Evaluation and characteristic research in diurnal surface temperature cycle in China using FY-2F data

  • role: First author第一作者
  • Email:xiangchenmeng@yeah.net
  • Introduction:孟翔晨,1990年生,男,博士研究生,研究方向为热红外地表温度反演的理论和应用。E-mail:xiangchenmeng@yeah.net
MENG Xiangchen,  
  • No information about the author is available
LIU Hao,  
  • role: Corresponding author通信作者
  • Email:Jie_Cheng@bnu.edu.cn
  • Introduction:程洁,1981年生,男,副教授,研究方向为热红外定量遥感。E-mail:Jie_Cheng@bnu.edu.cn
CHENG Jie*

ملخص

Diurnal Surface Temperature Cycle (DSTC) model is an important input parameter in the field of meteorology, hydrology, and ecology. In the past 20 years, various DSTC models under clear-sky conditions have been developed on the basis of Spinning Enhanced Visible and Infrared Imager (SEVIRI) and Geostationary Operational Environment Satellite (GOES) satellites. However, only a few related studies have focused on China due to its complex topography, geomorphology, and climatic conditions. This situation restricts the application and development of DSTC models in the region. Although the DSTC model is mature, its evaluation is often based on the data of a certain point or day, which lacks verification at a large range of time and spatial scales. Moreover, the free variable setting of the DSTC model cannot meet the need at large time and spatial scales. Thus, the variable setting of the DSTC model must be expanded. In this study, six DSTC models were evaluated on the basis of FY-2F land surface temperature product at the monthly average and large space scales for China. In addition, JNG06 model was used to analyze the diurnal variation characteristics of Land Surface Temperature (LST) with the changes in seasonality, latitude and longitude, and land cover types. Semi-empirical model is simple and convenient and has a wide range of applications. Physical model is close to the actual physical condition of the surface. Therefore, five semi-empirical models, namely, GOT01, VAN06, JNG06, INA08, and GOT09, and the GEM-V physical model were used for DSTC simulation and analysis. On the basis of the geographical location of 194 meteorological stations, the corresponding FY-2F LST data were extracted, and the average monthly and hourly LST data after quality control were set as the model initial value. To evaluate the fitting accuracy of DSTC model in China, the model evaluation was divided into two parts: 1) the fitting of DSTC model was divided into five time periods, and the precision of DSTC model was analyzed; and 2) the fitting accuracy of each model under different land cover types was statistically explored on the basis of the land cover types of 194 stations. JNG06 model was also used to analyze the diurnal variation characteristics of LST with the changes in seasonality, latitude and longitude, and land cover types. Analysis of the average root mean square error (RMSE) of five time windows showed that GOT09 model obtained the global optimal fitting accuracy with an RMSE of 0.89 K, followed by JNG06 and GEM_V models with RMSEs of 0.92 and 0.94 K, respectively. GOT01, INA08, and VAN06 models obtained the worst accuracy. Each model had the best fitting precision in urban and built-up, cropland/natural vegetation mosaic, and evergreen broadleaf forest with an RMSE between 0.89 and 0.92 K. The fitting precision in mixed forest and cropland followed with an RMSE of around 1.0 K. Each model had the worst fitting precision in barren or sparse vegetation and savannas with an RMSE of above 1.3 K. From the results, we can conclude that the accuracies of GOT01, VAN06, and INA08 are poor in all land cover types and time windows, and the remaining models are relatively robust with close accuracy. The land surface temperature simulated by JNG06 model in four land cover types vary with the changes in latitude and longitude. The rule of LST varies with the change in latitude and but is unaffected by the change in longitude. The DSTC model can be used as the input parameter of climate and hydrological models. The model can also be used as reference for future studies on the DSTC model and its applications.

مفهوم

remote sensing;FY-2F;MCD12C1;LST (Land Surface Temperature);diurnal surface temperature cycle;meteorology stations

