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A method for estimating the land surface albedo of OCO-2 oxygen A-band based on MODIS/MCD43C3

  • role： First author第一作者
  • Affiliation：

    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

  • Email：jie.yang@whu.edu.cn
  • Introduction：杨洁，研究方向为大气遥感。E-mail：jie.yang@whu.edu.cn
YANG Jie12,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

  • Email：siwei.li@whu.edu.cn
  • Introduction：李四维，研究方向为大气遥感、大气污染及相关领域研究。E-mail：siwei.li@whu.edu.cn
LI Siwei12*,  
  • Affiliation：

    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

WANG Qingxin1

Resümee

Land surface reflection depends on land surface albedo and interferes with the retrieval of cloud geometrical thickness from OCO-2 oxygen A-band observations due to its second-strongest reflection after the cloud. However, no product can provide the land surface albedo of the OCO-2 oxygen A-band required for the retrieval. Therefore, the accurate estimation of land surface albedo is necessary and beneficial to the retrieval quality.In this study, we proposed a method for estimating land surface albedo in the oxygen A-band from multichannel black/white albedos from MODIS/MCD43C3 products. Although the estimation (MODIS→OCO-2) is related to land cover type, the comparison based on Shannon entropy proved that the multichannel albedo data contains the type information and is sufficient to achieve the same accuracy as land cover-type estimation. In addition, we implement the estimation model by BP neural network, and the accuracy is consistent with that of the analysis based on the Shannon entropy.We verified the multichannel-based estimation model by tests in different times and spaces. Its correlation coefficients were all over 0.93, and the root-mean-squared errors were 0.026. In addition, the multichannel-based model was always superior to the single-channel linear model on all land cover types, whether applied to the best-performing type of barren or sparsely vegetated land or the worse-performing type of snow and ice. The quality of MODIS albedo data is the most important for the accuracy of estimation. The root-mean-squared error with the best inputs was slightly better than 0.02 and increased to more than 0.05 as the quality of the inputs decreased.The method of estimating the land surface albedo in the OCO-2 oxygen A-band from MODIS multichannel black/white albedo data is feasible and can resist the disturbance caused by unknown land cover type. The estimation accuracy depends on the quality of the input MODIS albedo data.

Schlüsselwort

remote sensing;land surface albedo;land cover type;OCO-2;oxygen A-band;cloud retrieval

