scholarly journals Snow cover data derived from MODIS for water balance applications

2009 ◽  
Vol 6 (1) ◽  
pp. 791-841 ◽  
Author(s):  
A. Gafurov ◽  
A. Bárdossy
Keyword(s):  
Snow Cover ◽  
Environmental Parameters ◽  
Mountainous Areas ◽  
Mountainous Regions ◽  
Available Energy ◽  
The North ◽  
Spatially Distributed ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Removal ◽  
High Elevations

Abstract. Snow cover information is of central importance for the estimation of water storage in cold mountainous regions. It is difficult to assess distributed snow cover information in a catchment in order to estimate possible water resources. It is especially a challenge to obtain snow cover information for high mountainous areas. Usually, snow depth is measured at meteorological stations, and it is relatively difficult to extrapolate this spatially or temporally since it highly depends on available energy and topography. The snow coverage of a catchment gives detailed information about the catchment's potential source for water. Many regions lack meteorological stations that measure snow, and usually no stations are available at high elevations. Satellite information is a very valuable source for obtaining several environmental parameters. One of the advantages is that the data is mostly provided in a spatially distributed format. This study uses satellite data to estimate snow coverage on high mountainous areas. Moderate-resolution Imaging Spectroradiometer (MODIS) snow cover data is used in the Kokcha Catchment located in the north-eastern part of Afghanistan. The main disadvantage of MODIS data that restricts its direct use in environmental applications is cloud coverage. This is why this study is focused on eliminating cloud covered cells and estimating cell information under cloud covered cells using six logical, spatial and temporal approaches. The results give total cloud removal and mapping of snow cover for the study areas.

scholarly journals Reanalysis of 47 Years of Climate in the French Alps (1958–2005): Climatology and Trends for Snow Cover

2009 ◽  
Vol 48 (12) ◽  
pp. 2487-2512 ◽  
Author(s):  
Yves Durand ◽  
Gérald Giraud ◽  
Martin Laternser ◽  
Pierre Etchevers ◽  
Laurent Mérindol ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Numerical Models ◽  
High Elevation ◽  
Climatic Conditions ◽  
Spatial And Temporal Variability ◽  
The South ◽  
French Alps ◽  
Mountainous Regions ◽  
The North

Abstract Since the early 1990s, Météo-France has used an automatic system combining three numerical models to simulate meteorological parameters, snow cover stratigraphy, and avalanche risk at various altitudes, aspects, and slopes for a number of mountainous regions (massifs) in the French Alps and the Pyrenees. This Système d’Analyse Fournissant des Renseignements Atmosphériques à la Neige (SAFRAN)–Crocus–Modèle Expert de Prévision du Risque d’Avalanche (MEPRA) model chain (SCM), usually applied to operational daily avalanche forecasting, is here used for retrospective snow and climate analysis. For this study, the SCM chain used both meteorological observations and guess fields mainly issued from the newly reanalyzed atmospheric model 40-yr ECMWF Re-Analysis (ERA-40) data and ran on an hourly basis over a period starting in the winter of 1958/59 until recent past winters. Snow observations were finally used for validation, and the results presented here concern only the main climatic features of the alpine modeled snowfields at different spatial and temporal scales. The main results obtained confirm the very significant spatial and temporal variability of the modeled snowfields with regard to certain key parameters such as those describing ground coverage or snow depth. Snow patterns in the French Alps are characterized by a marked declining gradient from the northwestern foothills to the southeastern interior regions. This applies mainly to both depths and durations, which exhibit a maximal latitudinal variation at 1500 m of about 60 days, decreasing strongly with the altitude. Enhanced at low elevations, snow depth shows a mainly negative temporal variation over the study period, especially in the north and during late winters, while the south exhibits more smoothed features. The number of days with snow on the ground shows also a significant general signal of decrease at low and midelevation, but this signal is weaker in the south than in the north and less visible at high elevation. Even if a statistically significant test cannot be performed for all elevations and areas, the temporal decrease is present in all the studied quantities. Concerning snow duration, this general decrease can also be interpreted as a sharp variation of the mean values at the end of the 1980s, inducing a step effect in its time series rather than a constant negative temporal trend. The results have also been interpreted in terms of potential for a viable ski industry, especially in the southern areas, and for different changing climatic conditions. Presently, French downhill ski resorts are economically viable from a range of about 1200 m MSL in the northern foothills to 2000 m in the south, but future prospects are uncertain. In addition, no clear and direct relationship between the North Atlantic Oscillation (NAO) or the ENSO indexes and the studied snow parameters could be established in this study.

