scholarly journals Snow Cover Area Detection using NDSI and Band Ratio Method

2020 ◽  
Vol 9 (7S) ◽  
pp. 66-70
Keyword(s):  
Snow Cover ◽  
Near Infrared ◽  
Ratio Method ◽  
Automated Classification ◽  
Gis Technology ◽  
Infrared Band ◽  
Snow Cover Area ◽  
Band Ratio ◽  
Snow Covered Area ◽  
Covered Area

Glaciers are a main source of water during summer in Himalayan areas. Corresponding to the historical studies, glacier is directly affected by climate change. It is important to identify change in snow cover area (Glacier area) to identify change in glacier. Remote sensing and GIS technology are used to monitor Snow covered area. This paper focuses on Sentinel-2B data of trisul glacier which is a part of Indian Himalayas to identify glacier. These multispectral images were extracted from USGS Earth Explorer. The sentinel-2B data are processed using Semi automated Classification Plugin (SCP) of QGIS tool. Snow covered area is identified by using two automated methods: Normalized Difference Snow Index (NDSI) and Band Ratio. For NDSI reflectance of visible, shortwave band is used. For Band Ratio reflectance of near infrared, shortwave infrared band is used. It is challenging to detect snow covered area from the satellite as snow covered area and cloud area have same white colure i.e. same reflectance. In this paper, represents experiments on two methods for snow area extraction on satelliteimages.

scholarly journals Analysis of the Snow Cover Area of the Gangotri and Surrounding Glaciers using Remote Sensing and GIS

2021 ◽  
Vol 9 (8) ◽  
pp. 2761-2776
Author(s):  
Vikram Nath
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Large Range ◽  
Remote Sensing And Gis ◽  
Landsat 8 ◽  
Snow Cover Area ◽  
Snow Covered Area ◽  
Covered Area ◽  
Drastic Increase ◽  
Freshwater Reservoir

Abstract: Himalayas has one in every of the biggest resources of snow and ice, which act as a freshwater reservoir for all of the rivers originating from it. Monitoring of these sources is vital for the assessment of availability of water within the Himalayan Rivers. The mapping of Glaciers could be very tough undertaking due to the inaccessibility and remoteness of the terrain. Faraway sensing techniques are regularly the simplest way to research glaciers in remote mountains and to monitor a large range of glaciers in multitemporal manner. This paper presents the results obtained from the analysis of 5 set of Landsat 8 Band 3 - Green and Band 6 - SWIR 1 images from year 2017 to 2021 for the monitoring and analysis of approx 76% of Gangotri and Surrounding Glaciers (GSG) main snow covered area. It is seen in the analysis that there has been a down fall around 85 sq km of the Snow Cover of the Gangotri and Surrounding Glacier and Surrounding Glaciers (GSG) Area in the years of 2018 and 2019 respectively from the year 2017. In 2020 huge recovery has occurred with a drastic increase in snow cover area by approximately the same amount which has been previously depleted. After 2020, it seems that a gradual drop of 27 sq km occurred in 2021. Calculation shows a dip of 14.91% of snow cover area from 2017 to 2018 of the Gangotri and Surrounding Glaciers (GSG) which was recovered to original level in 2020. Slight dip of around 4.88% occurred in the current year 2021.

scholarly journals Nimbus-7 SMMR Derived Global Snow Cover Parameters

1987 ◽  
Vol 9 ◽  
pp. 39-44 ◽  
Author(s):  
A.T.C. Chang ◽  
J.L. Foster ◽  
D.K. Hall
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Snow Water Equivalent ◽  
Theoretical Calculations ◽  
Winter Season ◽  
Western China ◽  
High Plains ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water

Snow covers about 40 million km2of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

scholarly journals Nimbus-7 SMMR Derived Global Snow Cover Parameters

1987 ◽  
Vol 9 ◽  
pp. 39-44 ◽  
Author(s):  
A.T.C. Chang ◽  
J.L. Foster ◽  
D.K. Hall
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Snow Water Equivalent ◽  
Theoretical Calculations ◽  
Winter Season ◽  
Western China ◽  
High Plains ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water

Snow covers about 40 million km2 of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

scholarly journals Utilization of Satellite Snow-Cover Observations for Seasonal Streamflow Estimates in the Western Himalayas

