A regional snow-line method for estimating snow cover from MODIS during cloud cover

Publicly available webcam images offer an enormous potential to study the variability of snow cover on a high spatio-temporal scale. Such cameras allow detailed analyses of snow cover on steep slopes due to their oblique view on the mountains and can provide snow cover information even under cloudy weather conditions. Our webcam-based snow cover monitoring network comprises several hundreds of webcams and enables to gather snow cover information over a large area with a minimum amount of manual user input. This information can serve as a reference for improved validation of satellite-based approaches, as well as complement satellite-based snow cover retrieval, in particular under cloudy weather conditions. Here, we present a framework to estimate the regional snow line elevation in the Swiss Alps. The snow line elevation is an important indicator of snow cover in mountainous regions and can be used, for example, as an input for hydrological modeling or to study the seasonality of river discharge. We compare and combine snow line retrieval from Sentinel-2 snow cover maps and webcam-based snow cover information to analyze regional differences in the spring snowmelt period. Since cloud cover is an important factor that affects the quality of satellite-based snow cover products, the combination with snow information from webcams can improve the accuracy and can fill temporal gaps, especially during recurrent cloud cover. Furthermore, we present a method to detect cloud cover in webcam images and discuss limitations of webcam-based snow cover monitoring.

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Cloud obstruction and snow cover in Alpine areas from MODIS products

Hydrology and Earth System Sciences ◽

10.5194/hess-18-4579-2014 ◽

2014 ◽

Vol 18 (11) ◽

pp. 4579-4600 ◽

Cited By ~ 16

Author(s):

P. Da Ronco ◽

C. De Michele

Keyword(s):

Snow Cover ◽

River Basin ◽

Cloud Cover ◽

Large Scale ◽

Snow Water Equivalent ◽

Practical Interest ◽

Computational Effort ◽

Po River ◽

Average Value ◽

Po River Basin

Abstract. Snow cover maps provide information of great practical interest for hydrologic purposes: when combined with point values of snow water equivalent (SWE), they enable estimation of the regional snow resource. In this context, Earth observation satellites are an interesting tool for evaluating large scale snow distribution and extension. MODIS (MODerate resolution Imaging Spectroradiometer on board Terra and Aqua satellites) daily Snow Covered Area product has been widely tested and proved to be appropriate for hydrologic applications. However, within a daily map the presence of cloud cover can hide the ground, thus obstructing snow detection. Here, we consider MODIS binary products for daily snow mapping over the Po River basin. Ten years (2003–2012) of MOD10A1 and MYD10A1 snow maps have been analysed and processed with the support of a 500 m resolution Digital Elevation Model (DEM). We first investigate the issue of cloud obstruction, highlighting its dependence on altitude and season. Snow maps seem to suffer the influence of overcast conditions mainly in mountain and during the melting period. Thus, cloud cover highly influences those areas where snow detection is regarded with more interest. In spring, the average percentages of area lying beneath clouds are in the order of 70%, for altitudes over 1000 m a.s.l. Then, starting from previous studies, we propose a cloud removal procedure and we apply it to a wide area, characterized by high geomorphological heterogeneity such as the Po River basin. In conceiving the new procedure, our first target was to preserve the daily temporal resolution of the product. Regional snow and land lines were estimated for detecting snow cover dependence on elevation. In cases when there was not enough information on the same day within the cloud-free areas, we used temporal filters with the aim of reproducing the micro-cycles which characterize the transition altitudes, where snow does not stand continually over the entire winter. In the validation stage, the proposed procedure was compared against others, showing improvements in the performance for our case study. The accuracy is assessed by applying the procedure to clear-sky maps masked with additional cloud cover. The average value is higher than 95% considering 40 days chosen over all seasons. The procedure also has advantages in terms of input data and computational effort requirements.

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Snow Cover and Melting Snow from ERTS Imagery

The Canadian Surveyor ◽

10.1139/tcs-1974-0030 ◽

1974 ◽

Vol 28 (2) ◽

pp. 128-134 ◽

Cited By ~ 3

Author(s):

Douglas L. Golding

Keyword(s):

Thin Film ◽

Snow Cover ◽

Return Period ◽

Infrared Radiation ◽

Cloud Cover ◽

Near Infrared ◽

Infrared Imagery ◽

Transient Phenomena ◽

Melting Snow ◽

Mountain Watersheds

To evaluate the usefulness of ERTS imagery for obtaining information on snow cover for small mountain watersheds, two specific objectives were set: (1) to determine if snowpack ablation due to chinooks can be detected on ERTS imagery, and (2) to determine if melting snow can be distinguished from snow that has not yet begun to melt. The length of ERTS return period and the frequency of cloud cover over the mountains in winter combined to make the ERTS system almost useless in studying transient phenomena of short-return period such as the chinook. Melting snow could be distinguished from snow that had not reached melting temperature. The latter appeared light toned on both visible and near-infrared imagery because of its high reflectivity in these portions of the spectrum. Melting snow, however, appeared dark on near-infrared imagery because much of the incident infrared radiation is absorbed by the thin film of water on the surface of the melting snow.

