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Optimized AVHRR land surface temperature downscaling method for local scale observations: case study for the coastal area of the Gulf of Gdańsk

Abstract

Satellite imaging systems have known limitations regarding their spatial and temporal resolution. The approaches based on subpixel mapping of the Earth’s environment, which rely on combining the data retrieved from sensors of higher temporal and lower spatial resolution with the data characterized by lower temporal but higher spatial resolution, are of considerable interest. The paper presents the downscaling process of the land surface temperature (LST) derived from low resolution imagery acquired by the Advanced Very High Resolution Radiometer (AVHRR), using the inverse technique. The effective emissivity derived from another data source is used as a quantity describing thermal properties of the terrain in higher resolution, and allows the downsampling of low spatial resolution LST images. The authors propose an optimized downscaling method formulated as the inverse problem and show that the proposed approach yields better results than the use of other downsampling methods. The proposed method aims to find estimation of high spatial resolution LST data by minimizing the global error of the downscaling. In particular, for the investigated region of the Gulf of Gdansk, the RMSE between the AVHRR image downscaled by the proposed method and the Landsat 8 LST reference image was 2.255°C with correlation coefficient R equal to 0.828 and Bias = 0.557°C. For comparison, using the PBIM method, it was obtained RMSE = 2.832°C, R = 0.775 and Bias = 0.997°C for the same satellite scene. It also has been shown that the obtained results are also good in local scale and can be used for areas much smaller than the entire satellite imagery scene, depicting diverse biophysical conditions. Specifically, for the analyzed set of small sub-datasets of the whole scene, the obtained RSME between the downscaled and reference image was smaller, by approx. 0.53°C on average, in the case of applying the proposed method than in the case of using the PBIM method.

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Category:
Articles
Type:
artykuły w czasopismach recenzowanych i innych wydawnictwach ciągłych
Published in:
Open Geosciences no. 9, edition 1, pages 419 - 435,
ISSN: 2391-5447
Language:
English
Publication year:
2017
Bibliographic description:
Chybicki A., Łubniewski Z.: Optimized AVHRR land surface temperature downscaling method for local scale observations: case study for the coastal area of the Gulf of Gdańsk// Open Geosciences. -Vol. 9., iss. 1 (2017), s.419-435
DOI:
Digital Object Identifier (open in new tab) 10.1515/geo-2017-0032
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  1. Arnfield A. J., Two decades of urban climate research: A review of turbulence, exchanges of energy and water, and the urban heat 30 island. International Journal of Climatology, 2003, 23, 1-26, DOI: 10.1002/joc.859 open in new tab
  2. Urbański J. A., Wochna A., Bubak I., Grzybowski W., Łukawska- Matuszewska K., Lacka M. et al., Application of Landsat 8 im- agery to regional-scale assessment of lake water quality. Inter- 35 national Journal of Applied Earth Observation and Geoinforma- tion, 2016, 51, 28-36, DOI: 10.1016/j.jag.2016.04.004 open in new tab
  3. Chybicki A., Łubniewski Z., Kulawiak M., Characterizing surface and air temperature in the Balitc Sea Coastal Area using remote sensing techniques. Polish Maritime Research, 2016, .3, 3-11, 40 2016, DOI: 10.1515/pomr-2016-0001 open in new tab
  4. Merchant C. J., Matthiesen S., Rayner N. A, Remedios J. J., Jones P. D., Olesen F. et al., The surface temperatures of the earth: steps towards integrated understanding of variability and change, Geosci. Instrumentation Methods Data Syst. Dis- 45 cuss. 2013, 3, 305-345, DOI: 10.5194/gid-3-305-2013 open in new tab
