Optical Remote Sensing of Snow Cover

2016 ◽  
pp. 115-137 ◽  
Author(s):  
Marie Dumont ◽  
Simon Gascoin
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Optical Remote Sensing

Automatic mapping of snow cover depletion curves using optical remote sensing data under conditions of frequent cloud cover and temporary snow

2008 ◽  
Vol 22 (16) ◽  
pp. 2930-2942 ◽  
Author(s):  
Baolin Li ◽  
A‐Xing Zhu ◽  
Chenghu Zhou ◽  
Yichi Zhang ◽  
Tao Pei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Cloud Cover ◽  
Remote Sensing Data ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Automatic Mapping

Dry and Wet Snow Cover Mapping in Mountain Areas Using SAR and Optical Remote Sensing Data

2017 ◽  
Vol 10 (6) ◽  
pp. 2575-2588 ◽  
Author(s):  
Guangjun He ◽  
Xuezhi Feng ◽  
Pengfeng Xiao ◽  
Zhenghuan Xia ◽  
Zuo Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Remote Sensing Data ◽  
Optical Remote Sensing ◽  
Mountain Areas ◽  
Sensing Data ◽  
Wet Snow

scholarly journals Spatiotemporal dynamics of snow cover based on multi-source remote sensing data in China

2016 ◽  
Vol 10 (5) ◽  
pp. 2453-2463 ◽  
Author(s):  
Xiaodong Huang ◽  
Jie Deng ◽  
Xiaofang Ma ◽  
Yunlong Wang ◽  
Qisheng Feng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Snow Depth ◽  
Winter Season ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
The Tibetan Plateau ◽  
Optical Remote Sensing ◽  
The North ◽  
The Mean

Abstract. By combining optical remote sensing snow cover products with passive microwave remote sensing snow depth (SD) data, we produced a MODIS (Moderate Resolution Imaging Spectroradiometer) cloudless binary snow cover product and a 500 m snow depth product. The temporal and spatial variations of snow cover from December 2000 to November 2014 in China were analyzed. The results indicate that, over the past 14 years, (1) the mean snow-covered area (SCA) in China was 11.3 % annually and 27 % in the winter season, with the mean SCA decreasing in summer and winter seasons, increasing in spring and fall seasons, and not much change annually; (2) the snow-covered days (SCDs) showed an increase in winter, spring, and fall, and annually, whereas they showed a decrease in summer; (3) the average SD decreased in winter, summer, and fall, while it increased in spring and annually; (4) the spatial distributions of SD and SCD were highly correlated seasonally and annually; and (5) the regional differences in the variation of snow cover in China were significant. Overall, the SCD and SD increased significantly in south and northeast China, and decreased significantly in the north of Xinjiang province. The SCD and SD increased on the southwest edge and in the southeast part of the Tibetan Plateau, whereas it decreased in the north and northwest regions.

Subpixel mapping of snow cover in forests by optical remote sensing

2003 ◽  
Vol 84 (1) ◽  
pp. 69-82 ◽  
Author(s):  
Dagrun Vikhamar ◽  
Rune Solberg
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Optical Remote Sensing ◽  
Subpixel Mapping

scholarly journals A Compilation of Snow Cover Datasets for Svalbard: A Multi-Sensor, Multi-Model Study

2021 ◽  
Vol 13 (10) ◽  
pp. 2002
Author(s):  
Hannah Vickers ◽  
Eirik Malnes ◽  
Ward J. J. van Pelt ◽  
Veijo A. Pohjola ◽  
Mari Anne Killie ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Spatial Resolution ◽  
Spatial Data ◽  
Water Resource Management ◽  
Climate Impact ◽  
Optical Remote Sensing ◽  
Snow Cover Fraction ◽  
Model Study ◽  
Snow Model

Reliable and accurate mapping of snow cover are essential in applications such as water resource management, hazard forecasting, calibration and validation of hydrological models and climate impact assessments. Optical remote sensing has been utilized as a tool for snow cover monitoring over the last several decades. However, consistent long-term monitoring of snow cover can be challenging due to differences in spatial resolution and retrieval algorithms of the different generations of satellite-based sensors. Snow models represent a complementary tool to remote sensing for snow cover monitoring, being able to fill in temporal and spatial data gaps where a lack of observations exist. This study utilized three optical remote sensing datasets and two snow models with overlapping periods of data coverage to investigate the similarities and discrepancies in snow cover estimates over Nordenskiöld Land in central Svalbard. High-resolution Sentinel-2 observations were utilized to calibrate a 20-year MODIS snow cover dataset that was subsequently used to correct snow cover fraction estimates made by the lower resolution AVHRR instrument and snow model datasets. A consistent overestimation of snow cover fraction by the lower resolution datasets was found, as well as estimates of the first snow-free day (FSFD) that were, on average, 10–15 days later when compared with the baseline MODIS estimates. Correction of the AVHRR time series produced a significantly slower decadal change in the land-averaged FSFD, indicating that caution should be exercised when interpreting climate-related trends from earlier lower resolution observations. Substantial differences in the dynamic characteristics of snow cover in early autumn were also present between the remote sensing and snow model datasets, which need to be investigated separately. This work demonstrates that the consistency of earlier low spatial resolution snow cover datasets can be improved by using current-day higher resolution datasets.

Optical remote sensing of the ocean

1984 ◽  
Author(s):  
W. HOVIS
Keyword(s):  
Remote Sensing ◽  
Optical Remote Sensing
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