предыдущий|следующий

Bilateral independent cross-calibration method of the satellite-borne imaging altimeter

  • role： First author第一作者
  • Affiliation：

    College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China

  • Email：baizhuo@stu.ouc.edu.cn
  • Introduction：白卓，研究方向为干涉成像高度计的误差处理。E-mail： baizhuo@stu.ouc.edu.cn
BAI Zhuo1,  
  • Affiliation：

    College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China

ZHANG Haoxin1,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China

    Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China

  • Email：chunyongma@ouc.edu.cn
  • Introduction：马纯永，研究方向为干涉成像高度计的误差处理。E-mail： chunyongma@ouc.edu.cn
MA Chunyong12*,  
  • Affiliation：

    College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China

    Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China

ZHAO Chaofang12,  
  • Affiliation：

    College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China

    Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China

CHEN Ge12

реферат

The Surface Water and Ocean Topography (SWOT) mission, which is scheduled to launch in 2022, will carry a Ka-band Radar Interferometer to characterize the ocean mesoscale and submesoscale circulation. The observation errors must be reduced to realize the observation target of the SWOT mission. Among the errors, the baseline inclination error caused by the inaccuracy of the baseline inclination measurement and the phase error induced by the phase mismatch in the process of interferometric imaging are difficult to eliminate. These two errors make the sea surface height measurement in the swath inaccurate and identification of the characteristics of mesoscale phenomena challenging. Therefore, the purpose of this article is to correct phase error and baseline roll error to achieve the observation goal.Both errors are linear with across track distance from nadir. Assuming that all errors, except the phase and roll errors, have met the error budget requirements, a bilateral independent cross-calibration method is proposed to detect and mitigate the two spatial coherence errors. First, the errors of the left and right swaths in the experiment are calculated. Second, the observed values in the cross region are subtracted to reduce the influence of the ocean signal. Finally, the errors are estimated based on their linear correlation with the across track distance. The orbit parameters of SWOT and Jason-2 (before October 2016) provided by Archiving, Verification, and Interpretation of Satellite Oceanographic data are used for along-track sampling. The errors in the 25-day SWOT sample data are estimated by using the self-crossover and crossover (with Jason-2) methods.In the SWOT self-crossover, more than 90% of the error was corrected from 8 cm to 4 cm, and approximately 73% of the intersection-region standard deviation was adjusted from 6 cm to 2 cm.In the SWOT crossover with Jason-2, approximately 86% of the error was corrected from 8 cm to 4 cm, and approximately 55% of the intersection-region standard deviation was adjusted from 6 cm to 2 cm. Based on the absolute value and standard deviation, the error inversion effect of the self-crossover and crossover (with Jason-2) methods on the intersection point is good. Results show that the bilateral independent cross-calibration method effectively estimates the overall error and significantly reduces the error level in the case of low instrument accuracy.The crossover method weakens the influence of a larger portion of the ocean signal, increases the weight of the error in the calculation process, and calculates the error through the matrix. Given that the characteristics that the phase error is different from the baseline inclination error, the left and right swaths are cross-calibrated separately to calculate the gradient slope value in the cross-track direction of the unilateral swath.The calculated gradient slope value can be directly back-calculated to obtain the overall error and eliminate the error. The bilateral independent cross-calibration method not only inherits the ability of the cross-calibration algorithm to correct the baseline inclination error but also scientifically and effectively corrects the phase error. The experiment also suggests that the cross-calibration formula can be optimized based on the error characteristics to estimate other errors with gradient changes in the across track and contributes to error reduction.

ключеви́че слова́

cross-calibration;Error-correction;phase error;Baseline roll error;Interferometric imaging altimeter

