سابِق|تال

Practical split-window algorithm for retrieving land surface temperature from Himawari 8 AHI data

  • role： First author第一作者
  • Affiliation：

    College of Information Engineering, China University of Geosciences, Wuhan 430074, China

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

  • Email：superlch1992@outlook.com
  • Introduction：刘超(1992—　)，男，硕士研究生，研究方向为热红外地表温度反演的理论和应用。E-mail：superlch1992@outlook.com
LIU Chao12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

  • Email：lihua@radi.ac.cn
  • Introduction：历华(1982—　)，男，副研究员，研究方向为热红外辐射传输建模、地表温度与发射率的遥感反演及验证。E-mail：lihua@radi.ac.cn
LI Hua2*,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

DU Yongming2,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

CAO Biao2,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

    Joint Center for Global Change Studies (JCGCS), Beijing 100875, China

LIU Qinhuo23,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing 100875, China

MENG Xiangchen4,  
  • Affiliation：

    College of Information Engineering, China University of Geosciences, Wuhan 430074, China

HU Youjian1

ملخص

Land Surface Temperature (LST) is a key parameter for hydrological, meteorological, climatological, and environmental research fields. Accurate regional and global LST products can be obtained from thermal infrared remote sensing data. Himawari-8 is the next-generation of Japan geostationary meteorological satellite, which carries a new optical sensor called Advanced Himawari Imager (AHI), with significantly higher temporal and spatial resolutions. AHI has 16 observation bands, with spatial resolutions of 0.5 or 1 km for visible and near-infrared bands and 2 km for infrared bands. AHI can provide full disk images every 10 minutes, and can provide high temporal and spatial resolution LST information for many studies. The bands 14 and 15 of AHI can be used for LST retrieval by using the Split-Window (SW) algorithm. Thus, the objective of this paper is to propose a practical SW algorithm to retrieve LST from AHI data. Land Surface Emissivity (LSE) is one of the essential parameters for SW algorithm. SW algorithm is extremely sensitive to emissivity errors, and the sensitivity is significantly higher for direr atmospheres. A 0.005 error in emissivity will result in a LST error of 1 K or more under drier conditions. The ASTER Global Emissivity Dataset (GED) version 4 was adopted to calculate the LSE in this paper to improve the accuracy of emissivity in barren surfaces. The refined Generalized Split-Window (GSW) algorithm developed for MODIS was adopted to retrieve LST from the brightness temperature of AHI bands 14 and 15. MODTRAN 5.2, TIGR 3 atmospheric profile database, and ASTER spectral library data were used to create a simulation database to obtain the coefficients of the GSW algorithm. The coefficients were determined based on view zenith angle and atmospheric Water Vapor (WV) sub-ranges to improve the accuracy, and the WV was directly calculated using a simple method based on the brightness temperature of AHI bands 14 and 15. Two kinds of emissivity products were used to calculate LSE for AHI bands 14 and 15. The first product is the ASTER GED version 4 monthly product. The second is the MODIS MOD11C3 version 6 monthly emissivity product. The spatial resolution of the two products is 0.05°. Ground measurements collected from three Aerosol Robotic NETwork (AERONET) sites were used to validate the WV result. The root mean square errors (RMSEs) were 1.16 g/cm2, 0.94 g/cm2, and 1.23 g/cm2 for Baotou, Beijing-CAMS, and Hong_Kong_PolyU sites, respectively. The 2079 daytime and 2983 nighttime scenes of AHI images between June 1, 2015 and December 29, 2015 were used for LST retrieval. Ground LST measurements collected from four Heihe Watershed Allied Telemetry Experimental Research (HiWATER) sites and MODIS LST product data of the central points of seven lakes were used to validate the LST. The results show that the proposed algorithm demonstrates a reasonable accuracy, with RMSE less than 3 K, which has a comparable accuracy of current remote sensing LST products, such as MODIS, VIIRS, and FY-3B VIRR LST products. For LSE, ASTER GED v4 provides more realistic values of surface emissivity than MOD11C3 v6 because the emissivities are in accordance with the seasonal variation on NDVI. The MOD11C3 v6 typically provides constant values of emissivity, which was overestimated over the JCHM site. Thus, the LST was underestimated due to the overestimation of the emissivity. A practical SW algorithm for estimating land surface temperature from Himawari 8 AHI data was proposed based on GSW algorithm. ASTER GED v4 product was introduced to estimate the LSE for GSW algorithm. The LST result was evaluated with ground LST measurements collected in four HiWATER sites and the MODIS LST products, with RMSE of less than 3 K. The results also show that ASTER GED v4 product has higher accuracy than MOD11C3 v6 product in our study sites; thus, is more suitable in generating high accuracy LST product.

