scholarly journals A probability of snow approach to removing cloud cover from MODIS Snow Cover Area products

2012 ◽  
Vol 9 (12) ◽  
pp. 13693-13728 ◽  
Author(s):  
V. López-Burgos ◽  
H. V. Gupta ◽  
M. Clark
Keyword(s):  
Snow Cover ◽  
Satellite Remote Sensing ◽  
Cloud Cover ◽  
Spatial Interpolation ◽  
Similar Degree ◽  
Step Method ◽  
Snow Cover Area ◽  
Rule Based ◽  
Salt River ◽  
Spatially Distributed

Abstract. Satellite remote sensing can be used to investigate spatially distributed hydrological states and fluxes for use in modeling, assessment and management. However, in the visual wavelengths, cloud cover can often obscure significant portions of the images. This study develops a rule-based, multi-step method for removing clouds from MODIS Snow Cover Area (SCA) images. The methods used include a combining images from more than one satellite, time interpolation, spatial interpolation, and estimation of the probability of snow occurrence based on topographic information. Applied over the Upper Salt River Basin in Arizona, the method reduced the degree of cloud obscuration by 93.8% while maintaining a similar degree of image accuracy to that of the original images.

scholarly journals Reducing cloud obscuration of MODIS snow cover area products by combining spatio-temporal techniques with a probability of snow approach

2013 ◽  
Vol 17 (5) ◽  
pp. 1809-1823 ◽  
Author(s):  
V. López-Burgos ◽  
H. V. Gupta ◽  
M. Clark
Keyword(s):  
Snow Cover ◽  
Satellite Remote Sensing ◽  
Spatial Interpolation ◽  
Multistep Method ◽  
Similar Degree ◽  
Snow Cover Area ◽  
Rule Based ◽  
Salt River ◽  
Spatially Distributed ◽  
Spatio Temporal

Abstract. Satellite remote sensing can be used to investigate spatially distributed hydrological states for use in modeling, assessment, and management. However, in the visual wavelengths, cloud cover can often obscure significant portions of the images. This study develops a rule-based, multistep method for removing clouds from MODIS snow cover area (SCA) images. The methods used include combining images from more than one satellite, time interpolation, spatial interpolation, and estimation of the probability of snow occurrence based on topographic information. Applied over the upper Salt River basin in Arizona, the method reduced the degree of cloud obscuration by 93.8%, while maintaining a similar degree of image accuracy to that of the original images.

scholarly journals MODIS Does Not Capture the Spatial Heterogeneity of Snow Cover Induced by Solar Radiation

2021 ◽  
Vol 9 ◽  
Author(s):  
Hafsa Bouamri ◽  
Christophe Kinnard ◽  
Abdelghani Boudhar ◽  
Simon Gascoin ◽  
Lahoucine Hanich ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Spatial Variability ◽  
Snow Cover ◽  
Model Performance ◽  
Added Value ◽  
Snow Cover Area ◽  
Temperature Index ◽  
Mountain Ranges ◽  
Clear Sky ◽  
Spatially Distributed

Estimating snowmelt in semi-arid mountain ranges is an important but challenging task, due to the large spatial variability of the snow cover and scarcity of field observations. Adding solar radiation as snowmelt predictor within empirical snow models is often done to account for topographically induced variations in melt rates. This study examines the added value of including different treatments of solar radiation within empirical snowmelt models and benchmarks their performance against MODIS snow cover area (SCA) maps over the 2003-2016 period. Three spatially distributed, enhanced temperature index models that, respectively, include the potential clear-sky direct radiation, the incoming solar radiation and net solar radiation were compared with a classical temperature-index (TI) model to simulate snowmelt, SWE and SCA within the Rheraya basin in the Moroccan High Atlas Range. Enhanced models, particularly that which includes net solar radiation, were found to better explain the observed SCA variability compared to the TI model. However, differences in model performance in simulating basin wide SWE and SCA were small. This occurs because topographically induced variations in melt rates simulated by the enhanced models tend to average out, a situation favored by the rather uniform distribution of slope aspects in the basin. While the enhanced models simulated more heterogeneous snow cover conditions, aggregating the simulated SCA from the 100 m model resolution towards the MODIS resolution (500 m) suppresses key spatial variability related to solar radiation, which attenuates the differences between the TI and the radiative models. Our findings call for caution when using MODIS for calibration and validation of spatially distributed snow models.

