Snow cover mapping using IRS-P6 AWiFS data and the relationships between some climatic factors with snowpack in the northwest of Iran

2015 ◽  
Vol 7 (2) ◽  
pp. 415-429
Author(s):  
M. Seyedielmabad ◽  
H. R. Moradi
Keyword(s):  
Snow Cover ◽  
Climatic Factors ◽  
Unsupervised Classification ◽  
Wide Field ◽  
Snow Cover Area ◽  
Multi Temporal ◽  
Moderate Resolution ◽  
Field Sensor ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Normalized Difference Snow Index

In this study, we explored the potential of the multispectral and multi-temporal IRS Advanced Wide Field Sensor (AWiFS) data for mapping of the snow cover in the northwest regions of Iran. The AWiFS snow cover maps, based on the unsupervised classification method, were compared with the estimates of snow cover area derived from the moderate resolution imaging spectroradiometer (MODIS) images based on the normalized difference snow index. Good concurrence was observed with respect to the snow area between the AWiFS features and the MODIS features; however, the snow spatial distribution of the AWiFS features differed from those of the MODIS based on the nonentity of the temporal accordance between two types of features. Also, we explored the relationships between some climatic and topographic factors with the snowpack in the northwest part of Iran. Relationships between some climatic factors with snowpack specifications were obtained, which showed significant correlation only between the components of daily temperature and snow density. The other results showed that the amounts of snowpack depth have significant correlations with the height of the stations and the height classes in 1% surface and snowpack depths showed significant differences together within the different height classes.

scholarly journals Cross-Comparison between MODIS and VIIRS Snow Cover Products for the 2016 Hydrological Year

Climate ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 57 ◽  
Author(s):  
Shubhechchha Thapa ◽  
Parveen K. Chhetri ◽  
Andrew G. Klein
Keyword(s):  
Snow Cover ◽  
Infrared Imaging ◽  
Environmental Data ◽  
Data Series ◽  
Climate Data ◽  
Snow Cover Area ◽  
Transition Zones ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Normalized Difference Snow Index

The VIIRS (Visible Infrared Imaging Radiometer Suite) instrument on board the Suomi-NPP (National Polar-Orbiting Partnership) satellite aims to provide long-term continuity of several environmental data series including snow cover initiated with MODIS (Moderate Resolution Imaging Spectroradiometer). Although it is speculated that MODIS and VIIRS snow cover products may differ because of their differing spatial resolutions and spectral coverage, quantitative comparisons between their snow products are currently limited. Therefore, this study intercompares MODIS and VIIRS snow products for the 2016 Hydrological Year over the Midwestern United States and southern Canada. Two hundred and forty-four swath snow products from MODIS/Aqua (MYD10L2) and the VIIRS EDR (Environmental Data Records) (VSCMO/binary) were intercompared using confusion matrices, comparison maps and false color imagery. Thresholding the MODIS NDSI (Normalized Difference Snow Index) Snow Cover product at a snow cover fraction of 30% generated binary snow maps are most comparable to the NOAA VIIRS binary snow product. Overall agreement between MODIS and VIIRS was found to be approximately 98%. This exceeds the VIIRS accuracy requirements of 90% probability of correct typing. The agreement was highest during the winter but lower during late fall and spring. MODIS and VIIRS often mapped snow/no-snow transition zones as a cloud. The assessment of total snow and cloud pixels and comparison snow maps of MODIS and VIIRS indicate that VIIRS is mapping more snow cover and less cloud cover compared to MODIS. This is evidenced by the average area of snow in MYD10L2 and VSCMO being 5.72% and 11.43%, no-snow 26.65% and 28.67% and cloud 65.02% and 59.91%, respectively. While VIIRS and MODIS have a similar capacity to map snow cover, VIIRS has the potential to map snow cover area more accurately, for the successful development of climate data records.

scholarly journals Changes in Snow Cover Dynamics over the Indus Basin: Evidences from 2008 to 2018 MODIS NDSI Trends Analysis

2020 ◽  
Vol 12 (17) ◽  
pp. 2782
Author(s):  
Sikandar Ali ◽  
Muhammad Jehanzeb Masud Cheema ◽  
Muhammad Mohsin Waqas ◽  
Muhammad Waseem ◽  
Usman Khalid Awan ◽  
...  
Keyword(s):  
Time Series ◽  
Snow Cover ◽  
River Basins ◽  
Google Earth ◽  
Indus Basin ◽  
Least Squares Regression ◽  
Snow Cover Area ◽  
Standard Product ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Normalized Difference Snow Index

