سابِق|تال

Remote sensing retrieval of high-spatial-resolution land surface albedo

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：zhaocongcong20@mails.ucas.ac.cn
  • Introduction：赵聪聪，研究方向为定量遥感。E-mail： zhaocongcong20@mails.ucas.ac.cn
ZHAO Congcong12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：wenjg@irsa.ac.cn
  • Introduction：闻建光，研究方向为遥感辐射传输建模与试验遥感。E-mail： wenjg@irsa.ac.cn
WEN Jianguang12*,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

YOU Dongqin1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

TANG Yong1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

CHEN Xi1,  
  • Affiliation：

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

LIU Qiang3

ملخص

Land surface albedo is an important variable for controlling Earth radiation budget. It is also recognized as an Essential Climate Variable by the Global Climate Observing System. The progress of the high-spatial-resolution satellite development allows high-spatial-resolution albedo to provide important data for the research of local radiation and energy balance, regional climate, and ecological environment. However, albedo is a variable related to solar angle, wavelength, and atmospheric status. Thus, the estimation of high-spatial-resolution land surface albedo becomes challenging. Different methods of land surface albedo remote sensing estimation have been developed in the last two decades. These methods greatly improve the high-resolution albedo mapping ability. Summarizing and analyzing the proposed methods of high-spatial-resolution albedo are important to improve the accuracy of its product estimation further.Two fundamental problems of insufficient multiangle observation and multisensor band information for high-resolution albedo estimation are proposed by analyzing the basic principle of the existing albedo estimation. Four main methods of high-spatial-resolution albedo are summarized in the characteristics of the algorithm and application cases according to how the problem of insufficient understanding of land surface reflectance anisotropy is overcome. Lastly, the conclusion and prospect of the high-spatial-resolution albedo method development are also summarized.According to whether the land surface anisotropic reflection characteristics are considered, the current high-resolution albedo retrieval methods are divided into two basic types: the method of narrowband to broadband conversions and the method of Bidirectional Reflectance Distribution Functionconsideration. The latter type considers the surface anisotropic reflection characteristics in different ways. It also includes estimation based on high-resolution multiangle reflectance data, estimation based on combining high-resolution reflectance data with low-resolution reflectance data, and estimation based on empirical knowledge of BRDF/albedo.These proposed methods of obtaining high-spatial-resolution land surface albedo with the surface bidirectional reflection characteristic information directly or indirectly are still the mainstream idea. They alleviate the problem of missing high-resolution valid data to a certain extent. However, they are still limited because of the lack of effective data, such as the angle and band of the high-resolution remote sensing data and the lack of high-resolution BRDF a priori knowledge information. The development of a high-resolution albedo algorithm is a prospect for future research. It can potentially provide theoretical support for high-resolution land surface albedo product development.

مفهوم

land surface albedo;Bidirectional Reflectance Distribution Function (BRDF);remote sensing retrieval;narrowband to broadband;high spatial resolution satellite

