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, 2025, Vol. 22, No. 1, pp. 9-25

Validation of land surface temperature values calculated from TIRS/Landsat-8 radiometer data and their use for temperature regime analysis of irrigated and post-irrigated soils of Chui intermountain basin (Altai Republic)

E.A. Mamash 1 , I.A. Pestunov 1, 2 , S.Ya. Kudryashova 3 , A.S. Chumbaev 3 
1 Federal Research Center for Information and Computational Technologies, Novosibirsk, Russia
2 Institute of Automation and Electrometry SB RAS, Novosibirsk, Russia
3 Institute of Soil Science and Agrochemistry SB RAS, Novosibirsk, Russia
Accepted: 21.11.2024
DOI: 10.21046/2070-7401-2025-22-1-9-25
This work is devoted to the validation of land surface temperature (LST) values calculated using various LST restoring algorithms based on the TIRS/Landsat-8 radiometer data by comparing them with the ground-based measurements. A modification of an existing LST calculation algorithm is proposed with the help of an alternative method of emission coefficient calculation. It is shown that the proposed algorithm provides the best agreement with the ground-based measurements. Analysis of the long-term dynamics of the temperature regime has been performed for irrigated and post-irrigated soils of the Chui intermountain basin (Altai Republic) using the Landsat data and the proposed modification of the algorithm. Cartograms of the LST values distribution have been developed for key areas of the Chui basin using the multi-temporal Landsat images since 1989 till 2022. A significant correlation has been revealed between the LST values and the values of the normalized difference moisture index (NDMI), and the normalized difference vegetation index (NDVI) for the studied irrigated areas of the Chui basin. The maximum values of the correlation coefficients are 0.87 for the NDMI moisture index, as well as for the NDVI vegetation index, and fall in the period when the irrigation systems operate at full capacity. It is revealed that if the irrigation systems operate, this leads to a significant change in distribution of the LST values both for the entire basin of Chui and for the key areas of the irrigation zone.
Keywords: validation, land surface temperature, LST, Landsat, soil temperature regime, irrigation systems, Chui intermountain basin
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References:

  1. Varentsov M. I., Grischenko M. Yu., Konstantinov P. I., Comparison between in situ and satellite multiscale temperature data for Russian Arctic cities for winter season, Issledovanie Zemli iz kosmosa, 2021, No. 2, pp. 64–76 (in Russian), DOI: 10.31857/S0205961421020093.
  2. Volobuev V. R., Vvedenie v energetiku pochvoobrazovaniya (Introduction to the energetics of soil formation), Moscow: Nauka, 1974, 128 p. (in Russian).
  3. Voropai N. N., Istomina E. A., Vasilenko O. V., Investigation of temperature field of land surface of Tunkinskaya bolson using Landsat space images, Optika Atmosfery i Okeana, 2011, V. 24, No. 1, pp. 67–73 (in Russian).
  4. Gornyy V. I., Kritsuk S. G., Latypov I. Sh. et al., Thermophysical properties of land surface in urban area (by satellite remote sensing of Saint Petersburg and Kiev), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, V. 14, No. 3, pp. 51–66 (in Russian), DOI: 10.21046/2070-7401-2017-14-3-51-66.
  5. Grischchenko M. Yu., Mikhaylyukova P. G., Comparing ground-based and satellite data to study the spatial variability of the natural area’s thermal field (case of Kunashir island, Great Kuril ridge, Sakhalin oblast, RF), Geodesy and Cartography, 2022, V. 83, No. 3, pp. 35–43 (in Russian), DOI: 10.22389/0016-7126-2022-981-3-35-43.
  6. Istomina E. A., Vasilenko O. V., Analysis of the temperature field of landscapes of the Tunka Basin using Landsat satellite images and ground data, Geografiya i prirodnye resursy, 2015, No. 4, pp. 162–170 (in Russian).
  7. Krechetova I. M., Medvedeva L. N., Development of land reclamation for forage production in the Altai Republic, Oroshaemoe zemledelie, 2020, No. 3, pp. 33–36 (in Russian), DOI: 10.35809/2618-8279-2020-3-6.
  8. Mamash E. A., Pestunov I. A., Sinyavskiy Yu. N., Analysis of patterns in the distribution of the temperature fields for large industrial cities of Siberia according to Landsat-8 data, Computational Technologies, 2022, V. 27, No. 3, pp. 95–111 (in Russian), DOI: 10.25743/ICT.2022.27.3.008.
  9. Muzylev E. L., Startseva Z. P., Uspensky A. B. et al., Using remote sensing data in modeling water and thermal regimes of rural areas, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, V. 14, No. 6, pp. 108–136 (in Russian), DOI: 10.21046/2070-7401-2017-14-6-108-136.
  10. Ponomareva T. V., Ponomarev E. I., Litvintsev K. Y. et al., Thermal state of disturbed soils in the permafrost zone of Siberia according the remote data and numerical simulation, Vychislitel’nye tekhnologii, 2022, V. 14, No. 3, pp. 16–35 (in Russian), DOI: 10.25743/ICT.2022.27.3.003.
