A comprehensive calibration site for high resolution remote sensors dedicated to quantitative remote sensing and its applications

  • role: First author第一作者
  • Affiliation:

    Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Email:crli@aoe.ac.cn
  • Introduction:李传荣,1956年生,男,研究员,研究方向为遥感技术系统与应用。E-mail: crli@aoe.ac.cn
LI Chuanrong,  
  • role: Corresponding author通信作者
  • Affiliation:

    Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Email:llma@aoe.ac.cn
  • Introduction:马灵玲,1981年生,女,研究员,研究方向为光学遥感定标与产品真实性检验。E-mail: llma@aoe.ac.cn
MA Lingling*,  
  • Affiliation:

    Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

TANG Lingli,  
  • Affiliation:

    Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

GAO Caixia,  
  • Affiliation:

    Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

QIAN Yonggang,  
  • Affiliation:

    Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

WANG Ning,  
  • Affiliation:

    Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

WANG Xinhong

реферат

The increasingly developing information age puts forward urgent need for stable and precise quantitative remote sensing information. The accuracy of ground object information retrieved by remote sensing technology is essentially decided by the sensor performance and timely evaluation of its variation occurred during long-term operational running. Since the “11th Five-Year Plan”, the Key Laboratory of Quantitative Remote Sensing Information Technology, Chinese Academy of Sciences, has organized relevant domestic advantageous institutions to design and realize the comprehensive calibration technical system for high resolution remote sensors which is dedicated to quantitative remote sensing, and built up the “Baotou Comprehensive Calibration Site for High Resolution Remote Sensors” (the Baotou site, for short). From the top-level viewing, the Baotou site consists of five main systems: (1) the standard test targets system, which contains a variety of permanent artificial optical targets, portable artificial optical targets, optical geometric control point targets, permanent bases for SAR corner reflector, SAR corner reflectors and natural scene targets. Within these miscellaneous targets, the knife-edge/greyscale dual functional target is a natural-material-paved permanent target for simultaneous evaluation of sensor radiometric/spatial/spectral characteristics, which is the first one in China and the largest one in the world; the microwave/optical dual functional artificial target is dedicated to directly detecting SAR/optical image resolution, which is the first one in the world. (2) the ground/atmospheric truth measurement system, which contains the automatic observation system for target characteristics, the automatic measurement system for atmospheric environment parameters, the surface flux automatic observation system, other ground feature measurement devices and the real-time monitoring system of automatic observation data. (3) the aerial flight test technical system, which contains the flight test standard sensors, the flight control & management system, the sensor ground calibration & testing system and the aerial flight data in-situ fast processing system. (4) the observation data processing and analysis system, which contains the RadCalNet automated radiometric calibration data processing system, the sensor performance analysis & evaluation system and the target characteristics knowledge base. (5) the basic guarantee facilities, which contains the test flight guarantee facilities, the ground test guarantee facilities and the logistical guarantee facilities. The rich types of optical/microwave targets and diverse environment measurement devices assure the polyfunctionality and flexibility of the site shown in various RS calibration/validation tasks. In general, the Baotou site can afford field comprehensive calibration for airborne/spaceborne high resolution remote sensors, which involves rigorous test flight for sensor performance, sensor on-orbit calibration and performance evaluation, and remote sensing product validation. Its powerful capability in comprehensive calibration and test has been widely acknowledged in the international earth observation community, and it was entitled the “National Calibration and Validation Site for High Resolution Remote Sensors” by the Ministry of Science and Technology of China. This paper will firstly point out the calibration requirements conforming to the trend of high resolution remote sensing, then describe in detail the system structure of the Baotou site and its miscellaneous functions, and finally show some successful applications based on the Baotou site in high resolution sensor calibration and performance evaluation, which can provide references for researchers in remote sensing field.