  5. Kustas W. P., Anderson M. C., Norman J. N., French A. N., Esti- mating subpixel surface temperatures and energy fluxes from the vegetation index-radiometric temperature relationship. Re- mote Sensing of Environment, 2003, 85, 429-440 open in new tab
  6. Yang G., Puc R., Zhaoa C., Huanga W., Wanga J., Estimation of subpixel land surface temperature using an endmember index based technique: A case examination on ASTER and MODIS temperature products over a heterogeneous area. Re- mote Sensing of Environment, 2011, 115(5), 15, 1202-1219, DOI: 55 10.1016/j.rse.2011.01.004 open in new tab
  7. Stathopoulou M., Cartalis C., Keramitsoglou I., Mapping micro- urban heat islands using NOAA/AVHRR images and CORINE Land Cover: An application to coastal cities of Greece. Interna- tional Journal of Remote Sensing, 2004, 25, 2301-2316, DOI: 60 10.1080/01431160310001618725 open in new tab
  8. Mitraka Z., Chrysoulakis N., Doxani G., Del Frate F., Berger M., Urban Surface Temperature Time Series Estimation at the Local Scale by Spatial-Spectral Unmixing of Satellite Observations. Remote Sens., 2015, 7(4), 4139-4156, DOI: 65 10.3390/rs70404139 open in new tab
  9. Bechtel B., Zakšek K., Hoshyaripour G., Downscaling land sur- face temperature in an urban area: A case study for Ham- burg, Germany. Remote Sens., 2012, 4, 3184-3200, DOI: 10.3390/rs4103184 70 open in new tab
  10. Keramitsoglou I., Kiranoudis C. T., Weng Q. Downscaling geo- stationary land surface temperature imagery for urban analysis. IEEE Geosci. Remote Sens. Lett. 2013, 10, 1253-1257 open in new tab
  11. Lewis H. G, Nixon M.S., Tatnall A. R. L., Brown M., Appropriate strategies for mapping land cover from satellite imagery, Pro-75 ceedings of the 25th Annual Conference of The Remote Sensing Society, Cardiff, United Kingdom, 1999, pp. 717-724.
  12. Lewis H. G., Brown M., Tatnall A. R. L., Incorporating uncertainty in land cover classification from remote sensing imagery. Ad- vanced Space Research A.R.L., 2000, 26(7), 1123-1126 80 open in new tab
  13. Seetle J. J., Drake N. A., Linear mixing and the estimation of ground cover proportions. International Journal of Remote Sensing, 1997, 14(6), 1559-1177 open in new tab
  14. Williamson H. D., Estimating sub-pixel components of a semi- arid woodland. International Journal of Remote Sensing, 1994, 85 15, 3303-3307 open in new tab
  15. Atkinson P. M., Cutler M. E. J., Lewis H., Mapping sub-pixel pro- portional land cover with AVHRR imagery. International Journal of Remote Sensing, 1997, 188, 917-935 open in new tab
  16. Mather P. M., Tso B., Classification Methods for Remotely 90 Sensed Data. Taylor and Francis, 2001
  17. Fisher P. F., Pathirana S., The evaluation of fuzzy memberships of land cover classes in the suburban zone. Remote Sensing of Environment, 1990, 34, 121-132 open in new tab
  18. Foody G. M., Approaches for the production and evaluation of 95 fuzzy land cover classifications from remotely-sensed data. In- ternational Journal of Remote Sensing, 1996, 17, 1317-1340 open in new tab
  19. Xiong Xu, Yanfei Zhong, Adaptive Subpixel Mapping Based on a Multiagent System for Remote-Sensing Imagery. IEEE Transac- tions on Geoscience and Remote Sensing, 2014, 52, 2, 787-804 100 open in new tab
  20. Guo L. J., Moore J. M., Pixel block intensity modulation: Adding spatial detail to TM band 6 thermal imagery. International Jour- nal of Remote Sensing, 1998, 19(13), 2477-2491 open in new tab
  21. Jiang L. S., Islam S., A methodology for estimation of sur- face evapotranspiration over large areas using remote sens-105 ing observations. Geophysical Research Letters, 1999, 26, DOI: 10.1029/1999GL006049 open in new tab
  22. Jiang L. S., Islam S., Estimation of surface evaporation map over southern Great Plains using remote sensing data. Water Re- sources Research, 2001, 37, DOI: 10.1029/2000WR900255 110 open in new tab
