Gap-filling of land surface temperature in arid regions by combining Landsat 8 and 9 imageries
Land surface temperature (LST) is an important factor in land monitoring studies, but due to the presence of clouds, dust and sensor issues, there are missing values. The aims of this research are to determine the optimal parameters for the reconstruction of Landsat-LST images, required in many appl...
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IOP Publishing
2024-01-01
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| Series: | Environmental Research Communications |
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| Online Access: | https://doi.org/10.1088/2515-7620/ad898e |
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| author | Fahime Arabi Aliabad Ebrahim Ghaderpour Ahmad Mazidi Fatemeh Houshmandzade |
| author_facet | Fahime Arabi Aliabad Ebrahim Ghaderpour Ahmad Mazidi Fatemeh Houshmandzade |
| author_sort | Fahime Arabi Aliabad |
| collection | DOAJ |
| description | Land surface temperature (LST) is an important factor in land monitoring studies, but due to the presence of clouds, dust and sensor issues, there are missing values. The aims of this research are to determine the optimal parameters for the reconstruction of Landsat-LST images, required in many applications, by the harmonic analysis of time series algorithm (HANTS) and to investigate the possibility of improving LST reconstruction accuracy using Landsat 8 and 9 images simultaneously. For these aims, 91 Landsat 8 and 9 images with 100 m spatial resolution in 2022 and 2023 are employed, covering Yazd-Ardakan plain in Iran. Three methods are used for evaluation. In method one, a part of LST image is considered as a gap and is compared with the initial value after reconstruction. In method two, on a cloudy day and a cloudless day, surface temperature values are measured using thermometers at fifty points in plain lands, and the difference between gap-filled satellite measurements and ground measurements is calculated. In method three, all the reconstructed LST images are compared with the original images. In method one, the root mean square error (RMSE) of reconstructed LST reduces by 1.3 °C when using the combined Landsat 8 and 9 images. In method two, RMSEs of reconstructed LST images are 6.1 °C when using Landsat 8 and 5.4 °C when using the combined Landsat 8 and 9. Method three shows that 41% of the study region has RMSE of less than 2 °C when using only Landsat 8, while this value becomes 72% when combining Landsat 8 and 9. In general, the combined use of Landsat 8 and 9 LST images improves the accuracy of reconstruction using HANTS. The findings of this research are crucial for regional applications and remote monitoring of surface temperature in areas with limited weather stations. |
| format | Article |
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| institution | OA Journals |
| issn | 2515-7620 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Communications |
| spelling | doaj-art-1bd0d4d07b8144b9b25df8f4b7e3c52f2025-08-20T02:12:11ZengIOP PublishingEnvironmental Research Communications2515-76202024-01-0161010503710.1088/2515-7620/ad898eGap-filling of land surface temperature in arid regions by combining Landsat 8 and 9 imageriesFahime Arabi Aliabad0https://orcid.org/0009-0007-0309-3774Ebrahim Ghaderpour1https://orcid.org/0000-0002-5165-1773Ahmad Mazidi2https://orcid.org/0000-0003-4558-9907Fatemeh Houshmandzade3Department of Remote Sensing, Faculty of Geography, Yazd University , University Blvd., Safayieh, Yazd 8915818411, IranDepartment of Earth Sciences & CERI Research Center, Sapienza University of Rome, P.le Aldo Moro, 5, Rome, 00185, ItalyDepartment of Geography, Yazd University , University Blvd., Safayieh, Yazd, 8915818411, IranFaculty of Natural Resources and Desert Studies, Yazd University , University Blvd., Safayieh, Yazd, 8915818411, IranLand surface temperature (LST) is an important factor in land monitoring studies, but due to the presence of clouds, dust and sensor issues, there are missing values. The aims of this research are to determine the optimal parameters for the reconstruction of Landsat-LST images, required in many applications, by the harmonic analysis of time series algorithm (HANTS) and to investigate the possibility of improving LST reconstruction accuracy using Landsat 8 and 9 images simultaneously. For these aims, 91 Landsat 8 and 9 images with 100 m spatial resolution in 2022 and 2023 are employed, covering Yazd-Ardakan plain in Iran. Three methods are used for evaluation. In method one, a part of LST image is considered as a gap and is compared with the initial value after reconstruction. In method two, on a cloudy day and a cloudless day, surface temperature values are measured using thermometers at fifty points in plain lands, and the difference between gap-filled satellite measurements and ground measurements is calculated. In method three, all the reconstructed LST images are compared with the original images. In method one, the root mean square error (RMSE) of reconstructed LST reduces by 1.3 °C when using the combined Landsat 8 and 9 images. In method two, RMSEs of reconstructed LST images are 6.1 °C when using Landsat 8 and 5.4 °C when using the combined Landsat 8 and 9. Method three shows that 41% of the study region has RMSE of less than 2 °C when using only Landsat 8, while this value becomes 72% when combining Landsat 8 and 9. In general, the combined use of Landsat 8 and 9 LST images improves the accuracy of reconstruction using HANTS. The findings of this research are crucial for regional applications and remote monitoring of surface temperature in areas with limited weather stations.https://doi.org/10.1088/2515-7620/ad898earid regionland surface temperatureLandsat 8/9harmonic analysis of time seriessplit window |
| spellingShingle | Fahime Arabi Aliabad Ebrahim Ghaderpour Ahmad Mazidi Fatemeh Houshmandzade Gap-filling of land surface temperature in arid regions by combining Landsat 8 and 9 imageries Environmental Research Communications arid region land surface temperature Landsat 8/9 harmonic analysis of time series split window |
| title | Gap-filling of land surface temperature in arid regions by combining Landsat 8 and 9 imageries |
| title_full | Gap-filling of land surface temperature in arid regions by combining Landsat 8 and 9 imageries |
| title_fullStr | Gap-filling of land surface temperature in arid regions by combining Landsat 8 and 9 imageries |
| title_full_unstemmed | Gap-filling of land surface temperature in arid regions by combining Landsat 8 and 9 imageries |
| title_short | Gap-filling of land surface temperature in arid regions by combining Landsat 8 and 9 imageries |
| title_sort | gap filling of land surface temperature in arid regions by combining landsat 8 and 9 imageries |
| topic | arid region land surface temperature Landsat 8/9 harmonic analysis of time series split window |
| url | https://doi.org/10.1088/2515-7620/ad898e |
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