A novel Machine Learning-based gap-filling of fine-resolution remotely sensed snow cover fraction data by combining downscaling and regression

2021 ◽  
Author(s):  
Soni Yatheendradas ◽  
Sujay Kumar
Keyword(s):  
Snow Cover ◽  
Earth Science ◽  
Super Resolution ◽  
Remotely Sensed ◽  
Gap Filling ◽  
Snow Cover Fraction ◽  
Data Gaps ◽  
Spatially Distributed ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Fine Resolution

AbstractSatellite-based remotely-sensed observations of snow cover fraction (SCF) can have data gaps in spatially distributed coverage from sensor and orbital limitations. We mitigate these limitations in the example fine-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) data by gap-filling using auxiliary 1-km datasets that either aid in downscaling from coarser-resolution (5 km) MODIS SCF wherever not fully covered by clouds, or else by themselves via regression wherever fully cloud-covered. This study’s prototype predicts a 1-km version of the 500 m MOD10A1 SCF target. Due to non-collocatedness of spatial gaps even across input and auxiliary datasets, we consider a recent gap-agnostic advancement of partial convolution in computer vision for both training and predictive gap-filling. Partial convolution accommodates spatially consistent gaps across the input images, effectively implementing a 2-dimensional masking. To overcome reduced usable data from non-collocated spatial gaps across inputs, we innovate a fully generalized 3-dimensional masking in this partial convolution. This enables a valid output value at a pixel even if only a single valid input variable and its value exist in the neighborhood covered by the convolutional filter zone centered around that pixel. Thus our gap-agnostic technique can use significantly more examples for training (~67%) and prediction (~100%), instead of only less than 10% for the previous partial convolution. We train an example simple 3-layer legacy Super-Resolution Convolutional Neural Network (SRCNN) to obtain downscaling and regression component performances that are better than baseline values of either climatology or MOD10C1 SCF as relevant. Our generalized partial convolution can enable multiple earth science applications like downscaling, regression, classification and segmentation that were hindered by data gaps.

scholarly journals PRACTISE – Photo Rectification And ClassificaTIon SoftwarE (V.1.0)

2013 ◽  
Vol 6 (3) ◽  
pp. 837-848 ◽  
Author(s):  
S. Härer ◽  
M. Bernhardt ◽  
J. G. Corripio ◽  
K. Schulz
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Digital Camera ◽  
Cost Effective ◽  
The North ◽  
Camera Image ◽  
Snow Cover Extent ◽  
Spatially Distributed ◽  
Camera Location ◽  
Elevation Model

Abstract. Terrestrial photography is a cost-effective and easy-to-use method for measuring and monitoring spatially distributed land surface variables. It can be used to continuously investigate remote and often inaccessible terrain. We focus on the observation of snow cover patterns in high mountainous areas. The high temporal and spatial resolution of the photographs have various applications, for example validating spatially distributed snow hydrological models. However, the analysis of a photograph requires a preceding georectification of the digital camera image. To accelerate and simplify the analysis, we have developed the "Photo Rectification And ClassificaTIon SoftwarE" (PRACTISE) that is available as a Matlab code. The routine requires a digital camera image, the camera location and its orientation, as well as a digital elevation model (DEM) as input. If the viewing orientation and position of the camera are not precisely known, an optional optimisation routine using ground control points (GCPs) helps to identify the missing parameters. PRACTISE also calculates a viewshed using the DEM and the camera position. The visible DEM pixels are utilised to georeference the photograph which is subsequently classified. The resulting georeferenced and classified image can be directly compared to other georeferenced data and can be used within any geoinformation system. The Matlab routine was tested using observations of the north-eastern slope of the Schneefernerkopf, Zugspitze, Germany. The results obtained show that PRACTISE is a fast and user-friendly tool, able to derive the microscale variability of snow cover extent in high alpine terrain, but can also easily be adapted to other land surface applications.

scholarly journals Snow-cover simulations in mountainous regions based on general circulation model outputs

1997 ◽  
Vol 25 ◽  
pp. 42-45
Author(s):  
Eric Martin ◽  
Eric Brun ◽  
Yves Durand
Keyword(s):  
Snow Cover ◽  
General Circulation Model ◽  
General Circulation ◽  
Circulation Model ◽  
Mountainous Regions ◽  
Temperature And Precipitation ◽  
Snow Model ◽  
High Elevations ◽  
Model Temperature ◽  
Meteorological Analysis

A meteorological-analysis procedure allowing simulation of the snow cover in mountainous regions, where the general circulation model orography differs from the real orography, is described. This procedure uses model outputs to estimate the data needed to force a snow model. Temperature and precipitation deduced from three five-year runs were compared to the snow climatology of the French Alps. The snow cover simulations are very sensitive to temperature at middle elevations. At high elevations, the altitude of the equilibrium line is simulated well.