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 Snow cover reconstruction methodology based on historic in situ observations and recent remote sensing data

2014 ◽  
Vol 8 (5) ◽  
pp. 4645-4680
Author(s):  
A. Gafurov ◽  
S. Vorogushyn ◽  
A. Merkushkin ◽  
D. Duethmann ◽  
D. Farinotti ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Remote Sensing Data ◽  
Snow Cover Area ◽  
Sensing Data ◽  
Snow Cover Extent ◽  
Spatially Distributed ◽  
Temporal Persistence ◽  
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 (SCA) 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 to overcome the data gap for earlier decades when the availability of remote sensing snow cover data was strongly limited.

scholarly journals The Potential of Satellite Remote Sensing of Snow over Great Britain in Relation to Cloud Cover

1994 ◽  
Vol 25 (1-2) ◽  
pp. 39-52 ◽  
Author(s):  
D.R. Archer ◽  
J.O. Bailey ◽  
E.C. Barrett ◽  
D. Greenhill
Keyword(s):  
Snow Cover ◽  
Satellite Remote Sensing ◽  
Cloud Cover ◽  
Satellite Images ◽  
Snow Accumulation ◽  
Satellite Monitoring ◽  
Infrared Images ◽  
Spectral Bands ◽  
The United Kingdom ◽  
Associated Conditions

Whilst satellite monitoring of snow cover is already operational in some countries, the maritime climate of the United Kingdom poses special problems for assessment of snow cover by satellite, including the short snow duration, its intermittent occurrence and associated conditions of cloud. Both satellite and ground-based observations of cloud have been used to assess the limitations imposed by cloud over broad regions and also for individual sites at different elevations and during periods of snow accumulation, stability and ablation. It is concluded that satellite sensing based on visible and infrared images alone is restricted by cloud cover, but can often provide helpful ancillary information in support of ground based measurements and satellite images from other spectral bands.

scholarly journals A Response of Snow Cover to the Climate in the Northwest Himalaya (NWH) Using Satellite Products

2021 ◽  
Vol 13 (4) ◽  
pp. 655
Author(s):  
Animesh Choudhury ◽  
Avinash Chand Yadav ◽  
Stefania Bonafoni
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Significant Negative Correlation ◽  
Shuttle Radar Topography Mission ◽  
Vital Role ◽  
Snow Cover Area ◽  
Study Region ◽  
Northwest Himalaya ◽  
The Past ◽  
Average Annual Temperature

