Estimation of Snow Cover Area Using Microwave SAR Dataset

2021 ◽  
pp. 35-55
Author(s):  
Shafiyoddin B. Sayyad ◽  
Mudassar A. Shaikh
Keyword(s):  
Snow Cover ◽  
Snow Cover Area

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>

scholarly journals Variation of snow cover in the Nyang River basin of southeastern Tibetan Plateau, China

2021 ◽  
Author(s):  
Rui Zhang ◽  
Zongxue Xu ◽  
Depeng Zuo ◽  
Chunguang Ban
Keyword(s):  
Snow Cover ◽  
River Basin ◽  
Snow Water Equivalent ◽  
Global Climate ◽  
Remote Sensing Data ◽  
Significant Negative Correlation ◽  
Spatial And Temporal Variation ◽  
Snow Cover Area ◽  
Northern Edge ◽  
Slight Upward Trend

Abstract Snow cover is highly sensitive to global climate change and strongly influences the climate at global and regional scales. Because of limited in situ observations, snow cover dynamics in the Nyang River basin (NRB) have been examined in few studies. Five snow cover indices derived from observation and remote sensing data from 2000 to 2018 were used to investigate the spatial and temporal variation of snow cover in the NRB. There was clear seasonality in the snow cover throughout the entire basin. The maximum snow-covered area was 8,751.35 km2, about 50% of the total basin area, and occurred in March. The maximum snow depth (SD) was 5.35 cm and was found at the northern edge of the middle reaches of the basin. Snow cover frequency, SD, and fraction of snow cover area increased with elevation. The decrease in SD was the most marked in the elevation range of 5,000–6,000 m. Above 6,000 m, the snow water equivalent showed a slight upward trend. There was a significant negative correlation between snow cover and temperature. The results of this study could improve our understanding of changes in snow cover in the NRB from multivariate perspectives. It is better for water resources management.

scholarly journals The Application of Geographic Information System (GIS) and Remote Sensing in Quantifying Snow Cover and Precipitation in Kabul Basin

2020 ◽  
Vol 5 (1) ◽  
pp. 80 ◽  
Author(s):  
Qamar Zaman ◽  
Shahid Nawaz Khan
Keyword(s):  
Remote Sensing ◽  
Water Resources ◽  
Snow Cover ◽  
Tropical Rainfall Measuring Mission ◽  
Well Being ◽  
Snow Cover Area ◽  
Hydrologic Modelling ◽  
Gis And Remote Sensing ◽  
Creative Commons ◽  
The People

Water Resources availability is very important to social and economic well-being of the people and has huge impacts on the socio-economic scenarios of a country. Precipitation and snow cover area assessment is some of the major inputs in hydrologic modelling and also for assessing and managing water resources in a basin. The change in the water availability in a basin has huge socio-economic impacts because of the water usage for food production, industries, and many others. The main aim of this study was to measure the snow cover area and precipitation from 2001 to 2015 in the Kabul basin. Moderate Resolution Image Spectroradiometer (MODIS) and Tropical Rainfall measuring Mission (TRMM) data were used to study snow cover area and precipitation respectively during 2001-2015. 8-day snow cover product for 15 years (January) was used to analyse the snow cover while monthly data of TRMM (3B43) were used to analyse the rainfall from 2001-2015. Different image processing techniques were applied on the data retrieved using GIS and Remote Sensing softwares. Initially, SCA was seen increasing, but during the last 3-4 years, it kept decreasing gradually. Rainfall was initially recorded as low, while later on, it was recorded high and reached the highest during 2010. Keywords: MODIS; Snow Cover; TRMM; Precipitation; Kabul Basin; Remote Sensing   Copyright (c) 2020 Geosfera Indonesia Journal and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

scholarly journals Using Air Temperature to Quantitatively Predict the MODIS Fractional Snow Cover Retrieval Errors over the Continental United States

2014 ◽  
Vol 15 (2) ◽  
pp. 551-562 ◽  
Author(s):  
Jiarui Dong ◽  
Mike Ek ◽  
Dorothy Hall ◽  
Christa Peters-Lidard ◽  
Brian Cosgrove ◽  
...  
Keyword(s):  
United States ◽  
Snow Cover ◽  
Air Temperature ◽  
Snow Water Equivalent ◽  
Probability Of Detection ◽  
Joint Analysis ◽  
Snow Cover Area ◽  
Fractional Snow Cover ◽  
Retrieval Errors

Abstract Understanding and quantifying satellite-based, remotely sensed snow cover uncertainty are critical for its successful utilization. The Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover errors have been previously recognized to be associated with factors such as cloud contamination, snowpack grain sizes, vegetation cover, and topography; however, the quantitative relationship between the retrieval errors and these factors remains elusive. Joint analysis of the MODIS fractional snow cover (FSC) from Collection 6 (C6) and in situ air temperature and snow water equivalent measurements provides a unique look at the error structure of the MODIS C6 FSC products. Analysis of the MODIS FSC dataset over the period from 2000 to 2005 was undertaken over the continental United States (CONUS) with an extensive observational network. When compared to MODIS Collection 5 (C5) snow cover area, the MODIS C6 FSC product demonstrates a substantial improvement in detecting the presence of snow cover in Nevada [30% increase in probability of detection (POD)], especially in the early and late snow seasons; some improvement over California (10% POD increase); and a relatively small improvement over Colorado (2% POD increase). However, significant spatial and temporal variations in accuracy still exist, and a proxy is required to adequately predict the expected errors in MODIS C6 FSC retrievals. A relationship is demonstrated between the MODIS FSC retrieval errors and temperature over the CONUS domain, captured by a cumulative double exponential distribution function. This relationship is shown to hold for both in situ and modeled daily mean air temperature. Both of them are useful indices in filtering out the misclassification of MODIS snow cover pixels and in quantifying the errors in the MODIS C6 product for various hydrological applications.

