Improvement of snow depth retrieval for FY3B-MWRI in China (original) (raw)

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

The primary objective of this work is to develop an operational snow depth retrieval algorithm for the FengYun3B Microwave Radiation Imager (FY3B-MWRI) in China. Based on 7-year (2002–2009) observations of brightness temperature by the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and snow depth from Chinese meteorological stations, we develop a semi-empirical snow depth retrieval algorithm. When its land cover fraction is larger than 85%, we regard a pixel as pure at the satellite passive microwave remote-sensing scale. A 1-km resolution land use/land cover (LULC) map from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences, is used to determine fractions of four main land cover types (grass, farmland, bare soil, and forest). Land cover sensitivity snow depth retrieval algorithms are initially developed using AMSR-E brightness temperature data. Each grid-cell snow depth was estimated as the sum of snow depths from each land cover algorithm weighted by percentages of land cover types within each grid cell. Through evaluation of this algorithm using station measurements from 2006, the root mean square error (RMSE) of snow depth retrieval is about 5.6 cm. In forest regions, snow depth is underestimated relative to ground observation, because stem volume and canopy closure are ignored in current algorithms. In addition, comparison between snow cover derived from AMSR-E and FY3B-MWRI with Moderate-resolution Imaging Spectroradiometer (MODIS) snow cover products (MYD10C1) in January 2010 showed that algorithm accuracy in snow cover monitoring can reach 84%. Finally, we compared snow water equivalence (SWE) derived using FY3B-MWRI with AMSR-E SWE products in the Northern Hemisphere. The results show that AMSR-E overestimated SWE in China, which agrees with other validations.

