Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 21, 2024
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2016, Vol. 13, No. 5, pp. 277-290

Spatial resolution improvement of satellite mapping of thermal properties of land surface

S.G. Kritsuk 1 , V.I. Gornyy 1 , I.Sh. Latypov 1 
1 Saint Petersburg Scientific-Research Centre for Ecological Safety RAS, Saint Petersburg, Russia
Accepted: 13.10.2016
DOI: 10.21046/2070-7401-2016-13-5-277-290
The aim of the study is to improve the spatial resolution of maps of land surface thermal characteristics (thermal inertia, heat flux, evaporation rate etc.) compiled from satellite imagery. Thermal characteristics of land surface were retrieved from EOS images (spatial resolution ~1000 m) with the help of the deterministic algorithm. Multidimensional statistical regression models were built by using EOS images. Land surface temperature, as well as coefficients of spectral brightness were used as predictors. After that, the model was applied to Landsat 8 images (geometric resolution 30 m ÷ 100 m). The result was more detailed maps of the thermal characteristics of the land surface. The spatial resolution of final maps was assessed by comparison with the images of different spatial resolution of visible spectral band ("scale"). The maxima of mutual information from the map of thermal characteristic and elements of "scale" was used as a measure of agreement. As the result, the spatial resolution of final maps was assessed as 90 m (more than 10 times better than the original EOS images of infrared-thermal spectral band). Errors of thermal characteristic mapping by using this technique were estimated as well. The regression approach opens the possibility to apply EOS satellite infrared thermal imagery to solve ecological safety and energy saving problems of city blocks.
Keywords: Key words: satellite, infrared-thermal imagery, land surface, map, thermal characteristics, spatial resolution, regression, resolution increase
Full text

References:

