Practice and reflection on the construction of remote sensing products validation network for the land observation satellite

  • role: First author第一作者
  • Affiliation:

    National Engineering Laboratory for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Email:mall@aircas.ac.cn
  • Introduction:E-mail mall@aircas.ac.cn
MA Lingling,  
  • role: Corresponding author通信作者
  • Affiliation:

    National Engineering Laboratory for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Email:zhouxiang@aircas.ac.cn
  • Introduction:E-mailzhouxiang@aircas.ac.cn
ZHOU Xiang*,  
  • Affiliation:

    National Engineering Laboratory for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

WANG Ning,  
  • Affiliation:

    National Engineering Laboratory for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

TAO Zui,  
  • Affiliation:

    National Engineering Laboratory for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

ZHAO Yongguang,  
  • Affiliation:

    National Engineering Laboratory for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

ZHU Xiaohua,  
  • Affiliation:

    National Engineering Laboratory for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

GAO Caixia,  
  • Affiliation:

    National Engineering Laboratory for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

YANG Jian,  
  • Affiliation:

    National Engineering Laboratory for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

GAO Hailiang,  
  • Affiliation:

    National Engineering Laboratory for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

YANG Hong,  
  • Affiliation:

    National Engineering Laboratory for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

XIAO Qing,  
  • Affiliation:

    National Engineering Laboratory for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

LI Qiangzi,  
  • Affiliation:

    National Engineering Laboratory for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

LYU Tingting,  
  • Affiliation:

    National Engineering Laboratory for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

ZHANG Fengli,  
  • Affiliation:

    National Engineering Laboratory for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

ZANG Wenqian

реферат

Validation of remote sensing products, as a crucial process bridging remote sensing data products and their application services, is essential for meeting the increasing demands for precision and performance in a series of quantitative applications. Such a validation is also necessary for improving the algorithm and production procedure after collecting feedback in such applications.Over the years, numerous sites for the validation of land remote sensing products have been established domestically and internationally. However, the theoretical and technical systems in the construction of a validation network is still not perfect in the entire process. These systems include the site characteristic representation and selection, measurement of surface and/or atmospheric parameters, sampling of measurement, and validation service mode toward different users. The possible reasons include the complexity and dynamic nature of the Earth system that lead the interaction between different spheres, land surface heterogeneities and uniformities, system and random errors in measurements, and various types of remote sensing satellite products for different applications. These problems result in difficulties in fully utilizing existing facilities. Therefore, to solve a series of basic theories and techniques problems, and to provide new models and specific solutions become the precondition in improving the application efficiency of validation system infrastructure at present and in the near future.In this research context, the status and development trends are first analyzed, including the research domain of ground observation networks for validation, acquisition technology for ground reference truth, and comprehensive validation services for remote sensing products domestically and internationally. Thereafter, three basis theories (i.e., spatial variability theory in geostatistics, uncertainty theory in metrology, and optimization theory in operations research) are adopted to improve the validation methods. These three theories explore the spatial representativeness characterization of the network of product validation, uncertainty analysis and transfer of the fiducial reference measurements, comprehensive weighting and composition of multiple validation results, and optimal balance between the validation resources and user demands. Based on the theories, a framework is developed for the methodological system of the terrestrial observation satellite product validation network. This framework lays the theoretical foundation for the construction of a remote sensing production validation system and plays an important supporting role in forming targeted solutions. Aiming with the construction of the National Civil Space Infrastructure and the application needs of validation of key common products, considering overall layout, product coverage completeness, spatio-temporal consistency, and traceability, this study proposes specific requirements for the validation targets, validation areas, validation methods, validation accuracy, validation frequency, and service mode of Chinese land observation satellite remote sensing product validation network. The blueprint of the validation network for the “14th Five Year Plan” was designed based on the proposed methodology.This study coordinates the construction of a comprehensive product validation network, comprising an aerial collaborative validation system, a benchmark referenced transfer and fiducial reference measurement system, and a high accuracy validation service system. This network forms a technical solution for Chinese land observation satellite remote sensing product validation. The proposed solution will promote the resolution of related bottleneck issues, such as the accuracy, efficiency, and consistency of product validation. Lastly, the proposed solution will significantly improve the theoretical method system of the product validation network by fully utilizing and optimizing the use of domestic land observation satellite remote sensing data, and enhancing the level of quantitative applications.