ключеви́че слова́

calibration site;high resolution;remote sensor;the Baotou site;test target

References

  1. 1.
    Berthelot B and Santer R. 2008. Calibration Test Sites Selection and Characterisation[OL]. [2008-03-18].
  2. 2.
    Blonski S and Cao C. 2015. Suomi NPP VIIRS Reflective Solar Bands Operational Calibration Reprocessing. Remote Sensing, 7(12): 16131-16149
  3. 3.
    Bouvet M, Thome K, Berthelot B, Bialek A, Czapla-Myers J, Fox N P, Goryl P, Henry P, Ma L L, Marcq S, Meygret A, Wenny B N and Woolliams E R. 2019. RadCalNet: A Radiometric Calibration Network for Earth Observing Imagers Operating in the Visible to Shortwave Infrared Spectral Range. Remote Sensing, 11(20): 2401
  4. 4.
    Cramer M. 2005. 10 years ifp test site Vaihingen/Enz: An independent performance study. Photogrammetric Week '05, pp. 79-92
  5. 5.
    Datla R U, Rice J P, Lykke K R, Johnson B C, Butler J J and Xiong X. 2011. Best Practice Guidelines for Pre-Launch Characterization and Calibration of Instruments for Passive Optical Remote Sensing. Journal of Research of the National Institute of Standards and Technology, 116(2): 621-646
  6. 6.
    Duan S B, Li Z L, Tang B H, Wu H, Ma L L, Zhao E Y and Li C R. 2013. Land Surface Reflectance Retrieval from Hyperspectral Data Collected by an Unmanned Aerial Vehicle over the Baotou Test Site. PLoS One, 8(6): e66972
  7. 7.
    Duan S B, Li Z L, Wu H, Tang B H, Ma L L, Zhao E Y and Li C R. 2014. Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 26: 12-20
  8. 8.
    Franks S, Neigh C S R, Campbell P K, Sun G Q, Yao T, Zhang Q Y, Huemmrich K F, Middleton E M, Ungar S G and Frye S W. 2017. EO-1 Data Quality and Sensor Stability with Changing Orbital Precession at the End of a 16 Year Mission. Remote Sensing, 9(5): 412
  9. 9.
    Gao C X, Ma L L, Liu Y K, Wang N, Qian Y G, Tang L L and Li C R. 2014. The Assessment of In-flight Dynamic Range and Response Linearity of Optical Payloads onboard GF-1 Satellite. Proc. of SPIE, Vol. 9264, 92641F [DOI: 10.1117/12.2068794]
  10. 10.
    Giles D M. 2019. AERONET (Aerosol Robotic Network)[OL]. [2019-09-26].
  11. 11.
    Honkavaara E, Hakala T, Peltoniemi J, Suomalainen J, Ahokas E and Markelin L. 2010. Analysis of Properties of Reflectance Reference Targets for Permanent Radiometric Test Sites of High Resolution Airborne Imaging Systems. Remote Sensing, 2(8): 1892-1917
  12. 12.
    Honkavaara E, Peltoniemi J, Ahokas E, Kulttinen R, Hyyppa J, Jaakkola J, Kaartinen H, Markelin L, Nurminen K and Suomalainen J. 2008. A Permanent Test Field for Digital Photogrammetric Systems. Photogrammetric Engineering and Remote Sensing, 74(1): 95-106
  13. 13.
    Li C R, Ma L L, Gao C X, Tang L L, Wang N, Liu Y K, Zhao Y G, Dou S, Zhang D D and Li X H. 2015a. Permanent target for optical payload performance and data quality assessment: Spectral characterization and a case study for calibration. Journal of Applied Remote Sensing, 8(1): 083498
  14. 14.
    Li C R, Ma L L, Yuan X F, Wang N, Tang L L, Qiu S and Li J J. 2014b. A color calibration method for spectral image based on radiative transfer mechanism. Proc. of SPIE, Vol. 9088, 90881E [DOI: 10.1117/12.2050297]
  15. 15.