References

  1. 1.
    Ablain M, Philipps S, Picot N and Bronner E. 2010. Jason-2 global statistical assessment and cross-calibration with jason-1. Marine Geodesy, 33(S1): 162-185
  2. 2.
    Amores A, Jordà G, Arsouze T and Le Sommer J. 2018. Up to what extent can we characterize ocean eddies using present-day gridded altimetric products?. Journal of Geophysical Research: Oceans, 123(10): 7220-7236
  3. 3.
    Chavanne C P and Klein P. 2010. Can oceanic submesoscale processes be observed with satellite altimetry. Geophysical Research Letters, 37(22): L22602
  4. 4.
    Chelton D B and Schlax M G. 2003. The accuracies of smoothed sea surface height fields constructed from tandem satellite altimeter datasets. Journal of Atmospheric and Oceanic Technology, 20(9): 1276-1302
  5. 5.
    Dibarboure G, Labroue S, Ablain M, Fjortoft R, Mallet A, Lambin J and Souyris J C. 2012. Empirical cross-calibration of coherent SWOT errors using external references and the altimetry constellation. IEEE Transactions on Geoscience and Remote Sensing, 50(6): 2325-2344
  6. 6.
    Dibarboure G and Ubelmann C. 2014. Investigating the performance of four empirical cross-calibration methods for the proposed SWOT mission. Remote Sensing, 6(6): 4831-4869
  7. 7.
    Durand M, Fu L L, Lettenmaier D P, Alsdorf D E, Rodriguez E and Esteban-Fernandez D. 2010. The surface water and ocean topography mission: observing terrestrial surface water and oceanic submesoscale eddies. Proceedings of the IEEE, 98(5): 766-779
  8. 8.
    Enjolras V, Vincent P, Souyris J C, Rodriguez E, Phalippou L and Cazenave A. 2006. Performances study of interferometric radar altimeters: from the instrument to the global mission definition. Sensors, 6(3): 164-192
  9. 9.
    Faugere Y, Dorandeu J, Lefevre F, Picot N and Femenias P. 2006. Envisat ocean altimetry performance assessment and cross-calibration. Sensors, 6(3): 100-130
  10. 10.
    Fu L L and Ubelmann C. 2014. On the transition from profile altimeter to swath altimeter for observing global ocean surface topography. Journal of Atmospheric and Oceanic Technology, 31(2): 560-568
  11. 11.
    Gómez-Navarro L, Fablet R, Mason E, Pascual A, Mourre B, Cosme E and Le Sommer J. 2018. SWOT spatial scales in the western Mediterranean Sea derived from pseudo-observations and an Ad Hoc filtering. Remote Sensing, 10(4): 599
  12. 12.
    Gaultier L, Ubelmann C and Fu L L. 2016a. The challenge of using future SWOT data for oceanic field reconstruction. Journal of Atmospheric and Oceanic Technology, 33(1): 119-126
  13. 13.
    Gaultier L, Ubelmann C and Fu L L. 2016b. SWOT Simulator Documentation Release 2.3.0[EB/OL]. (2017-03-15)[2017-05-13].
  14. 14.
    Lévy M, Iovino D, Resplandy L, Klein P, Madec G, Treguier A M, Masson S and Takahashi K. 2012. Large-scale impacts of submesoscale dynamics on phytoplankton: local and remote effects. Ocean Modelling, 43-44: 77-93
  15. 15.
    Ma C Y, Guo X X, Zhang H X, Di J K and Chen G. 2020. An investigation of the influences of SWOT sampling and errors on ocean eddy observation. Remote Sensing, 12(17): 2682
  16. 16.
    Metref S, Cosme E, Le Sommer J, Poel N, Brankart J M, Verron J and Navarro L G. 2019. Reduction of spatially structured errors in wide-swath altimetric satellite data using data assimilation. Remote Sensing, 11(11): 1336
  17. 17.
    Morrow R, Fu L L, Ardhuin F, Benkiran M, Chapron B, Cosme E, D’Ovidio F, Farrar J T, Gille S T, Lapeyre G, Le Traon P Y, Pascual A, Ponte A, Qiu B, Rascle N, Ubelmann C, Wang J B and Zaron E D. 2019. Global observations of fine-scale ocean surface topography with the Surface Water and Ocean Topography (SWOT) mission. Frontiers in Marine Science, 6: 232
  18. 18.
    Peral E and Esteban-Fernandez D. 2018. SWOT mission performance and error budget//IEEE International Geoscience and Remote Sensing Symposium. Valencia: IEEE: 8625-8628
  19. 19.
    Pujol M I, Dibarboure G, Le Traon P Y and Klein P. 2012. Using high-resolution altimetry to observe mesoscale signals. Journal of Atmospheric and Oceanic Technology, 29(9): 1409-1416
  20. 20.
    Rosen P A, Hensley S, Joughin I R, Li F K, Madsen S N, Rodriguez E and Goldstein R M. 2000. Synthetic aperture radar interferometry. Proceedings of the IEEE, 88(3): 333-382
  21. 21.
    Ubelmann C, Dibarboure G and Dubois P. 2018. A cross-spectral approach to measure the error budget of the SWOT altimetry mission over the ocean. Journal of Atmospheric and Oceanic Technology, 35(4): 845-857
  22. 22.
    Wang J B, Fu L L, Torres H S, Chen S M, Qiu B and Menemenlis D. 2019. On the spatial scales to be resolved by the surface water and ocean topography ka-band radar interferometer. Journal of Atmospheric and Oceanic Technology, 36(1): 87-99

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