ключеви́че слова́

land observation satellite;remote sensing products;validation;fiducial reference measurements;service system;theoretical and methodology system

References

  1. 1.
    Baret F, Morissette J T, Fernandes R A, Champeaux J L, Myneni R B, Chen J, Plummer S, Weiss M, Bacour C, Garrigues S and Nickeso J E. 2006. Evaluation of the representativeness of networks of sites for the global validation and intercomparison of land biophysical products: proposition of the CEOS-BELMANIP. IEEE Transactions on Geoscience and Remote Sensing, 44(7): 1794-1803
  2. 2.
    Bauer P, Dueben P D, Hoefler T, Quintino T, Schulthess T C and Wedi N P. 2021. The digital revolution of earth-system science. Nature Computational Science, 1(2): 104-113
  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.
    Breton E, Bouillon A, Gachet R and Delussy F. 2002. Pre-flight and in-flight geometric calibration of SPOT5 HRG and HRS images. [2024-02-22].
  5. 5.
    Brown L A, Camacho F, García-Santos V, Origo N, Fuster B, Morris H, Pastor-Guzman J, Sánchez-Zapero J, Morrone R, Ryder J, Nightingale J, Boccia V and Dash J. 2021. Fiducial reference measurements for vegetation bio-geophysical variables: an end-to-end uncertainty evaluation framework. Remote Sensing, 13(16): 3194
  6. 6.
    Campbell J L, Burrows S, Gower S T and Cohen W B. 1999. Bigfoot Field Manual, Version 2.1. Oak Ridge National Lab. (ORNL)
  7. 7.
    Chander G, Xiong X X, Choi T and Angal A. 2010. Monitoring on-orbit calibration stability of the Terra MODIS and Landsat 7 ETM+ sensors using pseudo-invariant test sites. Remote Sensing of Environment, 114(4): 925-939
  8. 8.
    Colliander A, Jackson T J, Bindlish R, Chan S, Das N, Kim S B, Cosh M H, Dunbar R S, Dang L, Pashaian L, Asanuma J, Aida K, Berg A, Rowlandson T, Bosch D, Caldwell T, Caylor K, Goodrich D, Al Jassar H, Lopez-Baeza E, Martínez-Fernández J, González-Zamora A, Livingston S, Mcnairn H, Pacheco A, Moghaddam M, Montzka C, Notarnicola C, Niedrist G, Pellarin T, Prueger J, Pulliainen J, Rautiainen K, Ramos J, Seyfried M, Starks P, Su Z, Zeng Y, Van Der velde R, Thibeault M, Dorigo W, Vreugdenhil M, Walker J P, Wu X, Monerris A, O’neill P E, Entekhabi D, Njoku E G and Yueh S. 2017. Validation of SMAP surface soil moisture products with core validation sites. Remote Sensing of Environment, 191: 215-231
  9. 9.
    Fang H L, Che T, Jin R, Li A N, Li X, Li Z Y, Liu S M, Ma M G, Xiao Q and Zhang Y G. 2021. On the construction of China’s fiducial reference measurement network for land surface remote sensing product validation. Advances in Earth Science, 36(12): 1215-1223
  10. 10.
    Frederic B , Weiss M , Allard D , Garrigues S, Leroy M, Jeanjean H, Fernandes R, Myneni R, Privette J and Morisette J.2005. VALERI: a network of sites and a methodology for the validation of medium spatial resolution land satellite products. Remote Sensing of Environment 76 (3):36-9.
  11. 11.
    Friedl M A, Davis F W, Michaelsen J and Moritz M A. 1995. Scaling and uncertainty in the relationship between the NDVI and land surface biophysical variables: an analysis using a scene simulation model and data from FIFE. Remote Sensing of Environment, 54(3): 233-246
  12. 12.
    Gao C X, Liu Y K, Liu J R, Ma L L, Wu Z F, Qiu S, Li C R, Zhao Y G, Han Q J, Zhao E Y, Qian Y G and Wang N. 2020. Determination of the key comparison reference value from multiple field calibration of Sentinel-2B/MSI over the Baotou site. Remote Sensing, 12(15): 2404
