سابِق|تال

Performance assessment of the operational MODIS BRDF model for snow/ice cover type

  • role： First author第一作者
  • Affiliation：

    Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • Email：danxin@mail.bnu.edu.cn
  • Introduction：丁安心，1993年生，男，硕士研究生，研究方向为定量遥感、地表反照率。E-mail：danxin@mail.bnu.edu.cn
DING Anxin1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

  • Email：jiaozt@ bnu.edu.cn
  • Introduction：焦子锑，1970年生，男，教授，研究方向为定量遥感和多角度光学遥感的建模。E-mail：jiaozt@ bnu.edu.cn
JIAO Ziti12*,  
  • Affiliation：

    Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

DONG Yadong1,  
  • Affiliation：

    Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

ZHANG Xiaoning1,  
  • Affiliation：

    Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

HE Dandan1,  
  • Affiliation：

    Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

CUI Lei1,  
  • Affiliation：

    Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

YIN Siyang1,  
  • Affiliation：

    Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

CHANG Yaxuan1

ملخص

Snow/ice surface albedo is an important climate parameter because it governs the surface energy budget on the earth–atmosphere system. MODIS snow/ice albedo products, which are generated on the basis of a kernel-driven Rossthick-LisparseR (RTLSR) model, are extensively used in quantitative remote sensing community. However, the model is developed on the basis of the physics of soil–vegetation system. In theory, the scattering properties between the snow/ice and soil/vegetation systems are distinctly different, thereby causing potential problems in characterizing a Bidirectional Reflectance Distribution Function (BRDF) and albedo for snow/ice cover type using this operational model. In this study, we assess the ability of the operational RTLSR model in characterizing the BRDF and retrieving the surface albedo of snow/ice using Polarization and Directionality of the Earth’s Reflectances (POLDER) BRDF data. To verify and compare BRDF/albedo for snow and ice using high-quality collected POLDER observations, we introduce an extensively used asymptotic radiative transfer (ART) model to estimate the BRDF/albedo surface with POLDER observations as a reference given the lack of in situ albedo observations. Results show that (1) the RTLSR model fits the POLDER observations with higher accuracy than the result derived using the ART model. The root mean square error (RMSE) reaches 0.0498 in the 1020 nm band, which increases by 53.70% in comparison with the result using the ART model. (2) The resultant albedos are significantly influenced by the inversion of the RTLSR model for the snow/ice cover type in comparison with the result using the ART model. The RMSE and bias between the two models reach 0.0333 and 0.0274, correspondingly, thus indicating a significant underestimation of surface albedos using the RTLSR model in comparison with the ART model. However, determination coefficient (R2) reaches 0.529, which indicates a high-level correlation between the RTLSR and ART models. Our findings show that the current operational RTLSR model cannot characterize the directional reflectance of snow/ice, thereby affecting the model’s capability in estimating the surface albedo for snow/ice, and thus, this model must be modified. Such development can improve the inversion accuracy of surface albedos when this model is used as an operational algorithm for snow/ice cover type in the future.