  23. Valipour M., Optimization of neural networks for precipitation analysis in a humid region to detect drought and wet year alarms. Meteorological Applications, 2016, 23(1), 91-100 open in new tab
  24. Liang S., Validation and spatial scaling. In: Kong J. (Ed.), Quan- titative Remote Sensing of Land Surfaces. Wiley & Sons, New Jersey, 2004, 431-471 open in new tab
  25. Munechika C. K., Warnick J. S., Salvaggio C., Schott J. R., Resolu- tion enhancement of multispectral image data to improve clas- sification accuracy. Photogrammetric Engineering and Remote Sensing, 1993, 59, 67-72
  26. Petropoulos G., Carlson T., Wooster M., Islam S., A Review of 10 open in new tab
  27. Ts/VI Remote Sensing Based Methods for the Retrieval of Land Surface Fluxes and Soil Surface Moisture Content. Progress in Physical Geography, 2009, 33(2), 224-250
  28. Stathopoulou M., Cartalis C., Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity 15 estimation. Remote Sensing of Environment, 2009, 113, 2592- 2605 open in new tab
  29. Liu Y., Hiyama T., Yamaguchi Y., Scaling of land surface temper- ature using satellite data: A case examination on ASTER and MODIS products over a heterogeneous terrain area. Remote 20 Sensing of Environment, 2006, 105, 115-128 open in new tab
  30. Valipour M., Mohammad Ali Gholami Sefidkouhi, Mahmoud Raeini-Sarjaz, Selecting the best model to estimate potential evapotranspiration with respect to climate change and magni- tudes of extreme events. Agricultural Water Management, 2017, 25 180(A), 50-60, http://dx.doi.org/10.1016/j.agwat.2016.08.025 open in new tab
  31. Jiménez-Muñoz J. C., Sobrino J. A., A generalized single-channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research, 2003, 108( D22) open in new tab
  32. Hoerl A. E., Kennard R. W., Ridge regression: Biased estimation 30 for nonorthogonal problems. Technometrics, 1992, 12, 55-67 open in new tab
  33. Knight J. F., Lunetta R. S., Ediriwickrema J., Khorram S., Re- gional scale land cover characterization using MODIS-NDVI 250 m multi-temporal imagery: a phenology-based approach. GI- Science Remote Sens., 2006, 43, 1-23 open in new tab
  34. Stisen S., Sandholt I., Nørgaard A., Fensholt R., Jensen K. H., Combining the triangle method with thermal inertia to estimate regional evapotranspiration -Applied to MSG-SEVIRI data in the Senegal River basin. Remote Sensing of Environment, 2008, 112 (3), 1242-1255 open in new tab
  35. O'Leary D., Near optimal parameters for Tikhonov regularization and other regularization methods. SIAM Journal on Scientific Computing, 2001, 23(4), 1161-1171 open in new tab
  36. Morozov V. A., On the solution of functional equations by the method of regularization. Soviet Math. Dokl., 1996, 7, 414-417 45 open in new tab
  37. Golub G., Heath M., Wahba G., Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics, 1979, 21, 215-223 open in new tab
  38. Hansen P. C., Analysis of discrete ill-posed problems by means of the L-curve. SIAM Rev., 1992, 34, 561-580 open in new tab
  39. Moodey A. J. F., Lawless A. S., Potthast R. W. E., P. Van Leeuwen J., Nonlinear error dynamics for cycled data assimilation. Report no. MPS-2012-06, 2013 open in new tab
  40. Li Z.L., Wu H., Wang N., Qiu S., Sobrino J. A., Wan Z. et al., Land surface emissivity retrieval from satellite data, Int. J. Remote 55 Sens., 2012, 34, 3084-3127 open in new tab
  41. Sobrino J. A., Coll C., Caselles V., Atmospheric correction for land surface temperature using NOAA-11 AVHRR channels 4 and 5. Remote Sensing of Environment, 1991, 38, 19-34 open in new tab
  42. Sobrino J. A., Li Z.L., Stoll P., Impact of the atmospheric trans- 60 mittance and total water vapor content in the algorithms for es- timating satellite sea surface temperatures. IEEE Transactions on Geoscience and Remote Sensing, 1993, 31, 946-952 open in new tab
  43. Sobrino J. A., Li, M Z. L., Stoll P., Becker F., Improvements in the split-window technique for land surface temperature determi-65 nation. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32, 243-253 open in new tab