  13. 13.
    Gao H L, Gu X F, Zhou X, Yu T and Wang Y X. 2023. Analysis of the development trend of Chinese remote sensing validation sites and infrastructure construction. National Remote Sensing Bulletin, 27(5): 1088-1098
  14. 14.
    Goryl P, Fox N, Donlon C and Castracane P. 2023. Fiducial reference measurements (FRMs): what are they? Remote Sensing, 15(20): 5017
  15. 15.
    Gube M, Gärtner V and Schmetz J. 1996. Analysis of the operational calibration of the Meteosat infrared‐window channel. Meteorological Applications, 3(4): 307-316
  16. 16.
    Hu X D, Yuan Y X, Zhang X S. 2012. Review and prospect for the development of operations research. Bulletin of Chinese Academy of Sciences, 27(2): 145-160
  17. 17.
    Huang T Q and Niu D. 2005. Chinese ecosystem research network (CERN)- basic information, achievements and perspectives. Advances in Earth Science, 20(8): 895-902
  18. 18.
    Jia Z Z, Liu S M, Xu Z W, Chen Y J and Zhu M J. 2012. Validation of remotely sensed evapotranspiration over the Hai River Basin, China. Journal of Geophysical Research: Atmospheres, 117(D13): D13113
  19. 19.
    Jimenez-Berni J A, Deery D M, Rozas-Larraondo P, Condon A G, Rebetzke G J, James R A, Bovill W D, Furbank R T and Sirault X R R. 2018. High throughput determination of plant height, ground cover, and above-ground biomass in wheat with LiDAR. Frontiers in Plant Science, 9: 237
  20. 20.
    Jin R, Li X, Ma M G, Ge Y, Liu S M, Xiao Q, Wen J G, Zhao K, Xin X P, Ran Y H, Liu Q H and Zhang R H. 2017. Key methods and experiment verification for the validation of quantitative remote sensing products. Advances in Earth Science, 32(6): 630-642
  21. 21.
    Joint Committee for Guides in Metrology. 2008. JCGM 100: evaluation of measurement data - guide to the expression of uncertainty in measurement. [2024-02-22].
  22. 22.
    Justice C, Belward A, Morisette J, Lewis P, Privette J and Baret F. 2000. Developments in the ‘validation’ of satellite sensor products for the study of the land surface. International Journal of Remote Sensing, 21(17): 3383-3390
  23. 23.
    Kerr Y H, Al-Yaari A, Rodriguez-Fernandez N, Parrens M, Molero B, Leroux D, Bircher S, Mahmoodi A, Mialon A, Richaume P, Delwart S, Al Bitar A, Pellarin T, Bindlish R, Jackson T J, Rüdiger C, Waldteufel P, Mecklenburg S and Wigneron J P. 2016. Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation. Remote Sensing of Environment, 180: 40-63
  24. 24.
    Li C R, Ma L L, Tang L L, Gao C X, Qian Y G, Wang N and Wang X H. 2021. A comprehensive calibration site for high resolution remote sensors dedicated to quantitative remote sensing and its applications. National Remote Sensing Bulletin, 25(1): 198-219
  25. 25.
    Li Z L, Liu Q L and Tang J B. 2017. Towards a scale-driven theory for spatial clustering. Acta Geodaetica et Cartographica Sinica, 46(10): 1534-1548
  26. 26.
    Li Z N and Pan W Q. 2010. Econometrics. 3rd ed. Beijing: Higher Education Press
  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.
    Matheron G. 1963. Principles of geostatistics. Economic Geology, 58(8): 1246-1266
  29. 29.
    Mittaz J, Merchant C J and Woolliams E R. 2019. Applying principles of metrology to historical Earth observations from satellites. Metrologia, 56(3): 032002
  30. 30.
