предыдущий|следующий

Robust reconstruction of missing data in Feng Yun geostationary satellite land surface temperature products

  • role： First author第一作者
  • Affiliation：

    School of Resources and Environment Engineering, Anhui University, Hefei 230601, China

  • Email：rs312lzh@yeah.net
  • Introduction：刘紫涵(1990— )，男，硕士研究生，研究方向为遥感图像处理与应用。E-mail：rs312lzh@yeah.net
LIU Zihan1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Resources and Environment Engineering, Anhui University, Hefei 230601, China

  • Email：wuph@ahu.edu.cn
  • Introduction：吴鹏海(1987—    )，男，讲师，研究方向为遥感信息处理与应用的研究。E-mail：wuph@ahu.edu.cn
WU Penghai1*,  
  • Affiliation：

    School of Resources and Environment Engineering, Anhui University, Hefei 230601, China

WU Yanlan1,  
  • Affiliation：

    School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China

SHEN Huanfeng2,  
  • Affiliation：

    State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China

ZENG Chao3

реферат

Land Surface Temperature (LST) derived from geostationary satellites (GEO-LST) is one of the key parameters in analyzing diurnal climate and environment changes. Compared with polar-orbiting satellite data, the LST of geostationary satellites has become increasingly attractive because of its high temporal resolution. GEO-LST has been widely applied in meteorological, climatological, and hydrological studies. However, the existing GEO-LST products can suffer from cloud cover, cloud contamination, and atmospheric disturbance, resulting in missing data. The objective of this study is to develop a new method for reconstructing Feng Yun geostationary satellite (FY-2F) LSTs based on Diurnal Temperature Cycle (DTC) and robust regression.The hourly LSTs from FY-2F with 5 km spatial resolution are adopted for this study, and different types of missing pixels are simulated and reconstructed based on a new model. This model is composed of polynomial, Fourier, and Gaussian functions (PFG), and a robust regression called Levenberg–Marquardt (LM) algorithm is adopted for solving and optimizing the modelparameters. A performance test is conducted by comparing the PFG model based on the LM algorithm with the other two methods implemented on FY-2F data, namely, a method based on Least Square (LS) algorithm modeling of the previous model (van den Bergh. F VAN2006) and a method based on LS algorithm modeling of the PFG. Moreover, the new method is also tested for real LST products (with missing data).By comparing the reconstructed results of different methods (LM-PFG, LS-PFG, and LS-VAN2006) with the real LST values, the simulated experiment indicates that the LM-PFG method obtains the lowest root mean square compared with the other two methods. The stability analysis of these methods shows that LM-PFG is the least sensitive to missing samples, which validates that LM-PFG is more stable than LS-PFG and LS-VAN2006. Simultaneously, experimental results show that LM-PFG can also reconstruct the missing values with high accuracy and better than the other methods.In this study, an approach for reconstructing GEO-LSTs based on DTC and robust regression is proposed. The method is evaluated using simulated and real FY-2F LST products and can obtain high scores on quantitative evaluation and visual connectivity. Further work can be conducted to expand this method to other potential GEO-LSTs, such as Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager LSTs or Geostationary Operational Environmental Satellite Imager LSTs. Similar to previous studies, the reconstructed LST values of the proposed method are estimated under cloud-free conditions. LSTs under cloudy conditions should be investigated in future studies.

ключеви́че слова́

land surface temperature;geostationary satellite;diurnal temperature cycle;robust regression;FY-2F