The frozen water reserves on the Earth are not only very dynamic in their nature, but also have significant effects on hydrological response of complex and dynamic river basins. The Indus basin is one of the most complex river basins in the world and receives most of its share from the Asian Water Tower (Himalayas). In such a huge river basin with high-altitude mountains, the regular quantification of snow cover is a great challenge to researchers for the management of downstream ecosystems. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) daily (MOD09GA) and 8-day (MOD09A1) products were used for the spatiotemporal quantification of snow cover over the Indus basin and the western rivers’ catchments from 2008 to 2018. The high-resolution Landsat Enhanced Thematic Mapper Plus (ETM+) was used as a standard product with a minimum Normalized Difference Snow Index (NDSI) threshold (0.4) to delineate the snow cover for 120 scenes over the Indus basin on different days. All types of errors of commission/omission were masked out using water, sand, cloud, and forest masks at different spatiotemporal resolutions. The snow cover comparison of MODIS products with Landsat ETM+, in situ snow data and Google Earth imagery indicated that the minimum NDSI threshold of 0.34 fits well compared to the globally accepted threshold of 0.4 due to the coarser resolution of MODIS products. The intercomparison of the time series snow cover area of MODIS products indicated R2 values of 0.96, 0.95, 0.97, 0.96 and 0.98, for the Chenab, Jhelum, Indus and eastern rivers’ catchments and Indus basin, respectively. A linear least squares regression analysis of the snow cover area of the Indus basin indicated a declining trend of about 3358 and 2459 km2 per year for MOD09A1 and MOD09GA products, respectively. The results also revealed a decrease in snow cover area over all the parts of the Indus basin and its sub-catchments. Our results suggest that MODIS time series NDSI analysis is a useful technique to estimate snow cover over the mountainous areas of complex river basins.

scholarly journals Distribution of seasonal snow cover in central and western Himalaya

2010 ◽  
Vol 51 (54) ◽  
pp. 123-128 ◽  
Author(s):  
Anil V. Kulkarni ◽  
B.P. Rathore ◽  
S.K. Singh ◽  
Ajai
Keyword(s):  
Snow Cover ◽  
Western Himalaya ◽  
Snow Accumulation ◽  
Wide Field ◽  
Seasonal Snow ◽  
Snow Line ◽  
Field Sensor ◽  
Normalized Difference Snow Index ◽  
The Individual ◽  
Seasonal Snow Cover

AbstractIndian rivers originating in the Himalaya depend on seasonal snow-cover melt during crucial summer months. The seasonal snow cover was monitored using Advanced Wide Field Sensor (AWiFS) data of the Indian Remote Sensing Satellite (IRS) and using the Normalized Difference Snow Index (NDSI) algorithm. The investigation was carried out for a period of 3 years (2004/05, 2005/06 and 2006/07) between October and June. A total of 28 sub-basins of the Ganga and Indus river basins were monitored at intervals of 5 or 10 days. Approximately 1500 AWiFS scenes were analyzed. A combination of area–altitude distribution and snow map was used to estimate the distribution of snow cover in altitude zones for the individual basins and for the western and central Himalaya. Hypsographic curve and snow-free area was used to estimate monthly snow-line elevation. The lowest snow-line altitude in the winters of 2004/05, 2005/06 and 2006/07 was observed at 2480 ma.s.l. on 25 February 2005. In Ravi basin for the year 2004/05, snow accumulation and ablation were continuous processes throughout the winter. Even in the middle of winter, the snow area was reduced from 90% to 55%. Similar trends were observed for 2005/06 and 2007/08. In Bhaga basin, snowmelt was observed in the early part of the winter, i.e. in December, and no significant melting was observed between January and April.

scholarly journals Estimating spatial distribution of daily snow depth with kriging methods: combination of MODIS snow cover area data and ground-based observations

2015 ◽  
Vol 9 (5) ◽  
pp. 4997-5020 ◽  
Author(s):  
C. L. Huang ◽  
H. W. Wang ◽  
J. L. Hou
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Snow Depth ◽  
Western China ◽  
Kriging Interpolation ◽  
Snow Cover Area ◽  
Smoothing Effects ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Accurately measuring the spatial distribution of the snow depth is difficult because stations are sparse, particularly in western China. In this study, we develop a novel scheme that produces a reasonable spatial distribution of the daily snow depth using kriging interpolation methods. These methods combine the effects of elevation with information from Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover area (SCA) products. The scheme uses snow-free pixels in MODIS SCA images with clouds removed to identify virtual stations, or areas with zero snow depth, to compensate for the scarcity and uneven distribution of stations. Four types of kriging methods are tested: ordinary kriging (OK), universal kriging (UK), ordinary co-kriging (OCK), and universal co-kriging (UCK). These methods are applied to daily snow depth observations at 50 meteorological stations in northern Xinjiang Province, China. The results show that the spatial distribution of snow depth can be accurately reconstructed using these kriging methods. The added virtual stations improve the distribution of the snow depth and reduce the smoothing effects of the kriging process. The best performance is achieved by the OK method in cases with shallow snow cover and by the UCK method when snow cover is widespread.

scholarly journals SnowCloudMetrics: Snow Information for Everyone

2020 ◽  
Vol 12 (20) ◽  
pp. 3341
Author(s):  
Ryan L. Crumley ◽  
Ross T. Palomaki ◽  
Anne W. Nolin ◽  
Eric A. Sproles ◽  
Eugene J. Mar
Keyword(s):  
Snow Cover ◽  
Data Storage ◽  
Image Data ◽  
Global Scale ◽  
Google Earth ◽  
Mountain Environments ◽  
Resource Managers ◽  
Moderate Resolution ◽  
Snow Telemetry ◽  
Moderate Resolution Imaging Spectroradiometer

