Estimation of snow depth from passive microwave brightness temperature data in forest regions of northeast China

Estimation of snow depth from passive microwave brightness temperature data in forest regions of... Snow depth is an important factor in water resources management in Northeast China. Forest covers 40% of Northeast China, and the presence of forests influences the accuracy of snow depth retrievals from passive microwave remote sensing data. An optimal iteration method was used to retrieve the forest transmissivities at 18 and 36GHz based on the snow and forest microwave radiative transfer models and the snow properties measured in field experiments. The transmissivities at 18 and 36GHz are 0.895 and 0.656 in the horizontal polarization, and 0.821 and 0.615 in the vertical polarization, respectively. Furthermore, the forest transmissivity and snow properties were input into the Microwave Emission Model of Layered Snowpacks (MEMLS) to establish a dynamic look-up table (LUT). Snow depths were retrieved from satellite passive microwave remote sensing data based on the LUT method, and these retrievals were verified by snow depth observations at 103 meteorological stations. The results showed that the bias between the retrieved and measured snow depths is very small, with root mean square errors (RMSEs) of approximately 6cm in forest regions and 4cm in non-forest regions. When compared with the existing snow products, the snow depth retrieved in this work presented the highest level of accuracy. The regional snow depth product in China is superior to the GlobSnow and NASA AMSR-E standard SWE products in non-forest regions, whereas the GlobSnow estimate is superior to the regional snow depth product in China and NASA AMSR-E standard SWE product estimates in forest regions. Therefore, we conclude that 1) the influence of forest on snow depth retrieval is important, and the appropriate forest parameters should be considered in the estimation of snow depth from passive microwave brightness temperature data; and 2) the snow depth retrieval algorithm based on the dynamic LUT method proved to be efficient in Northeast China. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Remote Sensing of Environment Elsevier

Estimation of snow depth from passive microwave brightness temperature data in forest regions of northeast China

Loading next page...
 
/lp/elsevier/estimation-of-snow-depth-from-passive-microwave-brightness-temperature-gIR41v7w2R
Publisher
Elsevier
Copyright
Copyright © 2016 Elsevier Inc.
ISSN
0034-4257
D.O.I.
10.1016/j.rse.2016.06.005
Publisher site
See Article on Publisher Site

Abstract

Snow depth is an important factor in water resources management in Northeast China. Forest covers 40% of Northeast China, and the presence of forests influences the accuracy of snow depth retrievals from passive microwave remote sensing data. An optimal iteration method was used to retrieve the forest transmissivities at 18 and 36GHz based on the snow and forest microwave radiative transfer models and the snow properties measured in field experiments. The transmissivities at 18 and 36GHz are 0.895 and 0.656 in the horizontal polarization, and 0.821 and 0.615 in the vertical polarization, respectively. Furthermore, the forest transmissivity and snow properties were input into the Microwave Emission Model of Layered Snowpacks (MEMLS) to establish a dynamic look-up table (LUT). Snow depths were retrieved from satellite passive microwave remote sensing data based on the LUT method, and these retrievals were verified by snow depth observations at 103 meteorological stations. The results showed that the bias between the retrieved and measured snow depths is very small, with root mean square errors (RMSEs) of approximately 6cm in forest regions and 4cm in non-forest regions. When compared with the existing snow products, the snow depth retrieved in this work presented the highest level of accuracy. The regional snow depth product in China is superior to the GlobSnow and NASA AMSR-E standard SWE products in non-forest regions, whereas the GlobSnow estimate is superior to the regional snow depth product in China and NASA AMSR-E standard SWE product estimates in forest regions. Therefore, we conclude that 1) the influence of forest on snow depth retrieval is important, and the appropriate forest parameters should be considered in the estimation of snow depth from passive microwave brightness temperature data; and 2) the snow depth retrieval algorithm based on the dynamic LUT method proved to be efficient in Northeast China.

Journal

Remote Sensing of EnvironmentElsevier

Published: Sep 15, 2016

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

Monthly Plan

  • Read unlimited articles
  • Personalized recommendations
  • No expiration
  • Print 20 pages per month
  • 20% off on PDF purchases
  • Organize your research
  • Get updates on your journals and topic searches

$49/month

Start Free Trial

14-day Free Trial

Best Deal — 39% off

Annual Plan

  • All the features of the Professional Plan, but for 39% off!
  • Billed annually
  • No expiration
  • For the normal price of 10 articles elsewhere, you get one full year of unlimited access to articles.

$588

$360/year

billed annually
Start Free Trial

14-day Free Trial