مفهوم

land surface temperature;split-window algorithm;AHI;ASTER GED;atmospheric water vapor

References

  1. 1.
    Anderson M C, Norman J M, Diak G R, Kustas W P and Mecikalski J R. 1997. A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing. Remote Sensing of Environment, 60(2): 195–216
  2. 2.
    Becker F and Li Z L. 1990. Towards a local split window method over land surfaces. International Journal of Remote Sensing, 11(3): 369–393
  3. 3.
    Bessho K, Date K, Hayashi M, Ikeda A, Imai T, Inoue H, Kumagai Y, Miyakawa T, Murata H, Ohno T, Okuyama A, Oyama R, Sasaki Y, Shimazu Y, Shimoji K, Sumida Y, Suzuki M, Taniguchi H, Tsuchiyama H, Uesawa D, Yokota H and Yoshida R. 2016. An introduction to Himawari-8/9—Japan’s new-generation geostationary meteorological satellites. Journal of the Meteorological Society of Japan, 94(2): 151–183
  4. 4.
    Cheng J, Liang S L, Yao Y J and Zhang X T. 2013. Estimating the optimal broadband emissivity spectral range for calculating surface longwave net radiation. IEEE Geoscience and Remote Sensing Letters, 10(2): 401–405
  5. 5.
    Galve J M, Coll C, Caselles V and Valor E. 2008. An atmospheric radiosounding database for generating land surface temperature algorithms. IEEE Transactions on Geoscience and Remote Sensing, 46(5): 1547–1557
  6. 6.
    Gillespie A, Rokugawa S, Matsunaga T, Cothern J S, Hook S and Kahle A B. 1998. A temperature and emissivity separation algorithm for advanced spaceborne thermal emission and reflection radiometer (ASTER) images. IEEE Transactions on Geoscience and Remote Sensing, 36(4): 1113–1126
  7. 7.
    Guillevic P C, Biard J C, Hulley G C, Privette J L, Hook S J, Olioso A, Göttsche F M, Radocinski R, Román M O, Yu Y Y and Csiszar I. 2014. Validation of land surface temperature products derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) using ground-based and heritage satellite measurements. Remote Sensing of Environment, 154: 19–37
  8. 8.
    Hulley G, Veraverbeke S and Hook S. 2014. Thermal-based techniques for land cover change detection using a new dynamic MODIS multispectral emissivity product (MOD21). Remote Sensing of Environment, 140: 755–765
  9. 9.
    Hulley G C, Hook S J and Baldridge A M. 2009. Validation of the north American ASTER land surface emissivity database (NAALSED) version 2. 0 using pseudo-invariant sand dune sites. Remote Sensing of Environment, 113(10): 2224–2233
  10. 10.
    Hulley G C, Hook S J, Abbott E, Malakar N, Islam T and Abrams M. 2015. The ASTER global emissivity dataset (ASTER GED): mapping earth’s emissivity at 100 meter spatial scale. Geophysical Research Letters, 42(19): 7966–7976
  11. 11.
    Jiang J X, Li H, Liu Q H, Wang H S, Du Y M, Cao B, Zhong B and Wu S L. 2015. Evaluation of land surface temperature retrieval from FY-3B/VIRR data in an arid area of Northwestern China. Remote Sensing, 7(6): 7080–7104
  12. 12.
    Julien Y, Sobrino J A, Mattar C and Jiménez-Muñoz J C. 2015. Near-real-time estimation of water vapor column from MSG-SEVIRI thermal infrared bands: implications for land surface temperature retrieval. IEEE Transactions on Geoscience and Remote Sensing, 53(8): 4231–4237
  13. 13.
    Kondratiev K Y A. 1969. Radiation in the Atmosphere vol. 12. New York: Academic Press: 911
  14. 14.
    Li H, Du Y M, Liu Q H, Xu D Q, Cao B, Jiang J X and Wang H S. 2014. Land surface temperature retrieval from Tiangong-1 data and its applications in urban heat island effect. Journal of Remote Sensing, 18(S1): 133–143
  15. 15.