References

  1. 1.
    Bacour C and Bréon F M. 2005. Variability of biome reflectance directional signatures as seen by POLDER. Remote Sensing of Environment, 98(1): 80-95
  2. 2.
    Bonafoni S and Sekertekin A. 2020. Albedo retrieval from sentinel-2 by new narrow-to-broadband conversion coefficients. IEEE Geoscience and Remote Sensing Letters, 17(9): 1618-1622
  3. 3.
    Brest C L and Goward S N. 1987. Deriving surface albedo measurements from narrow band satellite data. International Journal of Remote Sensing, 8(3): 351-367
  4. 4.
    Chen F, Li Y J, Ma Q M, Li X, Chen J, Li M, Gao C Z and Yang X Y. 2020. High-resolution BRDF and albedo parameters inversion from sentinel-2 multispectral instrument data//IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium. Waikoloa: IEEE
  5. 5.
    Dickinson R E. 1983. Land surface processes and climate-surface albedos and energy balance. Advances in Geophysics, 25: 305-353
  6. 6.
    Duguay C R and Ledrew E F. 1993. Estimating surface reflectance and albedo from landsat-5 thematic mapper over rugged terrain (VOL, 58, PG, 552, 1992). Photogrammetric Engineering and Remote Sensing, 59(4): 498-498
  7. 7.
    Franch B, Vermote E F and Claverie M. 2014b. Intercomparison of Landsat albedo retrieval techniques and evaluation against in situ measurements across the US SURFRAD network. Remote Sensing of Environment, 152: 627-637
  8. 8.
    Franch B, Vermote E F, Sobrino J A and Julien Y. 2014a. Retrieval of surface albedo on a daily basis: application to MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 52(12): 7549-7558
  9. 9.
    Gao B, Gong H L and Wang T X. 2015. A method for retrieving daily land surface albedo from space at 30-m resolution. Remote Sensing, 7(8): 10951-10972
  10. 10.
    Gao B, Jia L and Wang T X. 2014. Derivation of land surface albedo at high resolution by combining HJ-1A/B reflectance observations with MODIS BRDF products. Remote Sensing, 6(9): 8966-8985
  11. 11.
    Greuell W and Oerlemans J. 2004. Narrowband-to-broadband albedo conversion for glacier ice and snow: equations based on modeling and ranges of validity of the equations. Remote Sensing of Environment, 89(1): 95-105
  12. 12.
    Hautecœur O and Leroy M M. 1998. Surface bidirectional reflectance distribution function observed at global scale by POLDER/ADEOS. Geophysical Research Letters, 25(22): 4197-4200
  13. 13.
    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
  14. 14.
    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
  15. 15.
    He T, Liang S L, Wang D D, Shi Q Q and Tao X. 2014a. Estimation of high-resolution land surface shortwave albedo from AVIRIS data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(12): 4919-4928
  16. 16.
    He T, Liang S L, Wang D D, Shuai Y M and Yu Y Y. 2014b. 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
  17. 17.
    Hu Y H, Jia G S, Pohl C, Zhang X X and Van Genderen J. 2016. Assessing surface albedo change and its induced radiation budget under rapid urbanization with Landsat and GLASS data. Theoretical and Applied Climatology, 123(3/4): 711-722
  18. 18.
    Jiao Z T, Hill M J, Schaaf C B, Zhang H, Wang Z S and Li X W. 2014. An anisotropic flat index (AFX) to derive BRDF archetypes from MODIS. Remote Sensing of Environment, 141: 168-187
  19. 19.
    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
  20. 20.
    Kimes D S and Sellers P J. 1985. Inferring hemispherical reflectance of the earth’s surface for global energy budgets from remotely sensed nadir or directional radiance values. Remote Sensing of Environment, 18(3): 205-223
  21. 21.
    Knap W H, Brock B W, Oerlemans J and Willis I C. 1999a. Comparison of landsat TM-derived and ground-based albedos of Haut Glacier d’Arolla, Switzerland. International Journal of Remote Sensing, 20(17): 3293-3310
  22. 22.
    Knap W H, Reijmer C H and Oerlemans J. 1999b. Narrowband to broadband conversion of Landsat TM glacier albedos. International Journal of Remote Sensing, 20(10): 2091-2110
  23. 23.