scholarly journals PRACTISE – Photo Rectification And ClassificaTIon SoftwarE (V.1.0)

2013 ◽  
Vol 6 (1) ◽  
pp. 171-202
Author(s):  
S. Härer ◽  
M. Bernhardt ◽  
J. G. Corripio ◽  
K. Schulz
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Digital Camera ◽  
Cost Effective ◽  
Image Plane ◽  
Western Slope ◽  
The North ◽  
Snow Cover Extent ◽  
Spatially Distributed ◽  
The Status

Abstract. Terrestrial photography is a cost-effective and easy-to-use method to derive the status of spatially distributed land surface parameters. It can be used to continuously investigate remote and often inaccessible terrain. We focus on the observation of snow cover patterns in high mountainous areas. The high temporal and spatial resolution of the photographs have various applications, e.g. validating spatially distributed snow hydrological models. However, a one to one analysis of projected model results to photographs requires a preceding georectification of the digital camera images. To accelerate and simplify the analysis, we have developed the "Photo Rectification And ClassificaTIon SoftwarE" (PRACTISE) that is available as a Matlab code. The routine requires a digital camera image, the camera location and its orientation, as well as a digital elevation model (DEM) as input. In case of an unknown viewing orientation an optional optimisation routine using ground control points (GCPs) helps to identify the missing parameters. PRACTISE also calculates a viewshed using the DEM and the camera position and it projects the visible DEM pixels to the image plane where they are subsequently classified. The resulting projected and classified image can be directly compared to other projected data and can be used within any geoinformation system. The Matlab routine was tested using observations of the north western slope of the Schneefernerkopf, Zugspitze, Germany. The obtained results have shown that PRACTISE is a fast and user-friendly tool, able to derive the microscale variability of snow cover extent in high alpine terrain, but can also easily be adapted to other land surface applications.

scholarly journals Snow-cover reconstruction methodology for mountainous regions based on historic in situ observations and recent remote sensing data

2015 ◽  
Vol 9 (2) ◽  
pp. 451-463 ◽  
Author(s):  
A. Gafurov ◽  
S. Vorogushyn ◽  
D. Farinotti ◽  
D. Duethmann ◽  
A. Merkushkin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Remote Sensing Data ◽  
Snow Cover Area ◽  
Sensing Data ◽  
Mountainous Regions ◽  
Snow Cover Extent ◽  
Spatially Distributed ◽  
Data Gap ◽  
Availability Assessment

Abstract. Spatially distributed snow-cover extent can be derived from remote sensing data with good accuracy. However, such data are available for recent decades only, after satellite missions with proper snow detection capabilities were launched. Yet, longer time series of snow-cover area are usually required, e.g., for hydrological model calibration or water availability assessment in the past. We present a methodology to reconstruct historical snow coverage using recently available remote sensing data and long-term point observations of snow depth from existing meteorological stations. The methodology is mainly based on correlations between station records and spatial snow-cover patterns. Additionally, topography and temporal persistence of snow patterns are taken into account. The methodology was applied to the Zerafshan River basin in Central Asia – a very data-sparse region. Reconstructed snow cover was cross validated against independent remote sensing data and shows an accuracy of about 85%. The methodology can be used in mountainous regions to overcome the data gap for earlier decades when the availability of remote sensing snow-cover data was strongly limited.

scholarly journals Cloud removal methodology from MODIS snow cover product

2009 ◽  
Vol 13 (7) ◽  
pp. 1361-1373 ◽  
Author(s):  
A. Gafurov ◽  
A. Bárdossy
Keyword(s):  
Snow Cover ◽  
Spatial Information ◽  
Ground Truth ◽  
Vegetation Period ◽  
Cloud Coverage ◽  
Average Accuracy ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Removal

Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) employed by Terra and Aqua satellites provides spatially snow covered data with 500 m and daily temporal resolution. It delivers public domain data in raster format. The main disadvantage of the MODIS sensor is that it is unable to record observations under cloud covered regions. This is why this study focuses on estimating the pixel cover for cloud covered areas where no information is available. Our step to this product involves employing methodology based on six successive steps that estimate the pixel cover using different temporal and spatial information. The study was carried out for the Kokcha River basin located in northeastern part of Afghanistan. Snow coverage in catchments, like Kokcha, is very important where the melt-water from snow dominates the river discharge in vegetation period for irrigation purposes. Since no snow related observations were available from the region, the performance of the proposed methodology was tested using the cloud generated MODIS snow cover data as possible "ground truth" information. The results show successful performances arising from the methods applied, which resulted in all cloud coverage being removed. A validation was carried out for all subsequent steps, to be outlined below, where each step removes progressively more cloud coverage. Steps 2 to 5 (step 1 was not validated) performed very well with an average accuracy of between 90–96%, when applied one after another for the selected valid days in this study. The sixth step was the least accurate at 78%, but it led to the removal of all remaining cloud cover.