    Li C R, Tang L L, Ma L L, Zhou Y S, Gao C X, Wang N, Li X H, Wang X H and Zhu X H. 2015b. A comprehensive calibration and validation site for information remote sensing. ISPRS Archives, 40(7W3): 1233-1240
  16. 16.
    Li C R, Tang L L, Ma L L, Zhou Y S, Wang N, Gao C X and Wang X H. 2014a. Progress of calibration and validation for quantitative remote sensing in China. Proc. of SPIE, Vol. 9264, 92640T [DOI: 10.1117/12.2068670]
  17. 17.
    Li Z Y, Qian Y G, Shen Q F, Wang N, Liu Y K and Ma L L. 2014. Leaf area index retrieval from remotely sensed hyperspectral data. Infrared and Laser Engineering, 43(3): 944-949
  18. 18.
    Liu Y K, Li C R, Ma L L, Qian Y G, Wang N, Gao C X and Tang L L. 2019b. Land surface reflectance retrieval from optical hyperspectral data collected with an unmanned aerial vehicle platform. Optics Express, 27(5): 7174-7195
  19. 19.
    Liu Y K, Li C R, Ma L L, Wang N, Qian Y G and Tang L L. 2017b. An automatic reflectance-based approach to vicarious radiometric calibrate the Landsat8 operational land imager. Proceedings of IGARSS 2017, pp. 4699-4702
  20. 20.
    Liu Y K, Ma L L, Wang N, Qian Y G, Qiu S, Li C R and Tang L L. 2017a. Vicarious radiometric calibration/validation of Landsat-8 operational land imager using a ground reflected radiance-based approach with Baotou site in China. Journal of Applied Remote Sensing, 11(4): 044004
  21. 21.
    Liu Y K, Ma L L, Wang N, Qian Y G, Zhao Y G, Qiu S, Gao C X, Long X X and Li C R. 2020. On-orbit radiometric calibration of the optical sensors on-board SuperView-1 satellite using three independent methods. Optics Express, 28(8): 11085-11105
  22. 22.
    Liu Y K, Ma Z H, Ma L L, Wang N, Qian Y G, Li C R and Tang L L. 2018. Vicarious radiometric calibration using a ground radiance-based approach: A case study of Sentinel 2A MSI. Proceedings of IGARSS 2018, pp. 3296-3299
  23. 23.
    Liu Y K, Qian Y G, Wang N, Ma L L, Gao C X, Qiu S, Li C R and Tang L L. 2019a. An improved dense dark vegetation based algorithm for aerosol optical thickness retrieval from hyperspectral data. Proc. of SPIE, Vol. 11028, 1102812
  24. 24.
    Liu Y K, Wang T X, Ma L L and Wang N. 2014. Spectral Calibration of Hyperspectral Data Observed From a Hyperspectrometer Loaded on an Unmanned Aerial Vehicle Platform. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6): 2630-2638
  25. 25.
    Lv X K, Li C R, Ma L L, Yuan X F and Li X H. 2013. A true color correction model for hyperspectral remote sensing image based on physical mechanism. Remote Sensing Technology and Application, 28(3): 467-473
  26. 26.
    Ma L L, Wang N, Zhao Y G, Liu Y K, Wang X H, Ma Z H, Li C R, Tang L L and Qian Y G. 2019. Improved vicarious radiometric calibration method considering adjacency effect for high resolution optical sensors. Proceedings of IGARSS 2019, pp. 9014-9017
  27. 27.
    Ma L L, Zhao Y G, Woolliams E R, Dai C H, Wang N, Liu Y K, Li L, Wang X H, Gao C X, Li C R and Tang L L. 2020. Uncertainty Analysis for RadCalNet Instrumented Test Sites Using the Baotou Sites BTCN and BSCN as Examples. Remote Sensing, 12(11): 1696
  28. 28.
    Ma Z H, Ma L L, Liu Y K, Zhao Y G, Wang N, Li C R and Tang L L. 2019. A method for hyperspectral reflectance reconstruction from automatic observation with multispectral radiometer. Acta Optica Sinica, 39(7): 371-380
  29. 29.