The Himalayan region is one of the most crucial mountain systems across the globe, which has significant importance in terms of the largest depository of snow and glaciers for fresh water supply, river runoff, hydropower, rich biodiversity, climate, and many more socioeconomic developments. This region directly or indirectly affects millions of lives and their livelihoods but has been considered one of the most climatically sensitive parts of the world. This study investigates the spatiotemporal variation in maximum extent of snow cover area (SCA) and its response to temperature, precipitation, and elevation over the northwest Himalaya (NWH) during 2000–2019. The analysis uses Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra 8-day composite snow Cover product (MOD10A2), MODIS/Terra/V6 daily land surface temperature product (MOD11A1), Climate Hazards Infrared Precipitation with Station data (CHIRPS) precipitation product, and Shuttle Radar Topography Mission (SRTM) DEM product for the investigation. Modified Mann-Kendall (mMK) test and Spearman’s correlation methods were employed to examine the trends and the interrelationships between SCA and climatic parameters. Results indicate a significant increasing trend in annual mean SCA (663.88 km2/year) between 2000 and 2019. The seasonal and monthly analyses were also carried out for the study region. The Zone-wise analysis showed that the lower Himalaya (184.5 km2/year) and the middle Himalaya (232.1 km2/year) revealed significant increasing mean annual SCA trends. In contrast, the upper Himalaya showed no trend during the study period over the NWH region. Statistically significant negative correlation (−0.81) was observed between annual SCA and temperature, whereas a nonsignificant positive correlation (0.47) existed between annual SCA and precipitation in the past 20 years. It was also noticed that the SCA variability over the past 20 years has mainly been driven by temperature, whereas the influence of precipitation has been limited. A decline in average annual temperature (−0.039 °C/year) and a rise in precipitation (24.56 mm/year) was detected over the region. The results indicate that climate plays a vital role in controlling the SCA over the NWH region. The maximum and minimum snow cover frequency (SCF) was observed during the winter (74.42%) and monsoon (46.01%) season, respectively, while the average SCF was recorded to be 59.11% during the study period. Of the SCA, 54.81% had a SCF above 60% and could be considered as the perennial snow. The elevation-based analysis showed that 84% of the upper Himalaya (UH) experienced perennial snow, while the seasonal snow mostly dominated over the lower Himalaya (LH) and the middle Himalaya (MH).

Assessing the quality of a real-time Snow Cover Area product for hydrological applications

2012 ◽  
Vol 127 ◽  
pp. 271-287 ◽  
Author(s):  
G. Thirel ◽  
C. Notarnicola ◽  
M. Kalas ◽  
M. Zebisch ◽  
T. Schellenberger ◽  
...  
Keyword(s):  
Snow Cover ◽  
Real Time ◽  
Snow Cover Area ◽  
Area Product ◽  
Hydrological Applications

The optical behaviour of snow during a melting season at Ny Ålesund (Svalbard, Norway)

2021 ◽  
Author(s):  
Roberto Salzano ◽  
Christian Lanconelli ◽  
Giulio Esposito ◽  
Marco Giusto ◽  
Mauro Montagnoli ◽  
...  
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Full Range ◽  
Time Lapse ◽  
Test Site ◽  
Remotely Sensed ◽  
Optical Behaviour ◽  
Remotely Sensed Data ◽  
Snow Cover Area ◽  
Significant Information

<p><span>Polar areas are the most sensitive targets of </span><span>the </span><span>climate change and the continuous monitoring of the cryosphere represents a critical issue. The satellite remote sensing can fill this gap but further integration between remotely-sensed multi-spectral images and field data is crucial to validate retrieval algorithms and climatological models. The optical behaviour of snow, at different wavelengths, provides significant information about the micro-physical characteristics of the surface and this allow to discriminate different snow/ice covers. The aim of this work is to present an approach based on combining unmanned observations on spectral albedo and on the analysis of time-lapse images of sky and ground conditions in a</span><span>n </span><span>Ar</span><span>c</span><span>tic </span><span>test-site </span><span>(Svalbard, Norway). Terrestrial photography can provide, in fact, important information about the cloud cover and support the discrimination between white-sky or clear-sky illuminating conditions. Similarly, time-lapse cameras can provide a detailed description of the snow cover, estimating the fractional snow cover area. The spectral albedo was obtained by a narrow band device that was compared to a full-range commercial system and to remotely sensed data acquired during the 2015 spring/summer period at the </span><span>Amundsen - Nobile</span><span> Climate Change Tower (Ny </span><span>Å</span><span>lesund). The results confirmed the possibility to have continuous observations of the snow surface (microphisical) characteristics and highlighted the opportunity to monitor the spectral variations of snowed surfaces during the melting period. It was possible, </span><span>therefore,</span><span> to estimate spectral indexes, such as NDSI and SWIR albedo, and to found interesting links between both features and air/ground temperatures, wind-speed and precipitations. Different melting phases were detected and different processes were associated with the observed spectral variations.</span></p>

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