References

  1. 1.
    Bacour C, Bréon F M, Gonzalez L, Price I, Muller J P and Straume A G. 2020. Simulating multi-directional narrowband reflectance of the earth’s surface using ADAM (A surface reflectance database for ESA’s earth observation missions). Remote Sensing, 12(10): 1679
  2. 2.
    Claverie M, Ju J C, Masek J G, Dungan J L, Vermote E F, Roger J C, Skakun S V and Justice C. 2018. The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sensing of Environment, 219: 145-161
  3. 3.
    Dubovik O, Herman M, Holdak A, Lapyonok T, Tanré D, Deuzé J L, Ducos F, Sinyuk A and Lopatin A. 2011. Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations. Atmospheric Measurement Techniques, 4(5): 975-1018
  4. 4.
    Friedl M A, Sulla-Menashe D, Tan B, Schneider A, Ramankutty N, Sibley A and Huang X M. 2010. MODIS collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114(1): 168-182
  5. 5.
    Ge B Y, Li Z Q, Liu L, Yang L K, Chen X F, Hou W Z, Zhang Y, Li D H, Li L and Qie L L. 2019. A dark target method for Himawari-8/AHI aerosol retrieval: application and validation. IEEE Transactions on Geoscience and Remote Sensing, 57(1): 381-394
  6. 6.
    Hansen M C, Defries R S, Townshend J R G and Sohlberg R. 2000. Global land cover classification at 1km spatial resolution using a classification tree approach. International Journal of Remote Sensing, 21(6/7): 1331-1364
  7. 7.
    Heckel A, Kim S W, Frost G J, Richter A, Trainer M and Burrows J P. 2011. Influence of low spatial resolution a priori data on tropospheric NO2 satellite retrievals. Atmospheric Measurement Techniques, 4(9): 1805-1820
  8. 8.
    Herman J R and Celarier E A. 1997. Earth surface reflectivity climatology at 340-380 nm from TOMS data. Journal of Geophysical Research: Atmospheres, 102(D23): 28003-28011
  9. 9.
    Justice C O, Townshend J R G, Vermote E F, Masuoka E, Wolfe R E, Saleous N, Roy D P and Morisette J T. 2002. An overview of MODIS Land data processing and product status. Remote Sensing of Environment, 83(1/2): 3-15
  10. 10.
    Kaufman Y J, Tanré D, Remer L A, Vermote E F, Chu A and Holben B N. 1997. Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer. Journal of Geophysical Research: Atmospheres, 102(D14): 17051-17067
  11. 11.
    Kleipool Q L, Dobber M R, de Haan J F and Levelt P F. 2008. Earth surface reflectance climatology from 3 years of OMI data. Journal of Geophysical Research: Atmospheres, 113(D18): D18308
  12. 12.
    Koelemeijer R B A, de Haan J F and Stammes P. 2003. A database of spectral surface reflectivity in the range 335-772 nm derived from 5.5 years of GOME observations. Journal of Geophysical Research: Atmospheres, 108(D2): 4070
  13. 13.
    Liang S L, Bai R, Chen X N, Cheng J, Fan W J, He T, Jia K, Jiang B, Jiang L M, Jiao Z T, Liu Y B, Ni W J, Qiu F, Song L L, Sun L, Tang B H, Wen J G, Wu G P, Xie D H, Yao Y J, Yuan W P, Zhang Y G, Zhang Y Z, Zhang Y T, Zhang X T, Zhao T J and Zhao X. 2020. Review of China’s land surface quantitative remote sensing development in 2019. Journal of Remote Sensing, 24(6): 618-671
  14. 14.
    Liang S L, Cheng J, Jia K, Jiang B, Liu Q, Liu S H, Xiao Z Q, Xie X H and Yao Y J. 2016. Recent progress in land surface quantitative remote sensing. Journal of Remote Sensing, 20(5): 875-898
  15. 15.
    Lorente A, Boersma K F, Stammes P, Tilstra L G, Richter A, Yu H, Kharbouche S and Muller J P. 2018. The importance of surface reflectance anisotropy for cloud and NO2 retrievals from GOME-2 and OMI. Atmospheric Measurement Techniques, 11(7): 4509-4529
  16. 16.
    Lotsch A, Tian Y, Friedl M A and Myneni R B. 2003. Land cover mapping in support of LAI and FPAR retrievals from EOS-MODIS and MISR: classification methods and sensitivities to errors. International Journal of Remote Sensing, 24(10): 1997-2016
  17. 17.
    Loveland T R and Belward A S. 1997. The IGBP-DIS global 1 km land cover data set, DISCover: first results. International Journal of Remote Sensing, 18(15): 3289-3295
  18. 18.
    Lyapustin A, Wang Y J, Korkin S and Huang D. 2018. MODIS collection 6 MAIAC algorithm. Atmospheric Measurement Techniques, 11(10): 5741-5765
