithm to specific regions where the climate, snowpack structure and vegetation vary. In forest regions, the microwave emission from dense coniferous forests may overwhelm the emission from the underlying snow-covered ground. As a result, algorithms employing microwave data tend to underestimate snow depths. Preliminary results indicate that the amount of underestimation can be minimized when the fraction of forest cover can be accounted for and used as an additional input in microwave algorithms. In the boreal forest of Saskatchewan, the standard error between the measured and the estimated snow water equivalent was reduced from 2.7 to 2.1 cm by using a generalized snow retrieval algorithm that includes the percentage of forest cover. However, perhaps as much as 25 % of the boreal forest of North America and Eurasia is too dense to enable satisfactory snow water equivalent determinations to be made using passive microwave techniques alone. Key words: brightness temperature, boreal forest, microwaves, radiometer, snowpack RÉSUMÉ. Plusieurs chercheurs ont tenté de créer un algorithme global fiable en vue d’estimer la profondeur de la neige ou l’équivalent d’eau de neige (volume nival) et l’étendue de neige à l’aide des micro-ondes. Les complications surgissent lorsqu’on essaye d’appliquer un algorithme global àdes régions spécifiques où le climat, la structure de la neige accumulée et la végétation varient. Dans les régions forestières, l’émission de micro-ondes provenant des forêts denses de conifkres peut prévaloir sur celle provenant du sol enfoui sous le couvert de neige. I1 en résulte que les algorithmes qui utilisent les données des micro-ondes tendent à sous-estimer la profondeur de la neige au sol. Des résultats |
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