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An algorithm for estimation of surface albedo in 16 m resolution from Chinese GF-1WFV image

  • role： First author第一作者
  • Affiliation：

    Collage of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China

  • Email：202131490009@mail.bnu.edu.cn
  • Introduction：陆彦蓉，研究方向为地表反照率产品验证。E-mail：202131490009@mail.bnu.edu.cn
LU Yanrong1,  
  • Affiliation：

    Collage of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China

LI Xia1,  
  • Affiliation：

    Collage of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China

YANG Kaixiang1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Collage of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China

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

  • Email：toliuqiang@bnu.edu.cn
  • Introduction：刘强，研究方向为多角度遥感模型和反演。E-mail： toliuqiang@bnu.edu.cn
LIU Qiang12*,  
  • Affiliation：

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

    Aerospace Information Research Institute, Chinese Academy of Science, Beijing 100094,China

WEN Jianguang23,  
  • Affiliation：

    Collage of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China

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

LI Xiuhong12

Resümee

Surface albedo plays a vital role in land surface climate and biosphere models. Many researchers and teams use different algorithms to develop high- resolution surface albedo products for better satisfying the requirements of multi-field research and broaden the application of quantitative remote sensing parameters.The proposed algorithm aims to generate albedo products of high spatial resolution based on the sensor data of GF-1 WFV and the GLASS albedo product with 500 m resolution. The idea of the algorithm is to first invert the GF-1 WFV data with a direct inversion algorithm for obtaining the primary albedo product of 16 m resolution. Then, it downscales the GLASS albedo product of 500 m resolution with the texture information of the primary product of 16 m resolution to obtain the final albedo product of 16 m resolution.The algorithm results are verified with ground observations at eight stations in the Heihe Experimental Area using the data from 2016 to 2017. The time series graphs of the measured data and the inverted fusion data show that the fused albedo product of 16 m resolution agrees well with the measured value. At the same time, through the analysis of the scatter plots of all stations from 2016 to 2017, the root mean square error of the fusion albedo is 0.02439, and the primary albedo is 0.05135. The fusion albedo is closer to the measured value than the primary albedo. The GLASS albedo product of 500 m resolution in the photovoltaic industrial park was compared with the co-located albedo product of 16 m resolution to visually illustrate the effect of the albedo product of 16 m resolution. The albedo product of 16 m resolution could better support the studies on human activities and the environment. The algorithm quantitatively fuses the texture information of 16 m resolution in the GF-1 data with the mean value information of the GLASS albedo product of 500 m resolution to obtain the albedo product of 16 m resolution. It contains two main steps: a simple direct inversion algorithm and a downscale-to-fuse algorithm. The albedo product of 16 m resolution enriches the spatial texture information on the premise that the average value is consistent with that of the GLASS albedo product of 500 m resolution.

Schlüsselwort

remote sensing;albedo;algorithm;Downscaling fusion;high resolution;Verification;GF-1;Hei He

