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.

scholarly journals ГИС БАЗИРАНА ПРОСТОРНО-ВРЕМЕНСКА АНАЛИЗА ПРОДУКЦИЈЕ БИОМАСЕ НА ПОДРУЧЈУ РЕПУБЛИКЕ СРПСКЕ

HERALD ◽  
2017 ◽  
Vol 1 (21) ◽  
Author(s):  
Даворин Бајић ◽  
Драгутин Аџић ◽  
Радoслав Декић
Keyword(s):  
Google Earth ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Google Earth Engine ◽  
Moderate Resolution Imaging Spectroradiometer

У овом раду извршена је просторно-временска анализа продукције биомасе на подручју РепубликеСрпске, коришћењем продуката даљинске детекције и географских информационих технологија. Основни улазнипараметри, на основу којих је извршена анализа, су бруто примарна продуктивност (GPP) и нето примарнапродукција (NPP). На основу GPP и NPP, као мјера продукције и акумулације угљеника у екосистемима, наиндиректан начин извршено је мјерење продукције биомасе. Kоришћени су сателитски снимци који се базирајуна MODIS (енг. Moderate Resolution Imaging Spectroradiometer) MOD17 производу, који у осмодневним игодишњим интервалима генерише GPP и NPP параметре, у виду ГИС растерских слојева. Анализа је извршена задесетогодишњи период од 2005. до 2014. године, коришћењем Google Earth Engine ВЕБ ГИС апликације и QGISдесктоп ГИС апликације. Резултати анализе продукције биомасе на подручју Републике Српске, у посматраномпериоду, указују на одређена одступања и колебања у просторним и временским оквирима, зависно од типаекосистема, те у зависности од метеоролошких услова у појединим годинама посматрања.

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 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 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.

A Study on Flood Forecasting in the Upper Indus Basin Considering Snow and Glacier Meltwater

2017 ◽  
Vol 12 (4) ◽  
pp. 793-805 ◽  
Author(s):  
Tong Liu ◽  
Morimasa Tsuda ◽  
Yoichi Iwami ◽  
◽  
Keyword(s):  
Snow Cover ◽  
Monsoon Season ◽  
Indus Basin ◽  
Maximum Magnitude ◽  
Upper Indus Basin ◽  
Moderate Resolution ◽  
Daily Discharge ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Tarbela Dam

This study considered glacier and snow meltwater by using the degree–day method with ground-based air temperature and fractional glacier/snow cover to simulate discharge at Skardu, Partab Bridge (P. Bridge), and Tarbela Dam in the Upper Indus Basin during the monsoon season, from the middle of June to the end of September. The optimum parameter set was determined and validated in 2010 and 2012. The simulated discharge with glaciermelt and snowmelt could capture the variations of the observed discharge in terms of peak volume and timing, particularly in the early monsoon season. The Moderate Resolution Imaging Spectroradiometer (MODIS) daily and eight-day snow cover products were applied and recommended with proper settings for application. This study also investigated the simulations with snow packs instead of daily snow cover, which was found to approach the maximum magnitude of observed discharge even from the uppermost station, Skardu.This study estimated the glacier and snow meltwater contribution at Skardu, Partab Bridge, and Tarbela as 43.2–65.2%, 22.0–29.3%, and 6.3–19.9% of average daily discharge during the monsoon season, respectively. In addition, this study evaluated the main source of simulation discrepancies and concluded that the methodology proposed in the study worked well with proper precipitation.

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