    Moreno J, Alonso L, Fernández G, Fortea J, Gandía S, Guanter L, García J, Martí J, Melia J, De Coca F, García-Haro J, Martínez B, Verger A, Sobrino J, Cuenca J, Jiménez J, Sòria G, Romaguera M, Zaragoza M, Martínez-Lozano J, Utrillas M, Estellés V, Gómez J, Calpe-Maravilla J, Vila-Frances J, Ferrer F L, Belmonte A, Piqueras J, Moratalla A, Cuesta A, Rubio E, Riquelme F, Ramos A, Garcia F, Cruz A, Corcoles H, Urrea RL, Pujadas M, Molero F, Herranz A, Habermeyer M, Bachmann M, Holzwarth S, Muller A, Frederic B, Beal D, D’Urso G, Giorgiogaggia R, Lazzaro U, Boussema R, Abdelfattah R, Bouchnak H, Roujean J, Samain O, Blanchi R, Davidson M. 2004. The SPECTRA Barrax Campaign (SPARC): Overview and First Results from CHRIS Data. European Space Agency
  31. 31.
    Morisette J, Privette J L, Justice C, Olson D, Dwyer J L, Davis P, Starr D and Wickland D. 1999. The EOS Land Validation Core Sites: Background Information and Current Status. NASA
  32. 32.
    Nappo C J, Caneill J Y, Furman R W, Gifford F A, Kaimal J C, Kramer M L, Lockhart T J, Pendergast M M, Pielke R A, Randerson D, Shreffler J H and Wyngaard J C. 1982. The workshop on the representativeness of meteorological observations, June 1981, Boulder, Colo. Bulletin of the American Meteorological Society, 63(7): 761-764
  33. 33.
    Origo N, Gorroño J, Ryder J, Nightingale J and Bialek A. 2020. Fiducial Reference Measurements for validation of Sentinel-2 and Proba-V surface reflectance products. Remote Sensing of Environment, 241: 111690
  34. 34.
    Román 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
  35. 35.
    State Administration for Market Regulation and Standardization Administration. 2018. GB/T 36296-2018 Guide for the validation of remote sensing products. Beijing: Standards Press of China
  36. 36.
    State Administration for Market Regulation and Standardization Administration. 2020. GB/T 39468-2020 General methods for the validation of terrestrial quantitative remote sensing products. Beijing: Standards Press of China
  37. 37.
    Selection and arrangement of the surface observation field for the validation of terrestrial remote sensing products. Beijing: Standards Press of China
  38. 38.
    State Administration for Market Regulation and Standardization Administration. 2021a. GB/T 40033-2021 Validation of land surface evapotranspiration remote sensing products. Beijing: Standards Press of China
  39. 39.
    State Administration for Market Regulation and Standardization Administration. 2021b. GB/T 40034-2021 Validation of leaf area index remote sensing products. Beijing: Standards Press of China
  40. 40.
    State Administration for Market Regulation and Standardization Administration. 2021c. GB/T 40038-2021 Validation of vegetation index remote sensing products. Beijing: Standards Press of China
  41. 41.
    State Administration for Market Regulation and Standardization Administration. 2021d. GB/T 40039-2021 Validation of soil moisture remote sensing products. Beijing: Standards Press of China
  42. 42.
    State Administration for Market Regulation and Standardization Administration. 2022a. GB/T 41279-2022 Validation of albedo remote sensing products. Beijing: Standards Press of China
  43. 43.
    State Administration for Market Regulation and Standardization Administration. 2022b. GB/T 41281-2022 Validation of photosynthetically active radiation remote sensing products. Beijing: Standards Press of China
  44. 44.