References

  1. 1.
    Cai D H, Guo N and Li C W. 2009. Interpolation of air temperature based on DEM over eastern region of Gansu. Journal of Arid Meteorology, 27(1): 10–17
  2. 2.
    Coops N C, Duro D C, Wulder M A and Han T. 2007. Estimating afternoon MODIS land surface temperatures (LST) based on morning MODIS overpass, location and elevation information. International Journal of Remote Sensing, 28(10): 2391–2396
  3. 3.
    Duan S B, Li Z L, Wang N, Wu H and Tang B H. 2012. Evaluation of six land-surface diurnal temperature cycle models using clear-skyin situ and satellite data. Remote Sensing of Environment, 124: 15–25
  4. 4.
    Duan S B, Li Z L, Tang B H, Wu H, Tang R L, Bi Y Y and Zhou G Q. 2014. Estimation of diurnal cycle of land surface temperature at high temporal and spatial resolution from clear-sky MODIS data. Remote Sensing, 6(4): 3247–3262
  5. 5.
    Elagib N A, Alvi S H and Mansell M G. 1999. Day-length and extraterrestrial radiation for Sudan: a comparative study. International Journal of Solar Energy, 20(2): 93–109
  6. 6.
    Fan J Y. 2003. A modified Levenberg-Marquardt algorithm for singular system of nonlinear equations. Journal of Computational Mathematics, 21(5): 625–636
  7. 7.
    Finsterle S and Kowalsky M B. 2011. A truncated Levenberg-Marquardt algorithm for the calibration of highly parameterized nonlinear models. Computers and Geosciences, 37(6): 731–738
  8. 8.
    Gavin H P. 2016. The Levenberg-Marquardt Method for Nonlinear Least Squares Curve-Fitting Problems. Durham: Department of Civil and Environmental Engineering, Duke University: 1–18
  9. 9.
    Göttsche F M and Olesen F S. 2001. Modelling of diurnal cycles of brightness temperature extracted from METEOSAT data. Remote Sensing of Environment, 76(3): 337–348
  10. 10.
    Göttsche F M and Olesen F S. 2009. Modelling the effect of optical thickness on diurnal cycles of land surface temperature. Remote Sensing of Environment, 113(11): 2306–2316
  11. 11.
    Huang F, Zhan W F, Duan S B, Ju W M and Quan J L. 2014. A generic framework for modeling diurnal land surface temperatures with remotely sensed thermal observations under clear sky. Remote Sensing of Environment, 150: 140–151
  12. 12.
    Jing Y L, Li J B and Lin G. 2008. Research on numerical conversion algorithms of structure dynamic analysis in time and frequency domains. Journal of Dalian University of Technology, 48(6): 875–880
  13. 13.
    Ke L H, Ding X L and Song C Q. 2013. Reconstruction of time-series MODIS LST in Central Qinghai-Tibet Plateau using geostatistical approach. IEEE Geoscience and Remote Sensing Letters, 10(6): 1602–1606
  14. 14.
    Keramitsoglou I, Kiranoudis C T and Weng Q H. 2013. Downscaling geostationary land surface temperature imagery for urban analysis. IEEE Geoscience and Remote Sensing Letters, 10(5): 1253–1257
  15. 15.
    李静, 罗奇峰. 2013. HHT变换的特点及其优越性分析//中国地震学会成立三十年学术研讨会. 北京: 中国地震学会Li J and Luo Q F. 2013. Study on estimation of LST under cloudy region in MODIS images//The Thirty Year Academic Conference of the Seismological Society of China. Beijing: Seismological Society of China
  16. 16.
    Li Z L, Tang B H, Wu H, Ren H Z, Yan G J, Wan Z M, Trigo I F and Sobrino J A. 2013. Satellite-derived land surface temperature: current status and perspectives. Remote Sensing of Environment, 131: 14–37
  17. 17.
    刘梅. 2012. MODIS影像中云覆盖像元地表温度的估算研究. 南京: 南京大学Liu M. 2012. Study on Estimation of LST under Cloudy Region in MODIS Images. Nanjing: Nanjing University
  18. 18.
    Ma C F and Jiang L H. 2007. Some research on Levenberg-Marquardt method for the nonlinear equations. Applied Mathematics and Computation, 184(2): 1032–1040