1983 ◽  
Vol 14 (5) ◽  
pp. 257-266 ◽  
Author(s):  
B. Dey ◽  
D. C. Goswami ◽  
A. Rango
Keyword(s):  
Regression Model ◽  
Snow Cover ◽  
Multiple Regression ◽  
Snowmelt Runoff ◽  
Indus River ◽  
Snow Covered Area ◽  
Covered Area ◽  
Measured Flow ◽  
Seasonal Runoff ◽  
Kabul River

The results presented in this study indicate the possibility of seasonal runoff prediction when satellite-derived basin snow-cover data are related to point source river discharge data for a number of years. NOAA-VHRR satellite images have been used to delineate the areal extent of snow cover for early April over the Indus and Kabul River basins in Pakistan. Simple photo-interpretation techniques, using a zoom transfer scope, were employed in transferring satellite snow-cover boundaries onto base map overlays. A linear regression model with April 1 through July 31 seasonal runoff (1974-1979) as a function of early April snow cover explains 73% and 82% of the variance, respectively, of the measured flow in the Indus and Kabul Rivers. The correlation between seasonal runoff and snow cover is significant at the 97% level for the Indus River and at the 99% level for the Kabul River. Combining Rango et al.'s (1977) data for 1969-73 with the above period, the April snow cover explains 60% and 90% of the variance, respectively, of the measured flow in the Indus and Kabul Rivers. In an attempt to improve the Indus relationship, a multiple regression model, with April 1 through July 31, 1969-79, seasonal runoff in the Indus River as a function of early April snow-covered area of the basin and concurrent runoff in the adjoining Kabul River, explains 79% of the variability in flow. Moreover, a significant reduction (27%) in the standard error of estimate results from using the multi-variate model. For each year of the study period, 1969-79, a separate multiple regression equation is developed dropping the data for the year in question from the data-base and using those for the rest of the years. The snow cover area and concurrent runoff data are then used to estimate the snowmelt runoff for that particular year.The difference between the estimated and observed dircharge values averaged over the 11 year study period is 10%. Satellite derived snow-covered area is the best available input for snowmelt-runoff estimation in remote, data sparse basins like the Indus and Kabul Rivers. The study has operational relevance to water resource planning and management in the Himalayan region.

scholarly journals A Spectral Analysis of Snow in Mt. Rainier

2018 ◽  
Vol 10 (3) ◽  
pp. 20
Author(s):  
Shrinidhi Ambinakudige ◽  
Pushkar Inamdar ◽  
Aynaz Lotfata
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Thermal Emission ◽  
Major Influence ◽  
Landsat 8 ◽  
Run Off ◽  
Snow Covered Area ◽  
Covered Area ◽  
Local Water ◽  
Normalized Difference Snow Index

Snow cover helps regulate the temperature of the Earth's surface. Snowmelt recharges groundwater, provides run-off for rivers and creeks, and acts as a major source of local water for many communities around the world. Since 2000, there has been a significant decrease in the snow-covered area in the Northern Hemisphere. Climate change is the major factor influencing the change in snow cover amount and distribution. We analyze spectral properties of the remote sensing sensors with respect to the study of snow and examine how data from some of the major remote sensing satellite sensors, such as (Advanced Spaceborne Thermal Emission and Reflection Radiometer) ASTER, Landsat-8, and Sentinel-2, can be used in studying snow. The study was conducted in Mt. Rainier. Although reflectance values recorded were lower due to the timing of the data collection and the aspect of the study site, data can still be used calculate normalized difference snow index (NDSI) to clearly demarcate the snow from other land cover classes. NDSI values in all three satellites ranged from 0.94 to 0.97 in the snow-covered area of the study site. Any pollutants in snow can have a major influence on spectral reflectance in the VIS spectrum because pollutants absorb more than snow.

scholarly journals Determination of snow-covered area in different land covers in central Alaska, U.S.A., from aircraft data — April 1995

1998 ◽  
Vol 26 ◽  
pp. 149-155 ◽  
Author(s):  
Dorothy K. Hall ◽  
James L. Foster ◽  
Alfred T. C. Chang ◽  
Carl S. Benson ◽  
Janet Y. L. Chien
Keyword(s):  
Snow Cover ◽  
Vegetation Cover ◽  
Near Infrared ◽  
Mixed Forest ◽  
Mixed Forests ◽  
Mapping Algorithm ◽  
Covered Area ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cover Density

