Estimating drainage area-based snow-cover percentages from NOAA AVHRR images

2002 ◽  
Vol 23 (15) ◽  
pp. 2971-2988 ◽  
Author(s):  
L. Matikainen ◽  
R. Kuittinen ◽  
J. Vepsäläinen
Keyword(s):  
Snow Cover ◽  
Drainage Area ◽  
Noaa Avhrr

The use of Satellite Imagery in Investigating the Dynamics of the Ice and Snow Cover Lake Baikal

2003 ◽  
Vol 34 (1-2) ◽  
pp. 33-50 ◽  
Author(s):  
S.V. Semovski ◽  
N. Yu Mogilev
Keyword(s):  
Snow Cover ◽  
Lake Baikal ◽  
Satellite Imagery ◽  
Ice Cover ◽  
Unfrozen Water ◽  
Processing Strategy ◽  
Noaa Avhrr ◽  
Satellite Information ◽  
Process Studies ◽  
Snow And Ice

The generation and sample applications of a set of multispectral remotely sensed products for investigations of Lake Baikal's ice cover variability are described. During the period from mid-January to the end of April, the lake is completely covered with ice, and by analyzing satellite information it is possible to investigate in detail the distribution and dynamics of the main types of snow and ice cover. Different ice cover classes and unfrozen water distributions are estimated from calibrated and navigated NOAA AVHRR 1.1-km imagery of Lake Baikal for January 1994 through May 1999. The processing strategy and characteristics of the products are reviewed. The utility of this type of multiparameter dataset for modelling applications and process studies is discussed. ERS SAR and Resurs images are used for detailed representation of different ice classes distributions.

Spatiotemporal patterns of snow cover retrieved from NOAA-AVHRR LTDR: a case study in the Tibetan Plateau, China

2016 ◽  
Vol 10 (5) ◽  
pp. 504-521 ◽  
Author(s):  
Siyuan Wang ◽  
Hang Yin ◽  
Qichun Yang ◽  
Hui Yin ◽  
Xiaoyue Wang ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
Spatiotemporal Patterns ◽  
The Tibetan Plateau ◽  
Noaa Avhrr

scholarly journals Application of Satellite Data to Monitoring Snow Cover and Runoff in Greenland

1988 ◽  
Vol 19 (4) ◽  
pp. 225-236 ◽  
Author(s):  
Henrik Søgaard ◽  
Thorkild Thomsen
Keyword(s):  
Snow Cover ◽  
Satellite Data ◽  
Power Production ◽  
Field Observations ◽  
Drainage Basins ◽  
Simulation Data ◽  
Hydrological Simulation ◽  
Hydro Power ◽  
Degree Day ◽  
Noaa Avhrr

Based on NOAA-AVHRR satellite data and runoff records from one of the major drainage basins in Western Greenland methods for monitoring snow cover, snowpack water equivalent and runoff have been elaborated and evaluated by use of field observations and hydrological simulation. Data from six years and more than 40 satellite scenes have been used in the analysis. A procedure for snow cover mapping in areas with alpine relief is presented, and it is shown that the snowpack water equivalent can be derived by applying either a hydrological simulation or a degree-day approach. Finally, the applicability of the results with respect to hydro-power production in Greenland is discussed.

scholarly journals Verification of simulated snow cover in an Arctic basin using satellite-derived snow-cover maps

2000 ◽  
Vol 31 ◽  
pp. 391-396 ◽  
Author(s):  
O. C. Turpin ◽  
R. G. Caves ◽  
R. I. Ferguson ◽  
B. Johansson
Keyword(s):  
Snow Cover ◽  
Arctic Basin ◽  
Synthetic Aperture ◽  
Model Parameters ◽  
Landsat Thematic Mapper ◽  
Snow Cover Area ◽  
Noaa Avhrr ◽  
Mountainous Basin ◽  
Very High ◽  
Image Type

AbstractTime series of Earth observation (EO) data (Landsat Thematic Mapper (TM), U.S. National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) and European Remote-sensing Satellite synthetic-aperture radar (ERS SAR)) were obtained for a 2250 km2 mountainous basin in northern Sweden to validate snow-cover area (SCA) estimates produced by a conceptual model (HBV) during three melt seasons. SCA depletion curves derived for each image type, arid coincident images, reveal that the SCA estimate varies with the sensor. Discrepancies betweenTM and AVHRR appear to be an effect of spatial resolution. However, differences betweenTM and SAR are not simply related. Since more AVHRR thanTM data were available, a TM-equivalent SCA was derived from AVHRR by relating TM SCA to AVHRR channel 1 reflectance. The TM-equivalent SCA was used to test SCA simulated by HBV for the 1992 melt season. Although the modelled and TM-equivalent SCA were in reasonable agreement, the modelled SCA declined faster than the TM-equivalent SCA. Partial recalibration of model parameters controlling snowpack accumulation improved the match between the modelled and EO-derived SCA decline. The recalibrated parameters were verified using SCA maps generated for the 1996 and 1998 melt seasons. The adjusted parameter sets had little effect on the Nash-Sutcliffe R2 runoff fit but improved the volume fit in all three years.

scholarly journals Operational sub-pixel snow mapping over the Alps with NOAA AVHRR data

2004 ◽  
Vol 38 ◽  
pp. 245-252 ◽  
Author(s):  
Nando Foppa ◽  
Stefan Wunderle ◽  
David Oesch ◽  
Florian Kuchen
Keyword(s):  
Snow Cover ◽  
Real Time ◽  
Principal Component ◽  
European Alps ◽  
Complex Topography ◽  
Heterogeneous Terrain ◽  
Noaa Avhrr ◽  
Research Activities ◽  
The Alps ◽  
Pixel Scale

AbstractThis study is part of research activities concentrating on the real-time application of the U.S. National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) sensor for snow-cover analysis of the European Alps. For mapping snow cover in heterogeneous terrain, we implement the widely used linear spectral mixture algorithm to estimate snow cover at sub-pixel scale. Principal component analysis, including the reflective part of AVHRR channel 3, is used to estimate fractions of “snow” and “not snow” within a pixel, using linear mixture modeling. The combination of these features leads to a fast, simple solution for operational and near-real-time processing. The presented algorithm is applied on the European Alps on 17 January 2003 and successfully maps snow at sub-pixel scale. The detailed snow-cover information makes it easy to recognize the complex topography of the Alps, more so than with either a classic binary map or a Moderate Resolution Imaging Spectroradiometer (MODIS) snow product. The sub-pixel algorithm reasonably identifies snow-cover fractions in regions and at altitudes where neither the classic binary map nor the MODIS algorithm detects any snow. Differences concerning the snow distribution are found in forested areas as well as in the lowest-elevation zones. The algorithm substantially improves snow mapping over complex topography for operational and near-realtime applications.

Sign in / Sign up

Export Citation Format

Share Document