  19. 19.
    Madsen K, Nielsen H B and Tingleff O. 2004. Methods for non-linear least squares problems (2nd ed.). SIAM Journal on Optimization, 16(1): 487–490
  20. 20.
    欧阳晓莹, 贾立. 2011. 基于静止气象卫星FY-2C/SVISSR的地表温度时间序列反演研究//第五届海峡两岸遥感遥测会议. 哈尔滨: 中国地理学会Ouyang X Y and Jia L. 2011. Retrieval of land surface temperature time series based on geostationary meteorological satellite FY-2C/SVISSR//5th Annual Symposium of Remote Sensing on Both Sides of the Taiwan Straits. Harbin: The Geographical Society of China
  21. 21.
    Parton W J and Logan J A. 1981. A model for diurnal variation in soil and air temperature. Agricultural Meteorology, 23: 205–216
  22. 22.
    Schädlich S, Göttsche F M and Olesen F S. 2001. Influence of land surface parameters and atmosphere on METEOSAT brightness temperatures and generation of land surface temperature maps by temporally and spatially interpolating atmospheric correction. Remote Sensing of Environment, 75(1): 39–46
  23. 23.
    Si S L and Guan Y. 2006. Determination of optimal power of data fitting to characteristic curve of trigonometric function. Computer Engineering and Design, 27(24): 4660–4662
  24. 24.
    Song T, Duan Z, Liu J Z, Shi J Z, Yan F, Sheng S J, Huang J and Wu W. 2015. Comparison of four algorithms to retrieve land surface temperature using Landsat 8 satellite. Journal of Remote Sensing, 19(3): 451–464
  25. 25.
    Udahemuka G, van den Bergh F, van Wyk B J and van Wyk M A. 2007. Robust fitting of diurnal brightness temperature cycle//18th Annual Symposium of the Pattern Recognition Association of South Africa (PRASA). Pietermaritzburg, Kwazulu-Natal, South Africa
  26. 26.
    van den Bergh F, van Wyk M A and van Wyk B J. 2006. A Comparison of data-driven and model-driven approaches to brightness temperature diurnal cycle interpolation//Proceeding of the 17th Annual Symposium of the Pattern Recognition Association of South Africa. Parys, South Africa: 252–256
  27. 27.
    Wei F, Wang S J, Liang J B, Wang Y, Zhang S Y, Jing T, Zhang H X, Zhang B Q and Leng S. 2013. Next generation Space Environment Monitor (SEM) for FY-2 satellite series. Chinese Journal of Geophysics, 56(1): 1–11
  28. 28.
    Wilamowski B M and Yu H. 2010. Improved computation for Levenberg-Marquardt training. IEEE Transactions on Neural Networks, 21(6): 930–937
  29. 29.
    Yang L and Chen Y P. 2005. On the convergence of a new Levenberg-Marquardt method. Mathematica Numerica Sinica, 27(1): 55–62
  30. 30.
    Yang L and Chen Y P. 2008. A new globally convergent Levenberg-Marquardt method for solving nonlinear system of equations. Mathematica Numerica Sinica, 30(4): 388–396
  31. 31.
    Zhan W F, Chen Y H, Zhou J, Wang J F, Liu W Y, Voogt J, Zhu X L, Quan J L and Li J. 2013. Disaggregation of remotely sensed land surface temperature: literature survey, taxonomy, issues, and caveats. Remote Sensing of Environment, 131: 119–139
  32. 32.
    Zhang H Y and Geng Z. 2009. Novel interpretation for Levenberg-Marquardt algorithm. Computer Engineering and Applications, 45(19): 5–8
  33. 33.
    Zhou Y, Qin Z H and Bao G. 2014. Progress in retrieving land surface temperature for the cloud-covered pixels from thermal infrared remote sensing data. Spectroscopy and Spectral Analysis, 34(2): 364–369
  34. 28.
    Wu P H, Shen H F, Zhang L P and Göttsche F M. 2015. Integrated fusion of multi-scale Polar-orbiting and geostationary satellite observations for the mapping of high spatial and temppral resolution land surface temperature. Remote Sensing of Enviroment, 156: 169–181

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