  11. Trinh L. H., Zablotskii V. R., Dao K. H., A study of the long-term dynamics of soil moisture in the Bac Binh district (Binh Thuan province, Vietnam) using Landsat multispectral images, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, V. 15, No. 7, pp. 89–101 (in Russian), DOI: 10.21046/2070-7401-2018-15-7-89-101.
  12. Chichulin A. V., The possibility of physico-theoretical methods in soil ecology (the case of modeling soil-climatic areas structure), The J. Soils and Environment, 2023, V. 6, No. 4, pp. 229–248 (in Russian), DOI: 10.31251/pos.v6i4.229.
  13. Anderson M. C., Allen R. G., Morse A., Kustas W. P., Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources, Remote Sensing of Environment, 2012, V. 122, pp. 50–65, DOI: 10.1016/j.rse.2011.08.025.
  14. Berk A., Conforti P., Kennett R. et al., MODTRAN6®: A major upgrade of the MODTRAN® radiative transfer code, Proc. 6 th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2014, DOI: 10.1109/WHISPERS.2014.8077573.
  15. Bhattacharya S., Halder S., Nag S. et al., Assessment of drought using multi-parameter indices, In: Advances in Water Resources Management for Sustainable Use, Singapore: Springer, 2021, pp. 243–255, DOI: 10.1007/978-981-33-6412-7_18.
  16. Bogdan E., Kamalova R., Suleymanov A. et al., Changing climatic indicators and mapping of soil temperature using Landsat data in the Yangan-Tau UNESCO global geopark, SOCAR Proc., 2022, pp. 32–41, DOI: 10.5510/OGP2022SI200768.
  17. Brabyn L., Zawar-Reza P., Stichbury G. et al., Accuracy assessment of land surface temperature retrievals from Landsat 7 ETM+ in the Dry Valleys of Antarctica using iButton temperature loggers and weather station data, Environmental Monitoring Assessment, 2014, V. 186, pp. 2619–2628, DOI: 10.1007/s10661-013-3565-9.
  18. Cook M., Schott J. R., Mandel J., Raqueno N., Development of an operational calibration methodology for the Landsat thermal data archive and initial testing of the atmospheric compensation component of a Land Surface Temperature (LST) product from the archive, Remote Sensing, 2014, V. 6, No. 11, pp. 11244–11266, DOI: 10.3390/rs61111244.
  19. Dash P., Göttsche F., Olesen F., Fischer H., Separating surface emissivity and temperature using two-channel spectral indices and emissivity composites and comparison with a vegetation fraction method, Remote Sensing of Environment, 2005, V. 96, pp. 1–17, DOI: 10.1016/j.rse.2004.12.023.
  20. Davies P., Gather U., The identification of multiple outliers, J. American Statistical Association, 1993, V. 88, No. 423, pp. 782–792, https://doi.org/10.2307/2290763.
  21. Duguay-Tetzlaff A., Bento V. A., Göttsche F. M. et al., Meteosat land surface temperature climate data record: achievable accuracy and potential uncertainties, Remote Sensing, 2015, V. 7, No. 10, pp. 13139–13156, DOI: 10.3390/rs71013139.
  22. Dyba K., Ermida S., Ptak M. et al., Evaluation of methods for estimating lake surface water temperature using Landsat 8, Remote Sensing, 2022, V. 14, No. 15, Article 3839, 21 p., DOI: 10.3390/rs14153839.
  23. Ermida S., Soares P., Mantas V. et al., Google Earth Engine open-source code for land surface temperature estimation from the Landsat series, Remote Sensing, 2020, V. 12, No. 9, Article 1471, 21 p., DOI: 10.3390/rs12091471.
  24. Galve J. M., Sánchez J. M., García-Santos V. et al., Assessment of land surface temperature estimates from Landsat 8-TIRS in a high-contrast semiarid agroecosystem: Algorithms intercomparison, Remote Sensing, 2022, V. 14, No. 8, Article 1843, 22 p., DOI: 10.3390/rs14081843.
  25. Ghasempour F., Sekertekin A., Kutoglu H., How Landsat 9 is superior to Landsat 8: comparative assessment of land use land cover classification and land surface temperature, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2023, V. X-4/W1-2022, pp. 221–227, DOI: 10.5194/isprs-annals-X-4-W1-2022-221-2023.
  26. Hulley G., Hook S., Abbott E. et al., The ASTER Global Emissivity Database (ASTER GED): Mapping Earth’s emissivity at 100 meter spatial resolution, Geophysical Research Letters, 2015, V. 42, pp. 7966–7976, DOI: 10.1002/2015GL065564.
  27. Jiménez-Muñoz J., Sobrino J., Plaza A. et al., Comparison between fractional vegetation cover retrievals from vegetation indices and spectral mixture analysis: Case study of PROBA/CHRIS data over an agricultural area, Sensors, 2009, V. 9, pp. 768–793, DOI: org/10.3390/s90200768.