References

  1. 1.
    Aires F, Prigent C and Rossow W B. 2004. Temporal interpolation of global surface skin temperature diurnal cycle over land under clear and cloudy conditions. Journal of Geophysical Research: Atmospheres, 109(D4): D04313
  2. 2.
    Chen Y, Duan S B, Leng P, Chen Y Y and Han X J. 2016. Modeling of diurnal cycle of land surface temperature based on polar orbiting satellite thermal infrared data. Remote Sensing Information, 31(6): 7–14
  3. 3.
    Cheng J, Liang S L, Wang J D and Li X W. 2010. A stepwise refining algorithm of temperature and emissivity separation for hyperspectral thermal infrared data. IEEE Transactions on Geoscience and Remote Sensing, 48(3): 1588–1597
  4. 4.
    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
  5. 5.
    Crosson W L, Al-Hamdan M Z, Hemmings S N J and Wade G M. 2012. A daily merged MODIS Aqua-Terra land surface temperature data set for the conterminous United States. Remote Sensing of Environment, 119: 315–324
  6. 6.
    Dickinson R E, Henderson-Sellers A and Kennedy P J. 1993. Biosphere-atmosphere Transfer Scheme (BATS) Version 1E As Coupled to the NCAR Community Climate Model. NCAR Technical Note NCAR/TN-387+STR. NCAR [DOI: 10.5065/D67W6959]
  7. 7.
    Duan S B, Li Z L, Tang B H, Wu H and Tang R L. 2014. Generation of a time-consistent land surface temperature product from MODIS data. Remote Sensing of Environment, 140: 339–349
  8. 8.
    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-sky in situ and satellite data. Remote Sensing of Environment, 124: 15–25
  9. 9.
    Friedl M A, Sulla-Menashe D, Tan B, Schneider A, Ramankutty N, Sibley A and Huang X M. 2010. MODIS collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114(5): 168–182
  10. 10.
    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
  11. 11.
    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
  12. 12.
    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
  13. 13.
    Ignatov A and Gutman G. 1999. Monthly mean diurnal cycles in surface temperatures over land for global climate studies. Journal of Climate, 12(7): 1900–1910
  14. 14.
    Inamdar A K, French A, Hook S, Vaughan G and Luckett W. 2008. Land surface temperature retrieval at high spatial and temporal resolutions over the southwestern United States. Journal of Geophysical Research: Atmospheres, 113(D7): D07107
  15. 15.
    Jiang G M, Li Z L and Nerry F. 2006. Land surface emissivity retrieval from combined mid-infrared and thermal infrared data of MSG-SEVIRI. Remote Sensing of Environment, 105(4): 326–340
  16. 16.
    Jin M L. 2000. Interpolation of surface radiative temperature measured from polar orbiting satellites to a diurnal cycle: 2. Cloudy‐pixel treatment. Journal of Geophysical Research: Atmospheres, 105(D3): 4061–4076
  17. 17.
    Jin M L and Dickinson R E. 1999. Interpolation of surface radiative temperature measured from polar orbiting satellites to a diurnal cycle: 1. Without clouds. Journal of Geophysical Research, 104(D2): 2105–2116
  18. 18.
    Jin M L and Treadon R.E 2003. Correcting the orbit drift effect on AVHRR land surface skin temperature measurements. International Journal of Remote Sensing, 24(22): 4543–4558
  19. 19.
    Kahle A B. 1977. A simple thermal model of the Earth’s surface for geologic mapping by remote sensing. Journal of Geophysical Research, 82(11): 1673–1680
  20. 20.
    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
  21. 21.
    Liang S L, Cheng J, Jia K, Jiang B, Liu Q, Liu S H, Xiao Z Q, Xie X H, Yao Y J, Yuan W P, Zhang X T and Zhao X. 2016. Recent progress in land surface quantitative remote sensing. Journal of Remote Sensing, 20(5): 875–898
  22. 22.
    Liu H and Cheng J. 2016. Construction of Diurnal Surface Temperature Cycle Model and Probe about the Regular Pattern of the Model in China. Beijing: Beijing Normal University: 21
  23. 23.
    Liu Z H, Wu P H, Wu Y L, Shen H F and Zeng C. 2017. Robust reconstruction of missing data in Feng Yun geostationary satellite land surface temperature products. Journal of Remote Sensing, 21(1): 40–51
  24. 24.
    Norman J M and Becker F. 1995. Terminology in thermal infrared remote sensing of natural surfaces. Remote Sensing Reviews, 12(3/4): 159–173
  25. 25.
    Parton W J and Logan J A. 1981. A model for diurnal variation in soil and air temperature. Agricultural meteorology, 23: 205–216
  26. 26.
    Price J C. 1977. Thermal inertia mapping: a new view of the earth. Journal of Geophysical Research, 82(18): 2582–2590
  27. 27.
    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
  28. 28.
    Sun D L and Pinker R T. 2005. Implementation of GOES-based land surface temperature diurnal cycle to AVHRR. International Journal of Remote Sensing, 26(18): 3975–3984
  29. 29.
    Van Den Bergh F, Van Wyk M A and Van Wyk B J. 2006. Comparison of data-driven and model-driven approaches to brightness temperature diurnal cycle interpolation//Proceedings of the 17th Annual Symposium of the Pattern Recognition Association of South Africa. Parys, South Africa: [s.n.]
  30. 30.
    Xiong Y Y and Wu X Q. 2010. The generalizing application of four judging criterions for gross errors. Physical Experiment of College, 23(1): 66–68
  31. 31.
    Xue X S and Wu Y L. 2017. A comparison of missing data reconstruction methods for Feng Yun geostationary satellite land surface temperature products. Journal of Anhui Agricultural University, 44(2): 308–315
  32. 32.
    Zhan W F, Chen Y H, Voogt J, Zhou J, Wang J F, Liu W Y and Ma W. 2012a. Interpolating diurnal surface temperatures of an urban facet using sporadic thermal observations. Building and Environment, 57: 239–252
  33. 33.
    Zhu L Q, Zhou J, Liu S M and Li G Q. 2017. Temporal normalization research of airborne land surface temperature. Journal of Remote Sensing, 21(2): 193–205

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