Früher|Nächster

Cross calibration of GF-4/PMS based on MODIS over Badain Jaran Desert

  • role： First author第一作者
  • Affiliation：

    Aerospace Information Research Institute(AIR) Chinese Academy of Sciences(CAS), Beijing 100094, China

  • Email：zhanghao612@radi.ac.cn
  • Introduction：张浩，研究方向为光学遥感辐射定标及大气校正。E-mail：zhanghao612@radi.ac.cn
ZHANG Hao1,  
  • Affiliation：

    Aerospace Information Research Institute(AIR) Chinese Academy of Sciences(CAS), Beijing 100094, China

    Zhongke Xingtu Space Technology Co., Ltd, Xi'An 710100, China

LIU Tao12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Institute of Mineral Resources Research, China Metallurgical Geology Bureau, Beijing 101300, China

  • Email：yandc@radi.ac.cn
  • Introduction：闫东川，研究方向为海量遥感数据智能化处理与分析。E-mail： yandc@radi.ac.cn
YAN Dongchuan3*,  
  • Affiliation：

    China University of mining and technology(Beijing), Beijing 100083, China

YAN Yueguan4,  
  • Affiliation：

    Aerospace Information Research Institute(AIR) Chinese Academy of Sciences(CAS), Beijing 100094, China

    School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China

CUI Zhenzhen15

Resümee

GF-4 is the first high-resolution geostationary orbit optical remote sensing satellite in China, and it is equipped with a staring camera that covers the visible-to-mid-infrared spectrum. Providing in-orbit radiometric calibration coefficients frequently is difficult due to the lack of on-board calibration devices. To effectively monitor the radiometric performance of GF-4/PMS and provide a solid basis for quantitative applications, the cross-calibration method was used in this study to derive the calibration coefficients over a selected calibration site located in the north of Badan Jaran Desert. The cross-calibration site was identified by searching the uniform area in a high-resolution image (i.e., Landsat 8 /OLI) to minimize the uniform impact on the cross-calibration results. To reduce the effects of the imaging angles of the sensor and atmospheric changes, the restraint was set to the imaging angle difference between MODIS and GF-4 (less than 20°), the imaging time difference (less than 2 h), the cloud, and image quality. In total, 13 image pairs were available from 2016 to 2018 and were used to calculate the cross-calibration coefficients after compensating for the spectral matching factors, which were simulated by MODTRAN. Results showed that (1) the cross-calibration coefficients calculated under the Lambertian assumption were highly consistent with those calculated under the Ross-Li BRDF assumption. The uncertainty was less than 7.4%, and the relative difference ranged between 0.8% and 4% when BRDF was neglected. (2) The radiometric performance of GF-4/PMS decreased slowly from 2016 to 2018, with 1% decrement per year. Thus, the proposed method can effectively improve the radiometric calibration frequency of GF-4/PMS with an acceptable accuracy and can be used for radiometric performance monitoring over the whole life cycle of the sensor.