Access this article

Log in via an institution

Subscribe and save

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

• Cao M S, Li P J, Robinson D A, et al. 1993. Evaluation and primary application of microwave remote sensing SMMR derived snow cover in Western China. J Rem Sens, 8: 260–269
  Google Scholar
• Chang A T C, Foster J L, Hall D K. 1987. Nimbus-7 SMMR derived global snow cover parameters. Ann Glaciol, 9: 39–44
  Google Scholar
• Chang A T C, Gloersen P, Schmugge T J, et al. 1976. Microwave emission from snow and glacier ice. J Glaciol, 16: 23–29
  Google Scholar
• Chang S, Shi J C, Jiang L M, et al. 2009. Improved snow depth retrieval algorithm in China area using passive microwave remote sensing data. In: Proceedings of 2009 IEEE International Geoscience and Remote Sensing Symposium. II: 614–617
  Google Scholar
• Che T, Li X, Jin R, et al. 2008. Snow depth derived from passive microwave remote-sensing data in China. Ann Glaciol, 49: 145–154
  Article Google Scholar
• Derksen C, Toose P, Rees A, et al. 2010. Development of a tundra-specific snow water equivalent retrieval algorithm for satellite passive microwave data. Remote Sens Environ, 114: 1699–1709
  Article Google Scholar
• Derksen C, Walker A, Goodison B. 2005. Evaluation of passive microwave snow water equivalent retrievals across the boreal forest/tundra of western Canada. Remote Sens Environ, 96: 315–327
  Article Google Scholar
• Derksen C. 2008. The contribution of AMSR-E 18.7 and 10.7 GHz measurements to improved boreal forest snow water equivalent retrievals. Remote Sens Environ, 112: 2701–2710
  Article Google Scholar
• Foster J L, Chang A T C, Hall D K. 1997. Comparison of snow mass estimates from a prototype passive microwave snow algorithm, a revised algorithm and a snow depth climatology. Remote Sens Environ, 62: 132–142
  Article Google Scholar
• Foster J L, Hall D K, Chang A T C, et al. 1984. An overview of passive microwave snow research and results. Rev Geophys Space Phys, 22: 195–208
  Article Google Scholar
• Goïta K, Walker A, Goodison B. 2003. Algorithm development for the estimation of snow water equivalent in the boreal forest using passive microwave data. Int J Remote Sens, 24: 1097–1102
  Article Google Scholar
• Grody N C, Basist A N. 1996. Global identification of snowcover using SSM/I measurements. IEEE Trans Geosci Remote Sens, 34: 237–249
  Article Google Scholar
• Grody N C. 1991. Classification of snow cover and precipitation using the special sensor microwave imager. J Geophys Res, 96: 7423–7435
  Article Google Scholar
• Guo Y, Shi J, Du J, et al. 2011. A topographic correction technique for satellite radiometer measurement with high resolution DEM. Int J Remote Sens, 32: 8899–8913
  Article Google Scholar
• Hallikainen M T, Jolma P. 1992. Comparison of algorithms for retrieval of snow water equivalent from Nimbus-7 SMMR data in Finland. IEEE Trans Geosci Remote Sens, 30: 124–131
  Article Google Scholar
• Hofer R, Mätzler C. 1980. Investigation of snow parameters by radiometry in the 3- to 60-mm wavelength region. J Geophys Res, 85: 453–460
  Article Google Scholar
• Kelly R E J. 2009. The AMSR-E snow depth algorithm: Description and initial results. J Remote Sens Soc Japan, 29: 307–317
  Google Scholar
• Koenig L S, Forster R R. 2004. Evaluation of passive microwave snow water equivalent algorithms in the depth hoar-dominated snowpack of the Kuparuk River Watershed, Alaska, USA. Remote Sens Environ, 93: 511–527
  Article Google Scholar
• Liston E G. 2004. Representing subgrid snow cover heterogeneities in regional and global models. J Climate, 17: 1381–1397
  Article Google Scholar
• Li X J, Liu Y J, Zhu X X, et al. 2007. Snow cover identification with SSM/I data in China. J Appl Meteor Sci, 18: 12–20
  Google Scholar
• Rott H, Sturm K. 1991. Microwave signature measurements of Antarctic and Alpine snow. Proceedings of the 11th EARSeL Symposium, European Association of Remote Sensing Laboratories in Boulogne-Billancourt. 140–151
  Google Scholar
• Savoie M H, Armstrong R L, Brodzik M J, et al. 2009. Atmospheric corrections for improved satellite passive microwave snow cover retrievals over the Tibet Plateau. Remote Sens Environ, 113: 2661–2669
  Article Google Scholar
• Shuman C, Alley R. 1993. Spatial and temporal characterization of hoar formation in central Greenland using SSM/I brightness temperatures. Geophys Res Lett, 20: 2643–2646
  Article Google Scholar
• Singh P R, Gan T Y. 2000. Retrieval of snow water equivalent using passive microwave brightness temperature data. Remote Sens Environ, 74: 275–286
  Article Google Scholar
• Sun Z W. 2007. Estimate snow depth and snow water equivalent algorithm for FY-3 MWRI and development of system. Master Thesis. Beijing: Beijing Normal University
  Google Scholar
• Tait A B. 1998. Estimation of snow water equivalent using passive microwave radiation data. Remote Sens Environ, 64: 286–291
  Article Google Scholar
• Tedesco M, Wang J R. 2006. Atmospheric correction of AMSR-E brightness temperatures for dry snow cover mapping. IEEE Trans Geosci Remote Sens Lett, 3: 320–324
  Article Google Scholar
• Ulaby F T, Moore R K, Fung A. 1981. Microwave remote sensing: Active and passive, vol. 1: Microwave remote sensing fundamentals and radiometry. Reading, Massachusetts: Addison-Wesley-Longman. 456
  Google Scholar
• Wang J R, Tedesco M. 2007. Identification of atmospheric influences on the estimation of snow water equivalent from AMSR-E measurements. Remote Sens Environ, 111: 398–408
  Article Google Scholar
• Wang P, Jiang L, Zhang L, et al. 2010. Impact of terrain topography on retrieval of snow water equivalence using passive microwave remote sensing. In: Proceedings of 2010 IEEE International Geoscience and Remote Sensing Symposium. 1757–1760
  Chapter Google Scholar
• Wu T W, Wu G X. 2004. An empirical formula to compute snow cover fraction in GCMs. Adv Atmosph Sci, 21: 529–535
  Article Google Scholar
• Yang H, Weng F Z, Lv L Q, et al. 2011a. The FengYun-3 microwave radiation imager on-orbit verification. IEEE Trans Geosci Remote Sens, 49: 4552–4560
  Article Google Scholar
• Yang H, Lv L Q, Xu H X, et al. 2011b. Evaluation of FY3B-MWRI instrument on-orbit calibration accuracy. In: Proceedings of 2011 IEEE International Geoscience and Remote Sensing Symposium. 2252–2254
  Chapter Google Scholar

Download references

Author information

Authors and Affiliations

1. State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing, 100875, China
   LingMei Jiang, Pei Wang, LiXin Zhang & JunTao Yang
2. National Satellite Meteorological Center, China Meteorological Administration, Beijing, 100081, China
   Hu Yang

Authors

1. LingMei Jiang
   You can also search for this author inPubMed Google Scholar
2. Pei Wang
   You can also search for this author inPubMed Google Scholar
3. LiXin Zhang
   You can also search for this author inPubMed Google Scholar
4. Hu Yang
   You can also search for this author inPubMed Google Scholar
5. JunTao Yang
   You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toLingMei Jiang.

Rights and permissions

About this article

Cite this article

Jiang, L., Wang, P., Zhang, L. et al. Improvement of snow depth retrieval for FY3B-MWRI in China.Sci. China Earth Sci. 57, 1278–1292 (2014). https://doi.org/10.1007/s11430-013-4798-8

Download citation

• Received: 22 February 2013
• Accepted: 25 July 2013
• Published: 13 February 2014
• Issue Date: June 2014
• DOI: https://doi.org/10.1007/s11430-013-4798-8

Keywords