  1. Gornyy V.I., Shilin B.V., Yasinskii G.I., Teplovaya aerokosmicheskaya s"emka (Thermal airborne and satellite flown survey), Moscow, Nedra, 1993, 128 p.
  2. Gornyy V.I., Kritsuk S.G., Latypov I.Sh., Tronin A.A., Shilin B.V., Distantsionnyi izmeritel'nyi monitoring teplopoter' gorodskikh i promyshlennykh aglomeratsii (na primere St-Peterburga i Khel'sinki) (Remote measuring monitoring of energy loss of urban and industrial areas (by example of Saint-Petersburg and Helsinki), Teploeffektivnye tekhnologii, Informatsionnyi byulleten', 1997, No. 2, pp.17–23.
  3. Gornyy V.I., Kosmicheskie izmeritel'nye metody infra-krasnogo teplovogo diapazona pri monitoringe potentsial'no opasnykh yavlenii i ob"ektov (Satellite based measuring remote sensing methods of infrared-thermal spectral band wile monitoring natural and technogenic hazardous events), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2004, Issue 1, pp. 10–16.
  4. Gornyy V.I., Kritsuk S.G., O vozmozhnosti kartografirovaniya fiziko-geograficheskikh zon teplovoi kosmicheskoi s"emkoi, (On possibility to map biomes by satellite based infrared thermal imagery), DAN, Vol. 411, No. 5, 2006, pp. 684–686.
  5. Gornyy V.I., Raspredelenie konvektivnogo teplovogo potoka v Belomorskom regione po dannym distantsionnogo geotermicheskogo metoda, (Distribution of convective heat flow in belomorian region according results of remote geothermal method), Prirodnaya sreda Solovetskogo arkhipelaga v usloviyakh menyayushchegosya klimata, Ekaterinburg, UrO RAN, 2007, 184 p.
  6. Gornyy V.I., Kritsuk S.G., Latypov I.Sh., Tronin A.A., Kolichestvennaya otsenka perspektiv neftegazonosnosti territorii na osnove kompleksnoi obrabotki materialov kosmicheskikh i geofizicheskikh s"emok (Quantitative estimation of hydrocarbon resources on the base of complex processing of results of satellite imageries and geophysical surveys), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2008, Issue 5, Vol. 1, pp. 349–355.
  7. Gornyy V.I., Kritsuk S.G., Latypov I.Sh., Termodinamicheskii podkhod dlya distantsionnogo kartografirovaniya narushennosti ekosistem, (Thermodynamic approach for remote mapping of ecosystem health disturbance), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2011, Vol. 8, No. 2, pp. 179–194.
  8. Gornyy V.I., Kritsuk S.G., Latypov I.Sh., Khramtsov V.N., Verifikatsiya krupnomasshtabnykh kart termodinamicheskogo indeksa narushennosti ekosistem, (Verification of large scale maps of thermodynamic index of ecosystem disturbance), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2013, Vol. 10, No. 4, pp. 201–212.
  9. Stankevich S.A., Filippovich V.E., Lubskii N.S., Krylova A.B., Kritsuk S.G., Brovkina O.V., Gornyy V.I., Tronin A.A., Interkalibratsiya metodov vosstanovleniya termodinamicheskoi temperatury poverkhnosti urbanizirovannoi territorii po materialam teplovoi kosmicheskoi s"emki, Ukrai'ns'kyj zhurnal dystancijnogo zonduvannja Zemli, 2015, No. 7, pp. 14–23.
  10. Agam N., Kustas W.P., Anderson M.C., Li F., Neale C.M., A vegetation index based technique for spatial sharpening of thermal imagery, Remote Sens. Environ, 2007, 107, pp. 545–558.
  11. Essa W., van der Kwast J., Verbeiren B., Batelaan O., Downscaling of thermal images over urban areas using the land surface temperature-impervious percentage relationship, Int. J. Appl. Earth Obs. Geoinf., 2013, Vol. 23, pp. 95–108.
  12. Jing L., Cheng Q., A technique based on non-linear transform and multivariate analysis to merge thermal infrared data and higher-resolution multispectral data, Int. J. Remote Sens., 2010, Vol. 31, pp. 6459–6471.
  13. Kustas W.P., Norman J.M., Anderson M.C., French A.N., Estimating subpixel surface temperatures and energy fluxes from the vegetation index–radiometric temperature relationship, Remote Sens. Environ., 2003, Vol. 85, pp. 429–440.
  14. Li W., Mutual Information Functions Versus Correlation Functions, Journal of Statistical Physics, 1990, Vol. 60, Issue 5–6, pp. 823–837.
  15. Rodriguez-Galiano V., Pardo-Iguzquiza E., Sanchez-Castillo M., Chica-Olmo M., Chica- Rivas M., Downscaling Landsat 7 ETM+ thermal imagery using land surface temperature and NDVI images, International Journal of Applied Earth Observation and Geoinformation, 2012, Vol. 18, pp. 515–527.
  16. da Silva B.B., Braga A.C., Braga C.C., de Oliveira L.M.M., Montenegro S.M.G.L., Barbosa J-r B., Procedures for calculation of the albedo with OLI-Landsat 8 images: Application to the Brazilian semi-arid, Revista Brasileira de Engenharia Agrícola e Ambiental, 2016, Vol. 20, No. 1, pp. 3–8.
  17. Stathopoulou M., Cartalis C., Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation, Remote Sens. Environ., 2009, Vol. 113, pp. 2592–2605.
  18. Wang F., Qin Zh., Li W., Song C., Karnieli A., Zhao Sh. An Efficient Approach for Pixel Decomposition to Increase the Spatial Resolution of Land Surface Temperature Images from MODIS Thermal Infrared Band Data, Sensors (Basel), 2015, Vol. 15 (1), pp. 304–330.
  19. Watson K., Rowan L.C., Offield T.V., Application of Thermal Modelling in Geologic Interpretation of IR Images, Proceedings of 7-th International Symposium on Remote Sensing of Environment, Ann Arbor, Michigan, 1971, pp. 2017–2041.
  20. Xue Y., Cracknell A.P., Advanced Thermal Inertia Modeling, International Journal of Remote Sensing, Vol. 16, No. 3, 1995, pp. 431–446.