Schlüsselwort

remote sensing;cross calibration;GF-4/PMS;MODIS;uncertainty analysis

References

  1. 1.
    Cao C Y, Xu H, Sullivan J, McMillin L, Ciren P and Hou Y T. 2005. Intersatellite radiance biases for the High-Resolution Infrared Radiation Sounders (HIRS) on board NOAA-15, -16, and -17 from simultaneous nadir observations. Journal of Atmospheric and Oceanic Technology, 22(4): 381-395
  2. 2.
    Chang T J, Xiong X X, Angal A, Wu A S and Geng X. 2017. Aqua and terra MODIS RSB calibration comparison using BRDF modeled reflectance. IEEE Transactions on Geoscience and Remote Sensing, 55(4): 2288-2298
  3. 3.
    Chen Y P, Sun K M, Li D R, Bai T and Huang C Q. 2017. Radiometric cross-calibration of GF-4 PMS sensor based on assimilation of landsat-8 OLI images. Remote Sensing, 9(8): 811
  4. 4.
    Chen Z C, Liu X, Li J S, Luo W F, Zhang L, Zhang H, Zhang X and Zhang B. 2008. The cross calibration of Beijing-1 microsatellite multispectral sensors. Journal of Astronautics, 29(2): 637-643
  5. 5.
    Chen Z C, Zhang B, Zhang H and Zhang W J. 2014. Vicarious calibration of Beijing-1 multispectral imagers. Remote Sensing, 6(2): 1432-1450
  6. 6.
    Gao H L, Gu X F, Yu T, Li X Y, Gong H and Li J G. 2010. The research overview on visible and near-infrared channels radiometric calibration of space-borne optical remote sensors. Remote Sensing Information, (4): 117-128
  7. 7.
    Gong H, Tian G L, Yu T, Zhang Y X, Gu X F and Gao H L. 2010. Irradiance-based calibration and validation of MODIS visible and near-infrared channels. Journal of Remote Sensing, 14(2): 207-218
  8. 8.
    Hu C M, Muller-Karger F E, Andrefouet S and Carder K L. 2001. Atmospheric correction and cross-calibration of LANDSAT-7/ETM+ imagery over aquatic environments: a multiplatform approach using SeaWiFS/MODIS. Remote Sensing of Environment, 78(1/2): 99-107
  9. 9.
    Lacherade S, Fougnie B, Henry P and Gamet P. 2013. Cross calibration over desert sites: description, methodology, and operational implementation. IEEE Transactions on Geoscience and Remote Sensing, 51(3): 1098-1113
  10. 10.
    Li X and Strahler A H. 1992. Geometric-optical bidirectional reflectance modeling of the discrete crown vegetation canopy: effect of crown shape and mutual shadowing. IEEE Transactions on Geoscience and Remote Sensing, 30(2): 276-292
  11. 11.
    Li X Y, Gu X F, Min X J, Yu T, Fu Q Y, Zhang Y and Li X W. 2005. Radiometric cross-calibration of the CBERS-02 CCD camera with the TERRA MODIS. Science in China Series E Engineering & Materials Science, 48(S2): 44-60
  12. 12.
    Lv W B. 2014. Research on the Method and System of Cross-Calibration for Domestic Moderate and High-Resolution Remote Sensing Data in Reflection Bands. Chengdu: Southwest Jiaotong University
  13. 13.
    Lyapustin A, Wang Y, Go S, Choi M, Korkin S, Huang D, Knyazikhin Y, Blank K and Marshak A. 2021. Atmospheric correction of DSCOVR EPIC: version 2 MAIAC algorithm. Frontiers in Remote Sensing, 2: 748362
  14. 14.
    Ma X H. 2011. Study on the Cross Calibration and Validation of CCD Cameras on HJ-1Satellites. Jiaozuo: Henan Polytechnic University, 2011
  15. 15.
    Nie J, Deng L, Hao X L, Liu M and He Y. 2018. Application of GF-4 satellite in drought remote sensing monitoring: a case study of Southeastern Inner Mongolia. Journal of Remote Sensing, 22(3): 400-407
  16. 16.
    Rao C R N, Zhang N and Sullivan J T. 2001. Inter-calibration of meteorological satellite sensors in the visible and near-infrared. Advances in Space Research, 28(1): 3-10
  17. 17.
    Schaaf C L B, Liu J, Gao F and Strahler A H. 2011. MODIS Albedo and Reflectance Anisotropy Products from Aqua and Terra. In Land Remote Sensing and Global Environmental Change: NASA's Earth Observing System and the Science of ASTER and MODIS, Remote Sensing and Digital Image Processing Series, Vol. 11, B. Ramachandran, C. Justice, M. Abrams, Eds, Springer-Cerlag, 873 pp.
  18. 18.
    Scott K P, Thome K J and Brownlee M R. 1996. Evaluation of Railroad Valley playa for use in vicarious calibration//Proceedings of SPIE 2818, Multispectral Imaging for Terrestrial Applications. Denver: SPIE
  19. 19.
    Teillet P M, Fedosejevs G, Thome K J and Barker J L. 2007. Impacts of spectral band difference effects on radiometric cross-calibration between satellite sensors in the solar-reflective spectral domain. Remote Sensing of Environment, 110(3): 393-409
  20. 20.
    Teillet P M, Slater P N, Ding Y, Santer R P, Jackson R D and Moran M S. 1990. Three methods for the absolute calibration of the NOAA AVHRR sensors in-flight. Remote Sensing of Environment, 31(2): 105-120
  21. 21.
    Vermote E F and Saleous N Z. 2006. Calibration of NOAA16 AVHRR over a desert site using MODIS data. Remote Sensing of Environment, 105(3): 214-220
  22. 22.
    Wu W. 2019. Application of GF-4 satellite in outburst flood disaster monitoring and assessment. Spacecraft Engineering, 28(2): 134-140
  23. 23.
    Yang A X, Zhong B, Lv W B, Wu S L and Liu Q H. 2015. Cross-calibration of GF-1/WFV over a desert site using Landsat-8/OLI imagery and ZY-3/TLC data. Remote Sensing, 7(8): 10763-10787
  24. 24.
    Yang A X, Zhong B, Wu S L and Liu Q H. 2017. Radiometric cross-calibration of GF-4 in multispectral bands. Remote Sensing, 9(3): 232
  25. 25.
    Yang Z D, Gu S Y, Qiu H, Huang Q and Fan T X. 2004. CBERS-1's CCD image quality evaluating and cross calibrating study. Journal of Remote Sensing, 8(2): 113-120
  26. 26.
    Zhang H, Zhang B, Chen Z C, Huang Z H. 2018. Vicarious radiometric calibration of the hyperspectral imaging microsatellites SPARK-01 and -02 over Dunhuang, China. Remote Sensing, 10(1): 120
  27. 27.
    Zhang L. 2018. Application of GF-4 satellite in changes monitoring for water body extent of Hongze Lake in flood season. Spacecraft Engineering, 27(2): 129-134
  28. 28.
    Zhang Y H, Mao H Q, Wang Z T, Guo W, Li Q, Li Z Q, Chen X F and Chen H. 2016. Cross calibration between GOCI and MODIS based on spectrum and geometry matching. Journal of Atmospheric and Environmental Optics, 11(6): 412-422
  29. 29.
    Zhao W N, Hu X Q, Fang W, Wang Y P and Xu N. 2015. Development and applications of intercalibration for satellite optical instruments. Optics and Precision Engineering, 23(7): 1921-1931
  30. 30.
    Zhong B, Zhang Y H, Du T T, Yang A X, Lv W B and Liu Q H. 2014. Cross-calibration of HJ-1/CCD over a desert site using Landsat ETM + imagery and ASTER GDEM product. IEEE Transactions on Geoscience and Remote Sensing, 52(11): 7247-7263

Lesen Sie die ganze Passage

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website