  28. Jiménez-Muñoz J. C., Sobrino J. A., Skoković D. et al., Land surface temperature retrieval methods from Landsat-8 Thermal Infrared Sensor data, IEEE Geoscience and Remote Sensing Letters, 2014, V. 11, No. 10, pp. 1840–1843, DOI: 10.1109/LGRS.2014.2312032.
  29. Kalnay E., Kanamitsu M., Kistler R. et al., The NCEP/NCAR 40-year reanalysis project, Bull. American Meteorological Soc. (BAMS), 1996, V. 77, Iss. 3, pp. 437–471, DOI: 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
  30. Koenig L., Hall D., Comparison of satellite, thermochron and air temperatures at Summit, Greenland, during the winter of 2008/09, J. Glaciology, 2010, V. 56, Iss. 198, pp. 735–741, DOI: org/10.3189/002214310793146269.
  31. Li Z.-L., Tang B.-H., Wu H. et al. (2013a), Satellite-derived land surface temperature: Current status and perspectives, Remote Sensing of Environment, 2013, V. 13, pp. 14–37, DOI: 10.1016/j.rse.2012.12.008.
  32. Li Z.-L., Wu H., Wang N. et al. (2013b), Land surface emissivity retrieval from satellite data, Intern. J. Remote Sensing, 2013, V. 34, pp. 3084–3127, DOI: org/10.1080/01431161.2012.716540.
  33. Malakar N., Hulley G., Hook S. et al., An operational land surface temperature product for Landsat thermal data: methodology and validation, IEEE Trans. Geoscience and Remote Sensing, 2018, V. 56, No. 10, pp. 5717–5735, DOI: 10.1109/TGRS.2018.2824828.
  34. McCarville D., Buenemann M., Bleiweiss M., Barsi J., Atmospheric correction of Landsat thermal infrared data: A calculator based on North American Regional Reanalysis (NARR) data, American Soc. for Photogrammetry and Remote Sensing Annu. Conf., 2011, pp. 319–330.
  35. Meng X., Cheng J., Guo H. et al., Accuracy evaluation of the Landsat 9 land surface temperature product, IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing, 2022, V. 15, pp. 8694–8703, DOI: 10.1109/JSTARS.2022.3212736.
  36. Pérez Díaz C. L., Lakhankar T., Romanov P. et al., Evaluation of VIIRS land surface temperature using CREST-SAFE air, snow surface, and soil temperature data, Geosciences, 2015, V. 5, pp. 334–360, DOI: 10.3390/geosciences5040334.
  37. Saunders R., Hocking J., Turner E. et al., An update on the RTTOV fast radiative transfer model (currently at version 12), Geoscientific Model Development, 2018, V. 11, Iss. 7, pp. 2717–2737, DOI: 10.5194/gmd-11-2717-2018.
  38. Sobrino J., Jimenez-Munoz J., Soria G. et al., Land surface emissivity retrieval from different VNIR and TIR sensors, IEEE Trans. Geoscience and Remote Sensing, 2008, V. 46, No. 2, pp. 316–327, DOI: 10.1109/TGRS.2007.904834.
  39. Taloor A. K., Mandas D. S., Kothyari G. C., Retrieval of land surface temperature, normalized difference moisture index, normalized difference water index of the Ravi basin using Landsat data, Applied Computing and Geosciences, 2021, V. 9, Article 100051, 11 p., DOI: 10.1016/j.acags.2020.100051.
  40. Wang F., Qin Z., Song C. et al., An improved mono-window algorithm for land surface temperature retrieval from Landsat 8 Thermal Infrared Sensor data, Remote Sensing, 2015, V. 7, No. 4, pp. 4268–4289, DOI: 10.3390/rs70404268.
  41. Wang M., Zhang Z., Hu T., Liu X., A practical single-channel algorithm for land surface temperature retrieval: Application to Landsat series data, J. Geophysical Research: Atmospheres, 2019, V. 12, pp. 299–316, DOI: 10.1029/2018JD029330.
  42. Wang M., He C., Zhang Z. et al., Evaluation of three land surface temperature products from Landsat series using in situ measurements, IEEE Trans. Geoscience and Remote Sensing, 2023, V. 61, Article 5000119, 19 p., DOI: 10.1109/TGRS.2022.3232624.
  43. Xu C., Qu J. J., Hao X. et al., Surface soil temperature seasonal variation estimation in a forested area using combined satellite observations and in-situ measurements, Intern. J. Applied Earth Observation and Geoinformation, 2020, V. 91, Article 102156, DOI: 10.1016/j.jag.2020.102156.
  44. Xu C., Liao S., Huang L., Xia J., Soil temperature estimation at different depths over the central Tibetan Plateau integrating multiple Digital Earth observations and geo-computing, Intern. J. Digital Earth, 2023, V. 16, pp. 4023–4043, DOI: 10.1080/17538947.2023.2264267.