    Leroy M, Deuzé J L, Bréon F M, Hautecoeur O, Herman M, Buriez J C, Tanré D, Bouffiès S, Chazette P and Roujean J L. 1997. Retrieval of atmospheric properties and surface bidirectional reflectances over land from POLDER/ADEOS. Journal of Geophysical Research: Atmospheres, 102(D14): 17023-17037
  24. 24.
    Lewis P, Guanter L, Saldana G L, Muller J P, Watson G, Shane N, Kennedy T, Fisher J, Domenech C, Preusker R, North P, Heckel A, Danne O, Krämer U, Zühlke M, Fomferra N, Brockmann C and Schaaf C. 2012. The ESA Globalbedo project: algorithm//2012 IEEE International Geoscience and Remote Sensing Symposium. Munich, Germany: IEEE
  25. 25.
    Li Z, Erb A, Sun Q S, Liu Y, Shuai Y M, Wang Z S, Boucher P and Schaaf C. 2018. Preliminary assessment of 20-m surface albedo retrievals from sentinel-2A surface reflectance and MODIS/VIIRS surface anisotropy measures. Remote Sensing of Environment, 217: 352-365
  26. 26.
    Liang S L. 2001. Narrowband to broadband conversions of land surface albedo I: algorithms. Remote Sensing of Environment, 76(2): 213-238
  27. 27.
    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. 2003. Narrowband to broadband conversions of land surface albedo: II. Validation. Remote Sensing of Environment, 84(1): 25-41
  28. 28.
    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 and Climatology, 38(6): 712-725
  29. 29.
    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
  30. 30.
    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
  31. 31.
    Liu Q H, Wen J G, Zhou X, Zhao J, Li Z Y, Li X, Ma M G, Wang W Z, Liao X H, Liu S M, Fan W J, Xiao Q, Zhong B, Li J, Xin X Z, Li L, Jia L, Gao Z H, Jin J D, Liang S, Xin J, Liao C J and Wu Y R. 2023. Technique system of remote sensing product generation and validation of GF common products. National Remote Sensing Bulletin， 27(3): 544-562
  32. 32.
    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
  33. 33.
    Martonchik J V, Diner D J, Pinty B, Verstraete M M, Myneni R B, Knyazikhin Y and Gordon H R. 1998. Determination of land and ocean reflective, radiative, and biophysical properties using multiangle imaging. IEEE Transactions on Geoscience and Remote Sensing, 36(4): 1266-1281
  34. 34.
    Nicodemus F E, Richmond J C, Hsia J J, Ginsberg I W and Limperis T. 1977. Geometrical Considerations and Nomenclature for Reflectance. Washington, DC: National Burean of Standards
  35. 35.
    Peng J J, Fan W J, Xu X R, Wang L Z, Liu Q H, Li J C and Zhao P. 2015. Estimating crop albedo in the application of a physical model based on the law of energy conservation and spectral invariants. Remote Sensing, 7(11): 15536-15560
  36. 36.
    Peng S, Wen J G, Xiao Q, You D Q, Dou B C, Liu Q and Tang Y. 2017. Multi-staged NDVI dependent snow-free land-surface shortwave albedo narrowband-to-broadband (NTB) coefficients and their sensitivity analysis. Remote Sensing, 9(1): 93
  37. 37.
    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
  38. 38.
    Roman M O, Gatebe C K, Shuai Y M, Wang Z S, Gao F, Masek J G, He T, Liang S L and Schaaf C B. 2013. Use of in situ and airborne multiangle data to assess MODIS-and Landsat-based estimates of directional reflectance and albedo. IEEE Transactions on Geoscience and Remote Sensing, 51(3): 1393-1404
  39. 39.
    Roujean J L, Tanré D, Bréon F M and Deuzé J L. 1997. Retrieval of land surface parameters from airborne POLDER bidirectional reflectance distribution function during HAPEX-Sahel. Journal of Geophysical Research: Atmospheres, 102(D10): 11201-11218
  40. 40.
    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
  41. 41.
    Shuai Y M, Masek J G, Gao F and Schaaf C B. 2011. An algorithm for the retrieval of 30-m snow-free albedo from Landsat surface reflectance and MODIS BRDF. Remote Sensing of Environment, 115(9): 2204-2216
  42. 42.
    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
  43. 43.
    Tasumi M, Allen R G and Trezza R. 2008. At-surface reflectance and albedo from satellite for operational calculation of land surface energy balance. Journal of Hydrologic Engineering, 13(2): 51-63 [DOI: )]
  44. 44.