    Merchant D C, Schenk A, Habib A and Yoon T. 2004. USGS/OSU progress with digital camera in situ calibration methods. ISPRS Archives, Vol. 35
  30. 30.
    Pang B, Ma L L, Liu Y K, Wang N, Zhao Y G, Han Q J, Meng F R, Li C R, Tang L L, Chen Z M and Wang G Z. 2019. Ground-based automatic radiometric calibration of land observation satellite optical sensors and cross validation analysis. Remote Sensing Technology and Application, 34(1): 146-154
  31. 31.
    Qian Y G, Wang N, Ma L L, Liu Y K, Wu H, Tang B H, Tang L L and Li C R. 2016. Land surface temperature retrieved from airborne multispectral scanner mid-infrared and thermal-infrared data. Optics Express, 24(2): A257-A269
  32. 32.
    Wang N, Li C R, Ma L L, Liu Y K, Meng F R, Zhao Y G, Pang B, Qian Y G, Li W, Tang L L and Wang D J. 2017. Ground based automated radiometric calibration system in Baotou site, China. Proc. of SPIE, Vol. 10427, 104271J [DOI: 10.1117/12.2278072]
  33. 33.
    Zhao Y G, Ma L L, Li C R, Gao C X, Wang N and Tang L L. 2018. Radiometric Cross-Calibration of Landsat-8/OLI and GF-1/PMS Sensors Using an Instrumented Sand Site. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(10): 3822-3829
  34. 34.
    Zhou C C, Li C R, He W J, Ma L L and Zhao Y G. 2018. A smile effect correction method for dispersive imaging spectrometer based on simultaneous radiometric and spectral calibration. Proc. of SPIE, Vol. 10785, 107851W [DOI: 10.1117/12.2501444]
  35. 35.
    Zhou C C, Li C R, Hu J, Ma L L and Yu G. 2012. A study on relative radiometric correction method based on line frequency difference for airborne hyperspectral imagery. Remote Sensing Technology and Application, 27(1): 33-38
  36. 36.
    Zhou C C, Meng F R, Li C R, He W J and Li W. 2017b. A parametric elastic registration method for airborne multispectral line scan imager. Proceedings of ICCT 2017, pp. 1618-1622
  37. 37.
    Zhou Y S, Li C R, Ma L L, Yang M Y and Liu Q. 2014. Improved trihedral corner reflector for high-precision SAR calibration and validation. Proceedings of IGARSS 2014, pp. 454-457
  38. 38.
    Zhou Y S, Li C R, Tang L L, Gao C X, Ren L and Ma L L. 2015. Permanent Target for Synthetic Aperture Radar Image Resolution Assessment. Proceedings of IGARSS 2015, pp. 4284-4287
  39. 39.
    Zhou Y S, Li C R, Tang L L, Ma L L, Wang Q and Liu Q. 2017a. A Permanent Bar Pattern Distributed Target for Microwave Image Resolution Analysis. IEEE Geoscience and Remote Sensing Letters, 14(2): 164-168
  40. 40.
    Zhu B, Li C R, Wang X H and Wang C L. 2017. A new method to estimate SNR of remote sensing imagery. Proc. of SPIE, Vol. 10462, 1046209
  41. 41.
    Zhu B, Wang X H, Li Z Y, Dou S, Tang L L and Li C R. 2013. A new method based on Spatial Dimension Correlation and Fast Fourier Transform for SNR estimation in remote sensing images. Proceedings of IGARSS 2013, pp. 4114-4117
  42. 42.
    Zhu X H, Li C R, Tang L L and Ma L L. 2019. Retrieval and scale effect analysis of LAI over typical farmland from UAV-based hyperspectral data. Proc. of SPIE, Vol. 11149, 111490K [DOI: 10.1117/12.2535478]
  43. 43.
    Zhu X W, Li X H and Zhu B. 2013. Influence of the hyperspectral data quality based on CCSDS compression algorithm. Remote Sensing Information, 28(4): 29-36

Читать полностью

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