scholarly journals Assimilating MODIS and AMSR-E Snow Observations in a Snow Evolution Model

2012 ◽  
Vol 13 (5) ◽  
pp. 1475-1492 ◽  
Author(s):  
Steven J. Fletcher ◽  
Glen E. Liston ◽  
Christopher A. Hiemstra ◽  
Steven D. Miller
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Evolution Model ◽  
The South ◽  
Earth Observing System ◽  
The North ◽  
Moderate Resolution ◽  
Snow Water ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract In this paper four simple computationally inexpensive, direct insertion data assimilation schemes are presented, and evaluated, to assimilate Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover, which is a binary observation, and Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) snow water equivalent (SWE) observations, which are at a coarser resolution than MODIS, into a numerical snow evolution model. The four schemes are 1) assimilate MODIS snow cover on its own with an arbitrary 0.01 m added to the model cells if there is a difference in snow cover; 2) iteratively change the model SWE values to match the AMSR-E equivalent value; 3) AMSR-E scheme with MODIS observations constraining which cells can be changed, when both sets of observations are available; and 4) MODIS-only scheme when the AMSR-E observations are not available, otherwise scheme 3. These schemes are used in the winter of 2006/07 over the southeast corner of Colorado and the tri-state area: Wyoming, Colorado, and Nebraska. It is shown that the inclusion of MODIS data enables the model in the north domain to have a 15% improvement in number of days with a less than 10% disagreement with the MODIS observation 24 h later and approximately 5% for the south domain. It is shown that the AMSR-E scheme has more of an impact in the south domain than the north domain. The assimilation results are also compared to station snow-depth data in both domains, where there is up-to-a-factor-of-5 underestimation of snow depth by the assimilation schemes compared with the station data but the snow evolution is fairly consistent.

scholarly journals Updating a Land Surface Model with MODIS-Derived Snow Cover

2004 ◽  
Vol 5 (6) ◽  
pp. 1064-1075 ◽  
Author(s):  
M. Rodell ◽  
P. R. Houser
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Land Surface Model ◽  
Surface Air Temperature ◽  
Surface Model ◽  
Mountainous Regions ◽  
Snow Water ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Land Data Assimilation System

Abstract A simple scheme for updating snow-water storage in a land surface model using snow cover observations is presented. The scheme makes use of snow cover observations retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA's Terra and Aqua satellites. Simulated snow-water equivalent is adjusted when and where the model and MODIS observation differ, following an internal accounting of the observation quality, by either removing the simulated snow or adding a thin layer. The scheme is tested in a 101-day global simulation of the Mosaic land surface model driven by the NASA/NOAA Global Land Data Assimilation System. Output from this simulation is compared to that from a control (not updated) simulation, and both are assessed using a conventional snow cover product and data from ground-based observation networks over the continental United States. In general, output from the updated simulation displays more accurate snow coverage and compares more favorably with in situ snow time series. Both the control and updated simulations have serious deficiencies on occasion and in certain areas when and where the precipitation and/or surface air temperature forcing inputs are unrealistic, particularly in mountainous regions. Suggestions for developing a more sophisticated updating scheme are presented.

scholarly journals Recent fluctuation of snow cover on mountainous areas in Japan

2001 ◽  
Vol 32 ◽  
pp. 97-101 ◽  
Author(s):  
Masujiro Shimizu ◽  
Osamu Abe
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Global Solar Radiation ◽  
High Elevation ◽  
Meteorological Conditions ◽  
Mountainous Areas ◽  
The North ◽  
Observation Network ◽  
Snow Conditions ◽  
The Relationship

AbstractTo monitor the snow-cover distribution in relation to meteorological conditions on high mountainous areas in Japan, NIED constructed a snow-observation network which it has operated for approximately 10 years. The network consists of seven pairs of stations, each comprising a mountainous site and a low-lying flatland site, from Hokkaido district in the north to San-in in the southwest. Data obtained include snow depth, snow weight, air temperature and global solar radiation. This study presents recent fluctuation of snow cover on mountainous areas for several recent winters in Japan. Most winters were warmer than average, but winter 1995/96 was normal, and maximum snow depths were recorded in high-elevation areas. Seventy-seven avalanche accidents occurred in winter 1995/96. The relationship between meteorological and snow conditions in mountainous areas and flatland areas was analyzed.

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