scholarly journals Using MODIS estimates of fractional snow cover area to improve streamflow forecasts in interior Alaska

2019 ◽  
Vol 23 (5) ◽  
pp. 2439-2459
Author(s):  
Katrina E. Bennett ◽  
Jessica E. Cherry ◽  
Ben Balk ◽  
Scott Lindsey
Keyword(s):  
Snow Cover ◽  
The United States ◽  
Remotely Sensed ◽  
Streamflow Forecasting ◽  
Snow Cover Area ◽  
Interior Alaska ◽  
Fractional Snow Cover ◽  
The Us ◽  
Physically Based ◽  
Streamflow Forecasts

Abstract. Remotely sensed snow cover observations provide an opportunity to improve operational snowmelt and streamflow forecasting in remote regions. This is particularly true in Alaska, where remote basins and a spatially and temporally sparse gaging network plague efforts to understand and forecast the hydrology of subarctic boreal basins and where climate change is leading to rapid shifts in basin function. In this study, the operational framework employed by the United States (US) National Weather Service, including the Alaska Pacific River Forecast Center, is adapted to integrate Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed observations of fractional snow cover area (fSCA) to determine if these data improve streamflow forecasts in interior Alaska river basins. Two versions of MODIS fSCA are tested against a base case extent of snow cover derived by aerial depletion curves: the MODIS 10A1 (MOD10A1) and the MODIS Snow Cover Area and Grain size (MODSCAG) product over the period 2000–2010. Observed runoff is compared to simulated runoff to calibrate both iterations of the model. MODIS-forced simulations have improved snow depletion timing compared with snow telemetry sites in the basins, with discernable increases in skill for the streamflow simulations. The MODSCAG fSCA version provides moderate increases in skill but is similar to the MOD10A1 results. The basins with the largest improvement in streamflow simulations have the sparsest streamflow observations. Considering the numerous low-quality gages (discontinuous, short, or unreliable) and ungauged systems throughout the high-latitude regions of the globe, this result is valuable and indicates the utility of the MODIS fSCA data in these regions. Additionally, while improvements in predicted discharge values are subtle, the snow model better represents the physical conditions of the snowpack and therefore provides more robust simulations, which are consistent with the US National Weather Service's move toward a physically based National Water Model. Physically based models may also be more capable of adapting to changing climates than statistical models corrected to past regimes. This work provides direction for both the Alaska Pacific River Forecast Center and other forecast centers across the US to implement remote-sensing observations within their operational framework, to refine the representation of snow, and to improve streamflow forecasting skill in basins with few or poor-quality observations.

scholarly journals A Conditional Probability Interpolation Method Based on a Space-Time Cube for MODIS Snow Cover Products Gap Filling

2020 ◽  
Vol 12 (21) ◽  
pp. 3577
Author(s):  
Siyong Chen ◽  
Xiaoyan Wang ◽  
Hui Guo ◽  
Peiyao Xie ◽  
Jian Wang ◽  
...  
Keyword(s):  
Snow Cover ◽  
Conditional Probability ◽  
Interpolation Method ◽  
Space Time ◽  
Landsat 8 ◽  
Snow Cover Area ◽  
Northern Xinjiang ◽  
Central Pixel ◽  
Snow Cover Extent ◽  
Snow Condition

Seasonal snow cover is closely related to regional climate and hydrological processes. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products from 2001 to 2018 were applied to analyze the snow cover variation in northern Xinjiang, China. As cloud obscuration causes significant spatiotemporal discontinuities in the binary snow cover extent (SCE), we propose a conditional probability interpolation method based on a space-time cube (STCPI) to remove clouds completely after combining Terra and Aqua data. First, the conditional probability that the central pixel and every neighboring pixel in a space-time cube of 5 × 5 × 5 with the same snow condition is counted. Then the snow probability of the cloud pixels reclassified as snow is calculated based on the space-time cube. Finally, the snow condition of the cloud pixels can be recovered by snow probability. The validation experiments with the cloud assumption indicate that STCPI can remove clouds completely and achieve an overall accuracy of 97.44% under different cloud fractions. The generated daily cloud-free MODIS SCE products have a high agreement with the Landsat–8 OLI image, for which the overall accuracy is 90.34%. The snow cover variation in northern Xinjiang, China, from 2001 to 2018 was investigated based on the snow cover area (SCA) and snow cover days (SCD). The results show that the interannual change of SCA gradually decreases as the elevation increases, and the SCD and elevation have a positive correlation. Furthermore, the interannual SCD variation shows that the area of increase is higher than that of decrease during the 18 years.

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