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The recent developments in cloud removal approaches of MODIS snow cover product

Hydrology and Earth System Sciences ◽

10.5194/hess-23-2401-2019 ◽

2019 ◽

Vol 23 (5) ◽

pp. 2401-2416 ◽

Cited By ~ 10

Author(s):

Xinghua Li ◽

Yinghong Jing ◽

Huanfeng Shen ◽

Liangpei Zhang

Keyword(s):

Snow Cover ◽

Cloud Cover ◽

Hydrological Cycle ◽

Global Climate ◽

Optical Observations ◽

Optical Remote Sensing ◽

Spatial Methods ◽

Fusion Methods ◽

Spatio Temporal ◽

Cloud Removal

Abstract. The snow cover products of optical remote sensing systems play an important role in research into global climate change, the hydrological cycle, and the energy balance. Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are the most popular datasets used in the community. However, for MODIS, cloud cover results in spatial and temporal discontinuity for long-term snow monitoring. In the last few decades, a large number of cloud removal methods for MODIS snow cover products have been proposed. In this paper, our goal is to make a comprehensive summarization of the existing algorithms for generating cloud-free MODIS snow cover products and to expose the development trends. The methods of generating cloud-free MODIS snow cover products are classified into spatial methods, temporal methods, spatio-temporal methods, and multi-source fusion methods. The spatial methods and temporal methods remove the cloud cover of the snow product based on the spatial patterns and temporal changing correlation of the snowpack, respectively. The spatio-temporal methods utilize the spatial and temporal features of snow jointly. The multi-source fusion methods utilize the complementary information among different sources among optical observations, microwave observations, and station observations.

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An examination of the differences between surface and free-air temperature trend at high-elevation sites: Relationships with cloud cover, snow cover, and wind

Journal of Geophysical Research Atmospheres ◽

10.1029/2005jd006150 ◽

2005 ◽

Vol 110 (D24) ◽

Cited By ~ 23

Author(s):

N. C. Pepin ◽

J. R. Norris

Keyword(s):

Snow Cover ◽

Air Temperature ◽

Cloud Cover ◽

High Elevation ◽

Temperature Trend ◽

Free Air

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Improvement of the Snow Depth in the Common Land Model by Coupling a Two-Dimensional Deterministic Ensemble Model with a Variational Hybrid Snow Cover Fraction Data Assimilation Scheme and a New Observation Operator

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0149.1 ◽

2017 ◽

Vol 18 (1) ◽

pp. 119-138 ◽

Cited By ~ 3

Author(s):

Jianhui Xu ◽

Feifei Zhang ◽

Hong Shu ◽

Kaiwen Zhong

Keyword(s):

Data Assimilation ◽

Snow Cover ◽

Snow Depth ◽

Cloud Cover ◽

Two Dimensional ◽

Observation Operator ◽

Snow Cover Fraction ◽

Depletion Curve ◽

Common Land Model ◽

The Common

Abstract During snow cover fraction (SCF) data assimilation (DA), the simplified observation operator and presence of cloud cover cause large errors in the assimilation results. To reduce these errors, a new snow cover depletion curve (SDC), known as an observation operator in the DA system, is statistically fitted to in situ snow depth (SD) observations and Moderate Resolution Imaging Spectroradiometer (MODIS) SCF data from January 2004 to October 2008. Using this new SDC, a two-dimensional deterministic ensemble–variational hybrid DA (2DEnVar) method of integrating the deterministic ensemble Kalman filter (DEnKF) and a two-dimensional variational DA (2DVar) is proposed. The proposed 2DEnVar is then used to assimilate the MODIS SCF into the Common Land Model (CoLM) at five sites in the Altay region of China for data from November 2008 to March 2009. The analysis performance of the 2DEnVar is compared with that of the DEnKF. The results show that the 2DEnVar outperforms the DEnKF as it effectively reduces the bias and root-mean-square error during the snow accumulation and ablation periods at all sites except for the Qinghe site. In addition, the 2DEnVar, with more assimilated MODIS SCF observations, produces more innovations (observation minus forecast) than the DEnKF, with only one assimilated MODIS SCF observation. The problems of cloud cover and overestimation are addressed by the 2DEnVar.

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Brief Communication: Updated GAMDAM Glacier Inventory over the High Mountain Asia

10.5194/tc-2018-139 ◽

2018 ◽

Cited By ~ 1

Author(s):

Akiko Sakai

Keyword(s):

Snow Cover ◽

Cloud Cover ◽

High Mountain ◽

Glacier Area ◽

Landsat Images ◽

Glacier Inventory ◽

Final Version ◽

Seasonal Snow ◽

Steep Slopes ◽

Seasonal Snow Cover

Abstract. The first version of the Glacier Area Mapping for Discharge from the Asian Mountains (GAMDAM) glacier inventory was the first methodologically consistent glacier inventory covering High Mountain Asia, and it underestimated glacier area because it did not include steep slopes covered with ice or snow and shadowed areas. During the process of revising the GAMDAM glacier inventory, source Landsat images were carefully selected to find images free of shadows, cloud cover, and seasonal snow cover taken from 1990 to 2010. Then, more than 90 % of the glacier area in the final version of the GAMDAM glacier inventory was delineated based on summer Landsat images. The total glacier area was 100,693±15,103 km2 and included 134,770 glaciers using 453 Landsat image scenes.