مفهوم

POLDER;BRDF;RTLSR;ART;snow/ice reflectance;snow/ice albedo;model estimating

References

  1. 1.
    Bai D N, Jiao Z T, Dong Y D, Zhang X N, Li Y and He D D. 2017. Analysis of the sensitivity of the anisotropic flat index to vegetation parameters based on the two-layer canopy reflectance model. Journal of Remote Sensing, 21(1): 1–11
  2. 2.
    Burakowski E A, Ollinger S V, Lepine L, Schaaf C B, Wang Z S, Dibb J E, Hollinger D Y, Kim J, Erb A and Martin M. 2015. Spatial scaling of reflectance and surface albedo over a mixed-use, temperate forest landscape during snow-covered periods. Remote Sensing of Environment, 158: 465–477
  3. 3.
    Csiszar I and Gutman G. 1999. Mapping global land surface albedo from NOAA AVHRR. Journal of Geophysical Research: Atmospheres, 104(D6): 6215–6228
  4. 4.
    Doyle M and Dorling S. 2002. Visibility trends in the UK 1950-1997. Atmospheric Environment, 36(19): 3161–3172
  5. 5.
    Henderson-Sellers A and Wilson M F. 1983. Surface albedo data for climatic modeling. Reviews of Geophysics, 21(8): 1743–1778
  6. 6.
    Hudson S R, Warren S G, Brandt R E, Grenfell T C and Six D. 2006. Spectral bidirectional reflectance of Antarctic snow: measurements and parameterization. Journal of Geophysical Research: Atmospheres, 111(D18): D18106
  7. 7.
    Jacob F and Olioso A. 2005. Derivation of diurnal courses of albedo and reflected solar irradiance from airborne POLDER data acquired near solar noon. Journal of Geophysical Research: Atmospheres, 110(D10): D10104
  8. 8.
    Jiang X. 2006. Progress in the research of snow and ice albedo. Journal of Glaciology and Geocryology, 28(5): 728–738
  9. 9.
    Jiao Z T, Zhang H, Dong Y D, Liu Q, Xiao Q and Li X W. 2015. An algorithm for retrieval of surface albedo from small view-angle airborne observations through the use of BRDF archetypes as prior knowledge. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(7): 3279–3293
  10. 10.
    Kimes D S. 1983. Dynamics of directional reflectance factor distributions for vegetation canopies. Applied Optics, 22(9): 1364–1372
  11. 11.
    Kokhanovsky A and Schreier M. 2009. The determination of snow specific surface area, albedo and effective grain size using AATSR space-borne measurements. International Journal of Remote Sensing, 30(4): 919–933
  12. 12.
    Kokhanovsky A A and Breon F M. 2012. Validation of an analytical snow BRDF model using parasol multi-angular and multispectral observations. IEEE Geoscience and Remote Sensing Letters, 9(5): 928–932
  13. 13.
    Kokhanovsky A A and Zege E P. 2004. Scattering optics of snow. Applied Optics, 43(7): 1589–1602
  14. 14.
    Liang S L. 2001. Narrowband to broadband conversions of land surface albedo I: algorithms. Remote Sensing of Environment, 76(2): 213–238
  15. 15.
    Liang S L, Shuey C J, Russ A L, Fang H L, Chen M Z, Walthall C L, Daughtry C S T and Hunt R Jr. 2003. Narrowband to broadband conversions of land surface albedo: II. Validation. Remote Sensing of Environment, 84(1): 25–41
  16. 16.
    Liu Q, Wang L Z, Qu Y, Liu N F, Liu S H, Tang H R and Liang S L. 2013. Preliminary evaluation of the long-term GLASS albedo product. International Journal of Digital Earth, 6(sup1): 69–95
  17. 17.
    Lucht W, Schaaf C B and Strahler A H. 2000. An algorithm for the retrieval of albedo from space using semiempirical BRDF models. IEEE Transactions on Geoscience and Remote Sensing, 38(2): 977–998
  18. 18.
    Lyapustin A, Gatebe C K, Kahn R, Brandt R, Redemann J, Russell P, King M D, Pedersen C A, Gerland S, Poudyal R, Marshak A, Wang Y, Schaaf C, Hall D and Kokhanovsky A. 2010. Analysis of snow bidirectional reflectance from ARCTAS spring-2008 campaign. Atmospheric Chemistry and Physics, 10(9): 4359–4375
  19. 19.
    Mote T L. 2008. On the role of snow cover in depressing air temperature. Journal of Applied Meteorology and Climatology, 47(7): 2008–2022
  20. 20.