During April 1995, a field and aircraft experiment was conducted in central Alaska in support of the Moderate Resolution Imaging Spectroradiometer (MODIS) snow-mapping project. The MODIS Airborne Simulator (MAS), a 50 channel spectroradiometer, was flown on board the NASA ER-2 aircraft. An objective of the mission was to determine the accuracy of mapping snow in different surface covers using an algorithm designed to map global snow cover after the launch of MODIS in 1998. The surface cover in this area of central Alaska is typically spruce, birch, aspen, mixed forest and muskeg. Integrated reflectance, Ri was calculated from the visible/near-infrared channels of the MAS sensor. The Ri was used to estimate different vegetation-cover densities because there is an inverse relationship between vegetation-cover density and albedo in snow-covered terrain. A vegetation-cover density map was constructed using MAS data acquired on 13 April 1995 over central Alaska. In the part of the scene that was mapped as having a vegetation-cover density of < 50%, the snow-mapping algorithm mapped 96.41% snow cover. These areas are generally composed of muskeg and mixed forests and include frozen lake. In the part of the scene that was estimated to have a vegetation-cover density of ≥50%, the snow-mapping algorithm mapped 71.23% snow cover. These areas are generally composed of dense coniferous or deciduous forests. Overall, the accuracy of the snow-mapping algorithm is > 87.41% for a 13 April MAS scene with a variety of surface covers (coniferous and deciduous and mixed forests, muskeg, tundra and frozen lake).

scholarly journals Forward-Looking Assimilation of MODIS-Derived Snow-Covered Area into a Land Surface Model

2009 ◽  
Vol 10 (1) ◽  
pp. 130-148 ◽  
Author(s):  
Benjamin F. Zaitchik ◽  
Matthew Rodell
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Land Surface Model ◽  
Radiation Budget ◽  
Surface Model ◽  
Energy Fluxes ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water

Abstract Snow cover over land has a significant impact on the surface radiation budget, turbulent energy fluxes to the atmosphere, and local hydrological fluxes. For this reason, inaccuracies in the representation of snow-covered area (SCA) within a land surface model (LSM) can lead to substantial errors in both offline and coupled simulations. Data assimilation algorithms have the potential to address this problem. However, the assimilation of SCA observations is complicated by an information deficit in the observation—SCA indicates only the presence or absence of snow, not snow water equivalent—and by the fact that assimilated SCA observations can introduce inconsistencies with atmospheric forcing data, leading to nonphysical artifacts in the local water balance. In this paper, a novel assimilation algorithm is presented that introduces Moderate Resolution Imaging Spectroradiometer (MODIS) SCA observations to the Noah LSM in global, uncoupled simulations. The algorithm uses observations from up to 72 h ahead of the model simulation to correct against emerging errors in the simulation of snow cover while preserving the local hydrologic balance. This is accomplished by using future snow observations to adjust air temperature and, when necessary, precipitation within the LSM. In global, offline integrations, this new assimilation algorithm provided improved simulation of SCA and snow water equivalent relative to open loop integrations and integrations that used an earlier SCA assimilation algorithm. These improvements, in turn, influenced the simulation of surface water and energy fluxes during the snow season and, in some regions, on into the following spring.

scholarly journals PRACTISE – Photo Rectification And ClassificaTIon SoftwarE (V.2.0)

2015 ◽  
Vol 8 (10) ◽  
pp. 8481-8518
Author(s):  
S. Härer ◽  
M. Bernhardt ◽  
K. Schulz
Keyword(s):  
Snow Cover ◽  
Satellite Images ◽  
Threshold Value ◽  
Landsat 8 ◽  
Snow Covered Area ◽  
Covered Area ◽  
Independent Information ◽  
Landsat 7 ◽  
Definition Of