    Valiente J A, Nunez M, Lopez-Baeza E and Moreno J F. 1995. Narrow-band to broad-band conversion for meteosat-visiible channel and broad-band albedo using both AVHRR-1 and AVHRR-2 channels. International Journal of Remote Sensing, 16(6): 1147-1166
  45. 45.
    Vermote E, Justice C O and Breon F M. 2009. Towards a generalized approach for correction of the BRDF effect in MODIS directional reflectances. IEEE Transactions on Geoscience and Remote Sensing, 47(3): 898-908
  46. 46.
    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
  47. 47.
    Wang J, Cui Y H, He X B, Zhang J and Yan S J. 2015. Surface albedo variation and its influencing factors over dongkemadi glacier, Central Tibetan Plateau. Advances in Meteorology, 2015: 852098
  48. 48.
    Wang Z S, Erb A M, Schaaf C B, Sun Q S, Liu Y, Yang Y, Shuai Y M, Casey K A and Román M O. 2016. Early spring post-fire snow albedo dynamics in high latitude boreal forests using Landsat-8 OLI data. Remote Sensing of Environment, 185: 71-83
  49. 49.
    Wen J G, Dou B C, You D Q, Tang Y, Xiao Q, Liu Q and Qinhuo L. 2017. Forward a small-timescale BRDF/albedo by multisensor combined BRDF inversion model. IEEE Transactions on Geoscience and Remote Sensing, 55(2): 683-697
  50. 50.
    Wen J G, Liu Q, Liu Q H, Xiao Q and Li X W. 2015. Remote Sensing Modeling of Land Surface Bidirectional Reflection and the Retrieval of Albedo. Beijing: Science Publishing Press, 2015: 5
  51. 51.
    Wen J G, Liu Q, Xiao Q, Liu Q H, You D Q, Hao D L, Wu S B and Lin X W. 2018. Characterizing land surface anisotropic reflectance over rugged terrain: a review of concepts and recent developments. Remote Sensing, 10(3): 370
  52. 52.
    Yan G J, Jiang H L, Yan K, Cheng S Y, Song W J, Tong Y Y, Liu Y N, Qi J B, Mu X H, Zhang W M, Xie D H and Zhou H M. 2021. Review of optical multi-angle quantitative remote sensing. Journal of Remote Sensing, 25(1): 83-108
  53. 53.
    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
  54. 54.
    Zhang G, Huang F and Zheng M L. 2017a. Generating time series of medium-resolution albedo images by Kalman filtering algorithm//2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics. Shanghai: IEEE
  55. 55.
    Zhang G D, Zhou H M, Wang C J, Xue H Z, Wang J D and Wan H W. 2019. Time series high-resolution land surface albedo estimation based on the ensemble Kalman filter algorithm. Remote Sensing, 11(7): 753
  56. 56.
    Zhang H, Jiao Z T, Chen L, Dong Y D, Zhang X N, Lian Y, Qian D and Cui T J. 2018. Quantifying the reflectance anisotropy effect on albedo retrieval from remotely sensed observations using archetypal BRDFs. Remote Sensing, 10(10): 1628
  57. 57.
    Zhang H, Jiao Z T, Dong Y D and Li X W. 2015. Evaluation of BRDF archetypes for representing surface reflectance anisotropy using MODIS BRDF data. Remote Sensing, 7(6): 7826-7845
  58. 58.
    Zhang H, Liu P F, He L, Lian Y and Cui T J. 2017b. Effects of reflectance anisotropy on albedo retrieval from satellite observations//2017 IEEE International Geoscience and Remote Sensing Symposium. Fort Worth: IEEE
  59. 59.
    Zhang K, Zhou H M, Wang J D and Xue H Z. 2014. Estimation and validation of high spatio-temporal resolution albedo by fusing Landsat ETM+and MODIS data. Journal of Remote Sensing, 18(3): 497-517
  60. 60.
    Zhang X N, Jiao Z T, Dong Y D, He T, Ding A X, Yin S Y, Zhang H, Cui L, Chang Y X, Guo J and Xie R. 2020. Development of the direct-estimation albedo algorithm for snow-free Landsat TM albedo retrievals using field flux measurements. IEEE Transactions on Geoscience and Remote Sensing, 58(3): 1550-1567
  61. 61.
    Zhou H M, Hu N, He T, Liang S L and Wang J D. 2018. High resolution albedo estimation with Chinese GF-1 WFV data//IGARSS 2018-2018 38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Valencia, Spain: IEEE
  62. 62.
    Zhou Y, Wang D D, Yu Y Y, Liang S L and IEEE. 2017. VIIRS land surface albedo product: algorithm development and validation//2017 IEEE International Geoscience and Remote Sensing Symposium. Fort Worth: IEEE

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