Snow is a critical component of the climate system, provides fresh water for millions of people globally, and affects forest and wildlife ecology. Snowy regions are typically data sparse, especially in mountain environments. Remotely-sensed snow cover data are available globally but are challenging to convert into accessible, actionable information. SnowCloudMetrics is a web portal for on-demand production and delivery of snow information including snow cover frequency (SCF) and snow disappearance date (SDD) using Google Earth Engine (GEE). SCF and SDD are computed using the Moderate Resolution Imaging Spectroradiometer (MODIS) Snow Cover Binary 500 m (MOD10A1) product. The SCF and SDD metrics are assessed using 18 years of Snow Telemetry records at more than 750 stations across the Western U.S. SnowCloudMetrics provides users with the capacity to quickly and efficiently generate local-to-global scale snow information. It requires no user-side data storage or computing capacity, and needs little in the way of remote sensing expertise. SnowCloudMetrics allows users to subset by year, watershed, elevation range, political boundary, or user-defined region. Users can explore the snow information via a GEE map interface and, if desired, download scripts for access to tabular and image data in non-proprietary formats for additional analyses. We present global and hemispheric scale examples of SCF and SDD. We also provide a watershed example in the transboundary, snow-dominated Amu Darya Basin. Our approach represents a new, user-driven paradigm for access to snow information. SnowCloudMetrics benefits snow scientists, water resource managers, climate scientists, and snow related industries providing SCF and SDD information tailored to their needs, especially in data sparse regions.

Integration of AWiFS and MODIS active fire data for burn mapping at regional level using the Burned Area Synergic Algorithm (BASA)

2009 ◽  
Vol 18 (4) ◽  
pp. 404 ◽  
Author(s):  
Federico González-Alonso ◽  
Silvia Merino-de-Miguel
Keyword(s):  
Forest Fires ◽  
Burned Area ◽  
Wide Field ◽  
Imaging Spectrometer ◽  
Area Index ◽  
North West ◽  
Active Fire ◽  
Moderate Resolution ◽  
Field Sensor ◽  
Speed Accuracy

The present paper presents an algorithm that synergistically combines data from four different parts of the spectrum (near-, shortwave, middle- and thermal infrared) to produce a reliable burned-area map. It is based on the use of a modified version of the BAIM (MODIS – Moderate Resolution Imaging Spectrometer – Burned Area Index) together with active fire information. The following study focusses in particular on an image from the AWiFS (Advanced Wide Field Sensor) sensor dated 21 August 2006 and MODIS active fires detected during the first 20 days of August as well as ancillary maps and information. The methodology was tested in Galicia (north-west Spain) where hundreds of forest fires occurred during the first 20 days of August 2006. Burned area data collected from the present work was compared with official fire statistics from both the Spanish Ministry of the Environment and the Galician Forestry Service. The speed, accuracy and cost-effectiveness of this method suggest that it would be of great interest for use at both regional and national levels.

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 Satellite monitoring of blue-ice extent near Byrd Glacier, Antarctica

2004 ◽  
Vol 39 ◽  
pp. 223-230 ◽  
Author(s):  
Ian C. Brown ◽  
Ted A. Scambos
Keyword(s):  
Snow Cover ◽  
Satellite Monitoring ◽  
Landsat Images ◽  
Moderate Resolution ◽  
Summer Maximum ◽  
Resolution Imaging ◽  
Snowfall Event ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Seasonal And Interannual Variations

AbstractWe use satellite images to track seasonal and interannual variations in blue-ice extent over the past 30 years near Byrd Glacier on the East Antarctic plateau. The study areas have low slope and few nearby nunataks, which may increase their climate sensitivity. A threshold-based algorithm sensitive to snow grain-size is used to analyze 56 Moderate Resolution Imaging Spectroradiometer (MODIS) images over three recent summer seasons. Seasonal blue-ice exposure grows rapidly in late spring, and peaks by late December. Exposure is relatively constant between late December and mid-January, then declines in February. We interpret this cycle as due to removal and re-accumulation of patchy snow. Interannual changes in blue-ice area may be estimated by tracking the near-constant summer maximum extent period. Fifteen mid-summer Landsat images, spanning 1974–2002, were analyzed to determine long-term variations. Interannual area changes are 10–30%; however, the MODIS data revealed that the exposed blue-ice area can be sharply reduced for up to 2 weeks after a snowfall event; and in the 2001/02 season, patchy snow cover persisted for the entire summer. The combination of MODIS seasonal and Landsat interannual data indicates that blue-ice areas can be climate-sensitive. The strong feedback between snow cover and surface energy balance implies that blue-ice areas could rapidly decrease due to climate-related increases in snowfall or reduced ablation.

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