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Estimation of snow depth from MWRI and AMSR-E data in forest regions of Northeast China

Remote Sensing of the Earth’s Cryosphere : Monitoring for operational applications and climate studies. Estimation of snow depth from MWRI and AMSR-E data in forest regions of Northeast China. Tao CHE and Liyun DAI

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Estimation of snow depth from MWRI and AMSR-E data in forest regions of Northeast China

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  1. Remote Sensing of the Earth’s Cryosphere: Monitoring for operational applications and climate studies Estimation of snow depth from MWRI and AMSR-E data in forest regions of Northeast China Tao CHE and LiyunDAI Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences Xingming ZHENG, XiaofengLi, Kai ZHAO Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences 7thEARSeL LISSIG Workshop, February 3-6, 2014 Bern, Switzerland

  2. Content • Objectives • Study area and snow measurement • Methodology • Results and validations • Conclusions

  3. Objectives • To develop a new algorithm of snow retrieval from passive microwave brightness temperature data in forest regions • To evaluate the use of MWRI to estimate snow depth and SWE • To quantify the forest influences on estimation of snow depth

  4. Study area In Northeast China, the forest cover, which represents 40% of the total area, is the most abundant type of land cover: the farmland and grassland cover 30% and 20% of the total area, respectively, and rivers, lakes, residential areas, and other areas cover the remaining 10%.

  5. Snow measurements 4-7 January 2012 (b) 9-14 January 2012 (c) 6-9 March 2012 (d) 9-14 January 2013 Courses length: 4,800 km For each snow layer: Snow thickness, density, grain size, temperature measurements in 76 snow pits Snow depth measurements in 401 points.

  6. Instrument specifications of MWRI on FY3 and AMSR-E on EOS-Aqua

  7. Content • Objectives • Study area and snow measurement • Methodology • Results and validations • Conclusions

  8. Microwave radiative transfer models (1) Snow cover (2) Soil (3) simulated by the microwave emission model of layered snowpacks (MEMLS) ω is set up to 0.05.

  9. Determination of forest transmissivity error

  10. Establishment of look-up table between snow properties and brightness temperature

  11. Content • Objectives • Study area and snow measurement • Methodology • Results and validations • Conclusions

  12. Transmissivities retrieved When the errorreaches its minimum, the transmissivities are 0.656 and 0.895 at 36 and 18 GHz, respectively.

  13. Snow depth retrieved along the snow courses 4-7 January 2012 9-14 January 2012 6-9 March 2012 9-14 January 2013

  14. Snow depth retrieved at meteorological stations

  15. Comparisons with snow products from NSIDC, ESA and WESTDC • the daily global SWE data from the NSIDC (Kelly, 2009; Tedesco and Narvekar, 2010) • the GlobSnow SWE dataset from the ESA (Pulliainen, 2006; Takala et al., 2011) • a snow depth product for China from the WESTDC (Che et al, 2008; Li et al., 2011) Snow depths were converted to SWE by multiplying the snow density that was measured in the meteorological stations every five days (pentad). For forest regions: NEW -> ESA -> WESTDC -> NSIDC For non-forest regions: NEW -> WESTDC -> ESA -> NSIDC

  16. Content • Objectives • Study area and snow measurement • Methodology • Results and validations • Conclusions

  17. Conclusions • The accuracy of the snow depth retrieved can be improved if the snow properties (primarily snow grain size and density) are known in advance. • The influence of forest on the retrieval of snow depth is important, and the single scattering albedo and transmissivity of forest should be considered when estimating snow depth from passive microwave remote sensing data. • The MWRI has similar specifications to AMSR-E, and the latter has not worked since 2010. Therefore, the MWRI can succeed AMSR-E for retrieval of the global SWE data before AMSR2 data are available, which can maintain temporal consistency of the daily global SWE products.

  18. Thank you for your attention!

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