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An improved Terra–Aqua MODIS snow cover and Randolph Glacier Inventory 6.0 combined product (MOYDGL06*) for high-mountain Asia between 2002 and 2018

Earth System Science Data ◽

10.5194/essd-12-345-2020 ◽

2020 ◽

Vol 12 (1) ◽

pp. 345-356 ◽

Cited By ~ 2

Author(s):

Sher Muhammad ◽

Amrit Thapa

Keyword(s):

Water Resources ◽

Snow Cover ◽

Cloud Cover ◽

High Mountain ◽

Landsat 8 ◽

Spatial Filters ◽

Glacier Inventory ◽

Input Dataset ◽

Moderate Resolution Imaging Spectroradiometer ◽

Improved Accuracy

Abstract. Snow is a significant component of the ecosystem and water resources in high-mountain Asia (HMA). Therefore, accurate, continuous, and long-term snow monitoring is indispensable for the water resources management and economic development. The present study improves the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites 8 d (“d” denotes “day”) composite snow cover Collection 6 (C6) products, named MOD10A2.006 (Terra) and MYD10A2.006 (Aqua), for HMA with a multistep approach. The primary purpose of this study was to reduce uncertainty in the Terra–Aqua MODIS snow cover products and generate a combined snow cover product. For reducing underestimation mainly caused by cloud cover, we used seasonal, temporal, and spatial filters. For reducing overestimation caused by MODIS sensors, we combined Terra and Aqua MODIS snow cover products, considering snow only if a pixel represents snow in both the products; otherwise it is classified as no snow, unlike some previous studies which consider snow if any of the Terra or Aqua product identifies snow. Our methodology generates a new product which removes a significant amount of uncertainty in Terra and Aqua MODIS 8 d composite C6 products comprising 46 % overestimation and 3.66 % underestimation, mainly caused by sensor limitations and cloud cover, respectively. The results were validated using Landsat 8 data, both for winter and summer at 20 well-distributed sites in the study area. Our validated adopted methodology improved accuracy by 10 % on average, compared to Landsat data. The final product covers the period from 2002 to 2018, comprising a combination of snow and glaciers created by merging Randolph Glacier Inventory version 6.0 (RGI 6.0) separated as debris-covered and debris-free with the final snow product MOYDGL06*. We have processed approximately 746 images of both Terra and Aqua MODIS snow containing approximately 100 000 satellite individual images. Furthermore, this product can serve as a valuable input dataset for hydrological and glaciological modelling to assess the melt contribution of snow-covered areas. The data, which can be used in various climatological and water-related studies, are available for end users at https://doi.org/10.1594/PANGAEA.901821 (Muhammad and Thapa, 2019).

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Dynamics of Maximum Snow Cover Area and Snow Line Altitude Across Nepal (2003-2018) Using Improved MODIS Data

Journal of Institute of Science and Technology ◽

10.3126/jist.v25i2.33729 ◽

2020 ◽

Vol 25 (2) ◽

pp. 17-24

Author(s):

Nitesh Khadka ◽

Shravan Kumar Ghimire ◽

Xiaoqing Chen ◽

Sudeep Thakuri ◽

...

Keyword(s):

Snow Cover ◽

Monsoon Season ◽

Temporal Variations ◽

Vital Role ◽

Eastern Region ◽

Snow Cover Area ◽

Post Monsoon ◽

Snow Line ◽

Elevation Zone ◽

Spatio Temporal

Snow is one of the main components of the cryosphere and plays a vital role in the hydrology and regulating climate. This study presents the dynamics of maximum snow cover area (SCA) and snow line altitude (SLA) across the Western, Central, and Eastern Nepal using improved Moderate Resolution Imaging Spectroradiometer (MODIS; 500 m) data from 2003 to 2018. The results showed a heterogeneous behavior of the spatial and temporal variations of SCA in different months, seasons, and elevation zones across three regions of Nepal. Further, the maximum and minimum SCA was observed in winter (December-February) and post-monsoon (October-November) seasons, respectively. The inter-annual variation of winter SCA showed an overall negative trend of SCA between 2003 to 2018 at the national and regional scales. The SLA was assessed in the post-monsoon season. At the national scale, the SLA lies in an elevation zone of 4500-5000 m, and the approximate SLA of Nepal was 4750 m in 2018. Regionally, the SLA lies in an elevation zone of 4500-5000 m in the Western and Central regions (approx. SLA at 4750 m) and 5000-5500 m in the Eastern region (approx. SLA at 5250 m) in 2018. The SLA fluctuated with the changes in SCA, and the spatio-temporal variations of SLAs were observed in three regions of Nepal. We observed an upward shift of SLA by 33.3 m yr-1 in the Western and Central Nepal and by 66.7 m yr-1 in Eastern Nepal. This study will help to understand the impacts of climate change on snow cover, and the information will be useful for the hydrologist and water resource managers.

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