    Li H, Sun D L, Yu Y Y, Wang H Y, Liu Y L, Liu Q H, Du Y M, Wang H S and Cao B. 2014. Evaluation of the VIIRS and MODIS LST products in an arid area of Northwest China. Remote Sensing of Environment, 142: 111–121
  16. 16.
    Li X, Cheng G D, Liu S M, Xiao Q, Ma M G, Jin R, Che T, Liu Q H, Wang W Z, Qi Y, Wen J G, Li H Y, Zhu G F, Guo J W, Ran Y H, Wang S G, Zhu Z L, Zhou J, Hu X L and Xu Z W. 2013a. Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design. Bulletin of the American Meteorological Society, 94(8): 1145–1160
  17. 17.
    Li Z L, Tang B H, Wu H, Ren H Z, Yan G J, Wan Z M, Trigo I F and Sobrino J A. 2013b. Satellite-derived land surface temperature: current status and perspectives. Remote Sensing of Environment, 131: 14–37
  18. 18.
    Meng X C, Li H, Du Y M, Cao B, Liu Q H, Sun L and Zhu J S. 2016. Estimating land surface emissivity from ASTER GED products. Journal of Remote Sensing, 20(3): 382–396
  19. 19.
    Ren H Z, Du C, Liu R Y, Qin Q M, Yan G J, Li Z L and Meng J J. 2015. Atmospheric water vapor retrieval from Landsat 8 thermal infrared images. Journal of Geophysical Research: Atmospheres, 120(5): 1723–1738
  20. 20.
    Snyder W C, Wan Z, Zhang Y and Feng Y. 1998. Classification-based emissivity for land surface temperature measurement from space. International Journal of Remote Sensing, 19(14): 2753–2774
  21. 21.
    Sobrino J A, Jiménez-Muñoz J C, Sòria G, Ruescas A B, Danne O, Brockmann C, Ghent D, Remedios J, North P, Merchant C, Berger M, Mathieu P P, Göttsche F M. 2016. Synergistic use of MERIS and AATSR as a proxy for estimating Land Surface Temperature from Sentinel-3 data. Remote Sensing of Environment, 179: 149–161
  22. 22.
    Sobrino J A and Raissouni N. 2000. Toward remote sensing methods for land cover dynamic monitoring: application to Morocco. International Journal of Remote Sensing, 21(2): 353–366
  23. 23.
    Sun D L and Pinker R T. 2003. Estimation of land surface temperature from a geostationary operational environmental satellite (GOES-8). Journal of Geophysical Research: Atmospheres, 108(D11): 4326
  24. 24.
    Tang B H, Bi Y Y, Li Z L and Xia J. 2008. Generalized split-window algorithm for estimate of land surface temperature from Chinese geostationary FengYun meteorological satellite (FY-2C) data. Sensors, 8(2): 933–951
  25. 25.
    Trigo I F, Monteiro I T, Olesen F and Kabsch E. 2008. An assessment of remotely sensed land surface temperature. Journal of Geophysical Research: Atmosphere, 113(D17): D17108
  26. 26.
    Valor E and Caselles V. 1996. Mapping land surface emissivity from NDVI: application to European, African, and South American areas. Remote Sensing of Environment, 57(3): 167–184
  27. 27.
    Wan Z, Li Z L. 1997. A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 35(4): 980–996
  28. 28.
    Wan Z M. 2013. New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sensing of Environment, 140: 36–45
  29. 29.
    Wan Z M and Dozier J. 1996. A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Transactions on Geoscience and Remote Sensing, 34(4): 892–905
  30. 30.
    Wan Z M, Zhang Y L, Zhang Q C and Li Z L. 2002. Validation of the land-surface temperature products retrieved from terra moderate resolution imaging Spectroradiometer data. Remote Sensing of Environment, 83(1/2): 163–180
  31. 31.
    Yu Y Y, Privette J L and Pinheiro A C. 2008. Evaluation of split-window land surface temperature algorithms for generating climate data records. IEEE Transactions on Geoscience and Remote Sensing, 46(1): 179–192

قراءة النص الكامل

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