Abstract. Terrestrial photography combined with the recently presented Photo Rectification And ClassificaTIon SoftwarE (PRACTISE V.1.0) has proven to be a valuable source to derive snow cover maps in a high temporal and spatial resolution. The areal coverage of the used digital photographs is however strongly limited. Satellite images on the other hand can cover larger areas but do show uncertainties with respect to the accurate detection of the snow covered area. This is especially the fact if user defined thresholds are needed e.g. in case of the frequently used Normalised-Difference Snow Index (NDSI). The definition of this value is often not adequately defined by either a general value from literature or over the impression of the user but not by reproducible independent information. PRACTISE V.2.0 addresses this important aspect and does show additional improvements. The Matlab based software is now able to automatically process and detect snow cover in satellite images. A simultaneously captured camera-derived snow cover map is in this case utilised as in-situ information for calibrating the NDSI threshold value. Moreover, an additional automatic snow cover classification, specifically developed to classify shadow-affected photographs was included. The improved software was tested for photographs and Landsat 7 Enhanced Thematic Mapper (ETM+) as well as Landsat 8 Operational Land Imager (OLI) scenes in the Zugspitze massif (Germany). The results have shown that using terrestrial photography in combination with satellite imagery can lead to an objective, reproducible and user-independent derivation of the NDSI threshold and the resulting snow cover map. The presented method is not limited to the sensor system or the threshold used in here but offers manifold application options for other scientific branches.

scholarly journals A seasonal algorithm of the snow-covered area fraction for mountainous terrain

2021 ◽  
Author(s):  
Nora Helbig ◽  
Michael Schirmer ◽  
Jan Magnusson ◽  
Flavia Mäder ◽  
Alec van Herwijnen ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Model Performance ◽  
Ground Surface ◽  
Grid Cell ◽  
Mountainous Terrain ◽  
Fine Scale ◽  
Snow Covered Area ◽  
Covered Area ◽  
Mean Square Errors

Abstract. The snow cover spatial variability in mountainous terrain changes considerably over the course of a snow season. In this context, fractional snow-covered area (fSCA) is therefore an essential model parameter characterizing how much of the ground surface in a grid cell is currently covered by snow. We present a seasonal fSCA algorithm using a recent scale-independent fSCA parameterization. For the seasonal implementation we track snow depth (HS) and snow water equivalent (SWE) and account for several alternating accumulation-ablation phases. Besides tracking HS and SWE, the seasonal fSCA algorithm only requires computing subgrid terrain parameters from a fine-scale summer digital elevation model. We implemented the new algorithm in a multilayer energy balance snow cover model. For a spatiotemporal evaluation of modelled fSCA we compiled three independent fSCA data sets. Evaluating modelled 1 km fSCA seasonally with fSCA derived from airborne-acquired fine-scale HS data, satellite- as well as terrestrial camera-derived fSCA showed overall normalized root mean square errors of respectively 9 %, 20 % and 22 %, and represented seasonal trends well. The overall good model performance suggests that the seasonal fSCA algorithm can be applied in other geographic regions by any snow model application.

scholarly journals PRACTISE – Photo Rectification And ClassificaTIon SoftwarE (V.2.1)

2016 ◽  
Vol 9 (1) ◽  
pp. 307-321 ◽  
Author(s):  
S. Härer ◽  
M. Bernhardt ◽  
K. Schulz
Keyword(s):  
Snow Cover ◽  
Satellite Images ◽  
Threshold Value ◽  
Landsat 8 ◽  
Snow Covered Area ◽  
Covered Area ◽  
Independent Information ◽  
Landsat 7 ◽  
Normalized Difference Snow Index ◽  
Definition Of

Abstract. Terrestrial photography combined with the recently presented Photo Rectification And ClassificaTIon SoftwarE (PRACTISE V.1.0) has proven to be a valuable source to derive snow cover maps in a high temporal and spatial resolution. The areal coverage of the used digital photographs is however strongly limited. Satellite images on the other hand can cover larger areas but do show uncertainties with respect to the accurate detection of the snow covered area. This is especially the fact if user defined thresholds are needed, e.g. in case of the frequently used normalized-difference snow index (NDSI). The definition of this value is often not adequately defined by either a general value from literature or over the impression of the user, but not by reproducible independent information. PRACTISE V.2.1 addresses this important aspect and shows additional improvements. The Matlab-based software is now able to automatically process and detect snow cover in satellite images. A simultaneously captured camera-derived snow cover map is in this case utilized as in situ information for calibrating the NDSI threshold value. Moreover, an additional automatic snow cover classification, specifically developed to classify shadow-affected photographs, was included. The improved software was tested for photographs and Landsat 7 Enhanced Thematic Mapper (ETM+) as well as Landsat 8 Operational Land Imager (OLI) scenes in the Zugspitze massif (Germany). The results show that using terrestrial photography in combination with satellite imagery can lead to an objective, reproducible, and user-independent derivation of the NDSI threshold and the resulting snow cover map. The presented method is not limited to the sensor system or the threshold used in here but offers manifold application options for other scientific branches.

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