  19. 19.
    Nanda S, de Graaf M, Veefkind J P, Sneep M, ter Linden M, Sun J Y T and Levelt P F. 2020. A first comparison of TROPOMI aerosol layer height (ALH) to CALIOP data. Atmospheric Measurement Techniques, 13(6): 3043-3059
  20. 20.
    O’Dell C W, Eldering A, Wennberg P O, Crisp D, Gunson M R, Fisher B, Frankenberg C, Kiel M, Lindqvist H, Mandrake L, Merrelli A, Natraj V, Nelson R R, Osterman G B, Payne V H, Taylor T E, Wunch D, Drouin B J, Oyafuso F, Chang A, McDuffie J, Smyth M, Baker D F, Basu S, Chevallier F, Crowell S M R, Feng L, Palmer P I, Dubey M, García O E, Griffith D W T, Hase F, Iraci L T, Kivi R, Morino I, Notholt J, Ohyama H, Petri C, Roehl C M, Sha M K, Strong K, Sussmann R, Te Y, Uchino O and Velazco V A. 2018. Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm. Atmospheric Measurement Techniques, 11(12): 6539-6576
  21. 21.
    Rahman H, Pinty B and Verstraete M M. 1993. Coupled surface-atmosphere reflectance (CSAR) model: 2. Semiempirical surface model usable with NOAA advanced very high resolution radiometer data. Journal of Geophysical Research: Atmospheres, 98(D11): 20791-20801
  22. 22.
    Russell A R, Perring A E, Valin L C, Bucsela E J, Browne E C, Wooldridge P J and Cohen R C. 2011. A high spatial resolution retrieval of NO2 column densities from OMI: method and evaluation. Atmospheric Chemistry and Physics, 11(16): 8543-8554
  23. 23.
    Shannon C E. 1997. The mathematical theory of communication. 1963. MD Computing, 14(4): 306-317
  24. 24.
    Shi S Y, Cheng T H, Gu X F, Chen H, Guo H, Wang Y, Bao F W, Xu B R, Wang W N, Zuo X, Meng C and Zhang X C. 2017. Synergy of MODIS and AATSR for better retrieval of aerosol optical depth and land surface directional reflectance. Remote Sensing of Environment, 195: 130-141
  25. 25.
    Strahler A H, Muller J P, Lucht W, Schaaf C B, Tsang T, Gao F, Li X W, Lewis P and Barnsley M J. 2021. MODIS BRDF/albedo product: algorithm theoretical basis document version 5.0[EB/OL].
  26. 26.
    Tilstra L G, Tuinder O N E, Wang P and Stammes P. 2017. Surface reflectivity climatologies from UV to NIR determined from Earth observations by GOME-2 and SCIAMACHY. Journal of Geophysical Research: Atmospheres, 122(7): 4084-4111
  27. 27.
    Veefkind J P, de Haan J F, Brinksma E J, Kroon M and Levelt P F. 2006. Total ozone from the Ozone Monitoring Instrument (OMI) using the DOAS technique. IEEE Transactions on Geoscience and Remote Sensing, 44(5): 1239-1244
  28. 28.
    Wang P, Stammes P, van der A R, Pinardi G and van Roozendael M. 2008. FRESCO+: an improved O2 A-band cloud retrieval algorithm for tropospheric trace gas retrievals. Atmospheric Chemistry and Physics, 8(21): 6565-6576
  29. 29.
    Wang Q X, Li S W, Zeng Q L, Sun L, Yang J and Lin H. 2020. Retrieval and validation of AOD from Himawari-8 data over Bohai Rim Region, China. Remote Sensing, 12(20): 3425
  30. 30.
    Wang Z S, Schaaf C B, Sun Q S, Shuai Y M and Román M O. 2018. Capturing rapid land surface dynamics with Collection V006 MODIS BRDF/NBAR/Albedo(MCD43) products. Remote Sensing of Environment, 207: 50-64
  31. 31.
    Worden J R, Doran G, Kulawik S, Eldering A, Crisp D, Frankenberg C, O’Dell C and Bowman K. 2017. Evaluation and attribution of OCO-2 XCO2 uncertainties. Atmospheric Measurement Techniques, 10(7): 2759-2771
  32. 32.
    Yang J, Li S W, Gong W, Min Q L, Mao F Y and Pan Z X. 2021. A fast cloud geometrical thickness retrieval algorithm for single-layer marine liquid clouds using OCO-2 oxygen A-band measurements. Remote Sensing of Environment, 256: 112305
  33. 33.
    Zeng Z C, Chen S H, Natraj V, Le T H, Xu F, Merrelli A, Crisp D, Sander S P and Yung Y L. 2020. Constraining the vertical distribution of coastal dust aerosol using OCO-2 O2 A-band measurements. Remote Sensing of Environment, 236: 111494
  34. 34.
    Zhang H K, Roy D P, Yan L, Li Z B, Huang H Y, Vermote E, Skakun S and Roger J C. 2018. Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences. Remote Sensing of Environment, 215: 482-494

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