  44. U.S. Geological Survey, The Landsat 8 Data User's Handbook, http://landsat.usgs.gov/l8handbook.php (access on January 2016) 70 open in new tab
  45. Schott J. R., Volchok W. J., Thematic Mapper thermal infrared cal- ibration. Photogrammetric Engineering and Remote Sensing, 1985, 51, 1351-1357
  46. Wukelic G. E., Gibbons D.E., Martucci L. M., Foote H. P., Radio- metric calibration of Landsat Thermatic Mapper Thermal Band. 75 open in new tab
  47. Remote Sensing of Environment, 1989, 28, 339-347 open in new tab
  48. Skokovic D., Sobrino J. A., Jimenez-Munoz J. C., Soria G., Julien Y., Mattar C. et al., Calibration and Validation of Land Surface Temperature for Landsat 8 -TIRS Sensor. In: Land Product Val- idation and Evolution, ESA/ESRIN, Frascati (Italy), 2014, 6-9 80
  49. Shaohua Zhao, Qiming Qin, Yonghui Yang, Yujiu Xiong and Guoyu Qiu, Comparison of two Split-Window Methods for Re- trieving Land Surface Temperature from MODIS Data. Journal of Earth Syst. Science, 2009, 118, 4, 345-353 open in new tab
  50. Jimenez-Munoz J. C., Sobrino J. A., Split-window Coeflcients for 85 Land Surface Temperature Retrieval from Low-Resolution Ther- mal Infrared Sensors. IEEE Geoscience and Remote Sensing Let- ters, 2008, 5(4), 806-809 open in new tab
  51. Jiménez-Muñoz J. C., Sobrino J. A., Skokovic D., Mattar C., Cristóbal J., Land Surface Temperature Retrieval Methods from 90
  52. Landsat-8 Thermal Infrared Sensor Data. IEEE Geoscience and Remote Sensing Letters, 2014, 11, 1840-1843 open in new tab
  53. Shahid Latif M. D., Land Surface Temperature Retrival of Landsat-8 Data Using Split Window Algorithm -A Case Study of Ranchi District. International Journal of Engineering Develop-95 ment and Research, 2014, 2(4), 3840-3849
  54. Jiménez-Muñoz J.C., Sobrino J.A., A generalized single-channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research, 2008, 108, 4688-4695 100 open in new tab
  55. Dubovik O., Holben B. N., Eck T .F., Smirnov A., Kaufman Y. J., King M. D. et al., Variability of absorption and optical prop- erties of key aerosol types observed in worldwide locations. J.Atm.Sci., 2002, 59, 590-608 open in new tab
  56. Aires F., Chédin A., Scott N. A., Rossow W. B., A regularized neu-105 ral net approach for retrieval of atmospheric and surface tem- peratures with the IASI instrument. J. Appl. Meteorol., 2002, 41, 2, 144-159 open in new tab
  57. Sobrino J. A., Raissouni N., Simarro J., Nerry F., Petitcolin F., Atmospheric Water Vapor Content over Land Surfaces Derived 110 from the AVHRR Data: Application to the Iberian Peninsula. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37, 3, 1425-1434 open in new tab
  58. Michalakes J., Dudhia J., Gill D., Henderson T., Klemp J., Skam- rock W. et al., The Weather Research and Forecast Model: Soft-115 ware Architecture and Performance. In: Mozdzynski G. (Ed.), Proceedings of the 11 th ECMWF Workshop on the Use of High Performance Computing in Meteorology, Reading, 2004 open in new tab
  59. Źróbek-Sokolnik A., Dynowski P., Stańczuk-Gałwiaczek M., Kryszk H., Kurowska K., Dudzińska M. et al., Application of ge-120
  60. ographic information system tools in a broad natural science: Estimation and evaluation of Earth surface characteristics using numerical weather prediction and satellite imagery. Nacjonalna Knijznica, Zagreb, 2014, p.115 open in new tab
  61. Choudhury B. J., Ahmed N. U., Idso S. B., Reginato R. J., Daughtry 5 C. S. T., Relations between evaporation coeflcients and vegeta- tion indices studied by model simulations. Remote Sensing of Environment, 1994, 50, 1-17 open in new tab
  62. Gillies R. R., Carlson T. N., Thermal remote sensing of surface soil water content with partial vegetation cover for incorpora- 10 tion into climate models. Journal of Applied Meteorology, 1995, 34, 45-756 open in new tab
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