    State Administration for Market Regulation and Standardization Administration. 2022c. GB/T 41282-2022 Validation of fractional vegetation cover remote sensing products. Beijing: Standards Press of China
  45. 45.
    State Administration for Market Regulation and Standardization Administration. 2022d. GB/T 41534-2022 Validation of surface temperature remote sensing products. Beijing: Standards Press of China
  46. 46.
    State Administration for Market Regulation and Standardization Administration. 2022e. GB/T 41535-2022 Validation of aerosol optical depth remote sensing products. Beijing: Standards Press of China
  47. 47.
    State Administration for Market Regulation and Standardization Administration. 2022f. GB/T 41536-2022 Validation of land cover remote sensing products. Beijing: Standards Press of China
  48. 48.
    State Administration for Market Regulation and Standardization Administration. 2022g. GB/T 41537-2022 Validation of snow cover area remote sensing products. Beijing: Standards Press of China
  49. 49.
    State Administration for Market Regulation and Standardization Administration. 2022h. GB/T 41538-2022 Validation of surface emissivity remote sensing products. Beijing: Standards Press of China
  50. 50.
    Tansock J, Bancroft D, Butler J, et al. 2015. Guidelines for Radiometric Calibration of Electro-Optical Instruments for Remote Sensing[OL]. [2021-11-10]. .
  51. 51.
    Wen J G, Liu Q H, Li Z Y, Li X, Liu S M, Xiao Q, Gao Z H, Ma M G, Che T, Liu L Y, Fang H L, Yan G J, Ge Y, Chen E X, Zhang Y, Ma L L, Wu X D, Chen X. 2023. A review of the development of remote sensing field experiments and product validation in China. National Remote Sensing Bulletin, 27(3): 573-583
  52. 52.
    Woolliams E R, Fox N P, Cox M G, Harris P M and Harrison N J. 2006. The CCPR K1-a key comparison of spectral irradiance from 250 nm to 2500 nm: measurements, analysis and results. Metrologia, 43(2): S98-S104
  53. 53.
    Woolliams, E, Hueni A, and Gorrono J. 2015. Intermediate uncertainty analysis for earth observation (instrument calibration). NPL Training Course Textbook. Available online: http://www. emceoc. org/documents/uaeo-int-trg-course. pdf (accessed on 28 May 2015).
  54. 54.
    Wu X D, Xiao Q, Wen J G, You D Q and Hueni A. 2019. Advances in quantitative remote sensing product validation: overview and current status. Earth-Science Reviews, 196: 102875
  55. 55.
    Xu B D, Li J, Liu Q H, Xin X Z, Zeng Y L and Yin G F. 2015. Review of methods for evaluating representativeness of ground station observations. Journal of Remote Sensing (in Chinese), 19(5): 703-718
  56. 56.
    Xu B D, Li J, Park T, Liu Q H, Zeng Y L, Yin G F, Zhao J, Fan W L, Yang L, Knyazikhin Y and Myneni R B. 2018. An integrated method for validating long-term leaf area index products using global networks of site-based measurements. Remote Sensing of Environment, 209: 134-151
  57. 57.
    Yang P, Bai Y F, Song C C and Wu X D. 2020. Construction of long-term ecological research sites in field station: status, progress and prospect. Bulletin of Chinese Academy of Sciences, 35(1): 125-134
  58. 58.
    Zhang R D. 2005. Spatial Variability Theory and Application. Beijing: Science Press: 13-15
  59. 59.
    Zhang R H, Tian J, Li Z L, Su H B and Chen S H. 2010. Principles and methods for the validation of quantitative remote sensing products. Science China Earth Sciences, 53(5): 741-751

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