References

  1. 1.
    Dickinson R E. 1983. Land surface processes and climate-surface albedos and energy balance. Advances in Geophysics, 25: 305-353
  2. 2.
    Dirmeyer P A and Shukla J. 1994. Albedo as a modulator of climate response to tropical deforestation. Journal of Geophysical Research: Atmospheres, 99(D10): 20863-20877
  3. 3.
    Gao B, Jia L and Menenti M. 2014. An improved method for retrieving land surface albedo over rugged terrain. IEEE Geoscience and Remote Sensing Letters, 11(2): 554-558
  4. 4.
    He T, Liang S L, Wang D D, Cao Y F, Gao F, Yu Y Y and Feng M. 2018. Evaluating land surface albedo estimation from Landsat MSS, TM, ETM+, and OLI data based on the unified direct estimation approach. Remote Sensing of Environment, 204: 181-196
  5. 5.
    He T, Liang S L, Wang D D, Chen X N, Song D X and Jiang B. 2015. Land surface albedo estimation from Chinese HJ satellite data based on the direct estimation approach. Remote Sensing, 7(5): 5495-5510
  6. 6.
    He T, Liang S L, Wang D D, Shuai Y M and Yu Y Y. 2014. Fusion of satellite land surface albedo products across scales using a multiresolution tree method in the North Central United States. IEEE Transactions on Geoscience and Remote Sensing, 52(6): 3428-3439
  7. 7.
    Kotchenova S Y and Eric F V. 2007. Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part II. Homogeneous Lambertian and anisotropic surfaces. Applied Optics, 46(20): 4455-4464
  8. 8.
    Lewis P, Brockmann C, Danne O, Fischer J, Guanter L, Heckel A, Krueger O, López G, Muller J, North P and Preusker R. 2011. GlobAlbedo Algorithm Theoretical Basis Document: Version 3.0. [s.n.]
  9. 9.
    Li C J, Liu L Y, Wang J H and Wang R C. 2004. Comparison of two methods of fusing remote sensing images with fidelity of spectral information. Journal of Image and Graphics, 9(11): 1376-1385
  10. 10.
    Li X, Li X W, Li Z Y, Wang J, Ma M G, Liu Q, Xiao Q, Hu Z Y, Che T, Wang J M, Liu Q H, Chen E X, Yan G J, Liu S M, Wang W Z, Zhang L X, Wang J D, Niu Z, Jin R, Ran Y H and Wang L X. 2012a. Progresses on the watershed allied telemetry experimental research (WATER). Remote Sensing Technology and Application, 27(5): 637-649
  11. 11.
    Li X, Liu Q, Liu Q H, Wang J, Ma M G, Xiao Q, Che T, Jin R and Ran Y H. 2012b. The progresses on the watershed allied telemetry experimental research (WATER): remote sensing of key hydrological and ecological parameters. Remote Sensing Technology and Application, 27(5): 650-662
  12. 12.
    Li X，Liu S M，Liu Q H，Xiao Q，Che T，Ma M G，Jin R，Ran Y H，Wen J G，Xu Z W and Li Z Y. 2023. Heihe remote sensing experiments： Retrospect and prospect. National Remote Sensing Bulletin， 27（2）：224-248
  13. 13.
    Liang S L. 2003. A direct algorithm for estimating land surface broadband albedos from MODIS imagery. IEEE Transactions on Geoscience and Remote Sensing, 41(1): 136-145
  14. 14.
    Liang S L, Li X W and Wang J D. 2013. Quantitative Remote Sensing: Ideas and Algorithms. Beijing: Science Press
  15. 15.
    Liang S L, Strahler A H and Walthall C. 1999. Retrieval of land surface albedo from satellite observations: a simulation study. Journal of Applied Meteorology, 38(6): 712-725
  16. 16.
    Liang S L, Stroeve J and Box J E. 2005. Mapping daily snow/ice shortwave broadband albedo from Moderate Resolution Imaging Spectroradiometer (MODIS): the improved direct retrieval algorithm and validation with Greenland in situ measurement. Journal of Geophysical Research: Atmospheres, 110(D10): D10109
  17. 17.
    Liang S L, Wang K C, Zhang X T and Wild M. 2010. Review on estimation of land surface radiation and energy budgets from ground measurement, remote sensing and model simulations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(3): 225-240
  18. 18.
    Liang S L, Zhao X, Liu S H, Yuan W P, Cheng X, Xiao Z Q, Zhang X T, Liu Q, Cheng J, Tang H R, Qu Y H, Bo Y C, Qu Y, Ren H Z, Yu K and Townshend J. 2013. A long-term Global LAnd Surface Satellite (GLASS) data-set for environmental studies. International Journal of Digital Earth, 6(S1): 5-33
  19. 19.
    Liu J, Wang L M, Yang L B, Teng F, Shao J, Yang F G and Fu C H. 2015. GF-1 satellite image atmospheric correction based on 6S model and its effect. Transactions of the Chinese Society of Agricultural Engineering, 31(19): 159-168
  20. 20.
    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(S1): 69-95
  21. 21.
    Peng J J, Liu Q, Wen J G, Liu Q H, Tang Y, Wang L Z, Dou B C, You D Q, Sun C K, Zhao X J, Feng Y B and Shi J, Multi-scale validation strategy for satellite albedo products and its uncertainty analysis. Science China: Earth Sciences, 45(1): 66-82
  22. 22.
    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
  23. 23.
    Sellers P J, Tucker C J, Collatz G J, Los S O, Justice C O, Dazlich D A and Randall D A. 1994. A global 1° by 1° NDVI data set for climate studies. Part 2: the generation of global fields of terrestrial biophysical parameters from the NDVI. International Journal of Remote Sensing, 15(17): 3519-3545
  24. 24.
    Shang R, Liu R G and Liu Y. 2015. Generation of global long-term albedo product based on the background knowledge. Journal of Geo-Information Science, 17(11): 1313-1322
  25. 25.
    Shuai Y M, Masek J G, Gao F, Schaaf C B and He T. 2014. An approach for the long-term 30-m land surface snow-free albedo retrieval from historic Landsat surface reflectance and MODIS-based a priori anisotropy knowledge. Remote Sensing of Environment, 152: 467-479
  26. 26.
    Siegel R and Howell J R. 1992. Thermal Radiation Heat Transfer. 3rd ed. Bristol: Hemisphere Publishing
  27. 27.
    Sundar K J A, Jahnavi M, Lakshmisaritha K. 2017. Multi-sensor image fusion based on empirical wavelet transform//Proceedings of 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques. Mysuru: IEEE: 93-97
  28. 28.
    Wang D D, Liang S L, He T and Yu Y Y. 2013. Direct estimation of land surface albedo from VIIRS data: algorithm improvement and preliminary validation. Journal of Geophysical Research: Atmospheres, 118(22): 12577-12586
  29. 29.
    Wang L Z, Zheng X C, Sun L, Liu Q and Liu S H. 2014. Validation of GLASS albedo product through Landsat TM data and ground measurements. Journal of Remote Sensing, 18(3): 547-558
  30. 30.
    You D Q, Wen J G, Xiao Q, Liu Q, Liu Q H, Tang Y, Dou B C and Peng J J. 2015. Development of a high resolution BRDF/albedo product by fusing airborne CASI reflectance with MODIS daily reflectance in the Oasis Area of the Heihe River Basin, China. Remote Sensing, 7(6): 6784-6807
  31. 31.
    Zhang H, Jiao Z T, Dong Y D, Li J Y and Li X W. 2015. Albedo retrieved from BRDF archetype and surface directional reflectance. Journal of Remote Sensing, 19(3): 355-367

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