    Negi H S and Kokhanovsky A. 2011. Retrieval of snow grain size and albedo of western Himalayan snow cover using satellite data. The Cryosphere, 5(4): 831–847
  21. 21.
    Pan H Z, Wang J and Li H Y. 2015a. Retrieval snow albedo using MODIS based on the ART model. Remote sensing Technology and Application, 30(6): 1059–1065
  22. 22.
    Pan H Z, Wang J and Li H Y. 2015b. Accuracy validation of the MODIS snow albedo products and estimate of the snow albedo under cloud over the Qilian Mountains. Journal of Glaciology and Geocryology, 37(1): 49–57
  23. 23.
    Qu Y, Liang S L, Liu Q, He T, Liu S H and Li X W. 2015. Mapping surface broadband albedo from satellite observations: a review of literatures on algorithms and products. Remote Sensing, 7(1): 990–1020
  24. 24.
    Qu Y, Liang S L, Liu Q, Li X J, Feng Y B and Liu S H. 2016. Estimating Arctic sea-ice shortwave albedo from MODIS data. Remote Sensing of Environment, 186: 32–46
  25. 25.
    Qu Y, Liu Q, Liang S L, Wang L Z, Liu N F and Liu S H. 2014. Direct-estimation algorithm for mapping daily land-surface broadband albedo from MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 52(2): 907–919
  26. 26.
    Qu Y, Liu Q and Liu S H. 2016. A forward kernel function for fitting in situ measured snow bidirectional reflectance factor. Spectroscopy and Spectral Analysis, 36(9): 2749–2754
  27. 27.
    Roujean J L, Leroy M and Deschamps P Y. 1992. A bidirectional reflectance model of the earth’s surface for the correction of remote sensing data. Journal of Geophysical Research: Atmospheres, 97(D18): 20455–20468
  28. 28.
    Schaaf C B, Gao F, Strahler A H, Lucht W, Li X W, Tsang T, Strugnell N C, Zhang X Y, Jin Y F, Muller J P, Lewis P, Barnsley M, Hobson P, Disney M, Roberts G, Dunderdale M, Doll C, D’Entremont R P, Hu B X, Liang S L, Privette J L and Roy D. 2002. First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sensing of Environment, 83(1/2): 135–148
  29. 29.
    Shao D H, Li H Y, Wang J, Hao X H, Wang R K and Ma Y. 2017. Retrieval of snow albedo based on multi-source remote sensing data. Remote Sensing Technology and Application, 32(1): 71–77, 139
  30. 30.
    Shi H Y, Xiao Z Q, Liang S L and Ma H. 2017. A method for consistent estimation of multiple land surface parameters from MODIS top-of-atmosphere time series data. IEEE Transactions on Geoscience and Remote Sensing, 55(9): 5158–5173
  31. 31.
    Stroeve J, Box J E, Gao F, Liang S L, Nolin A and Schaaf C. 2005. Accuracy assessment of the MODIS 16-day albedo product for snow: comparisons with Greenland in situ measurements. Remote Sensing of Environment, 94(1): 46–60
  32. 32.
    Stroeve J C, Box J E and Haran T. 2006. Evaluation of the MODIS (MOD10A1) daily snow albedo product over the Greenland ice sheet. Remote Sensing of Environment, 105(2): 155–171
  33. 33.
    Vermote E F, Tanre D, Deuze J L, Herman M and Morcette J J. 1997. Second simulation of the satellite signal in the solar spectrum, 6S: an overview. IEEE Transactions on Geoscience and Remote Sensing, 35(3): 675–686
  34. 34.
    Wang D D, Liang S L, Zhou Y, He T and Yu Y Y. 2017. A new method for retrieving daily land surface albedo from VIIRS data. IEEE Transactions on Geoscience and Remote Sensing, 55(3): 1765–1775
  35. 35.
    Wang J, Hao X H and Wang J. 2014. Validation the asymptotic radiative transfer theory by using multi-angular snow spectrum. Journal of Glaciology and Geocryology, 36(2): 386–393
  36. 36.
    Wu H Y, Liang S L, Tong L and He T. 2011. Snow BRDF characteristics from MODIS and MISR data//Proceedings of 2011 IEEE International Geoscience and Remote Sensing Symposium. Vancouver, BC, Canada: IEEE: 3187-3190
  37. 37.
    Wu H Y, Tong L and Li Y X. 2010. Long term validation of moderate resolution and multi-angle remote sensing snow albedo over Greenland//Proceedings of the 3rd International Congress on Image and Signal Processing. Yantai, China: IEEE: 2198-2202

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