Generating MODIS hourly land surface temperature under clear sky conditions using Fourier series analysis Article Swipe
Hadi Zare Khormizi
,
M Jafari
,
Hamid Reza Ghafarian Malamiri
,
Ali Tavili
,
Hamidreza Keshtkar
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.jag.2024.104341
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.jag.2024.104341
Land surface temperature (LST) data with high temporal and spatial resolution are used in many studies, e.g. to assess climate changes, land–atmosphere interactions, surface energy balance, etc. However, clouds and the limitations of geostationary and polar orbiting satellites hinder the collection of high-quality thermal infrared (TIR) data. This research aims to generate hourly LST data from the Moderate Resolution Imaging Spectroradiometer (MODIS) with four daily observations. The Multi-channel Singular Spectrum Analysis (M−SSA) algorithm was used to reconstruct lost data due to clouds in the MODIS annual LST time series. Subsequently, Fourier series analysis was employed to generate hourly LST data based on the four MODIS observations per day. The developed Fourier series model was evaluated using hourly LST data from Meteosat-9 and ground surface soil temperature data at eight different Ameriflux sites. The evaluation of the Fourier series model showed that the Root Mean Square Error (RMSE) and coefficient of determination (R2) between the hourly LST data from the Meteosat-9 satellite and the hourly LST data generated by the Fourier series model using four simultaneous MODIS observations averaged 1.70 Kelvin and 0.98, respectively, throughout Iran. For Ameriflux sites, the average RMSE and R2 were 1.15 K and 0.98 between the surface soil temperature data and the surface soil temperature data generated using four simultaneous MODIS observations per day, respectively. Notably, the highest RMSE was observed during sunrise and sunset.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.jag.2024.104341
- OA Status
- gold
- Cited By
- 1
- References
- 38
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- OpenAlex ID
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All OpenAlex metadata
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https://doi.org/10.1016/j.jag.2024.104341Digital Object Identifier
- Title
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Generating MODIS hourly land surface temperature under clear sky conditions using Fourier series analysisWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-25Full publication date if available
- Authors
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Hadi Zare Khormizi, M Jafari, Hamid Reza Ghafarian Malamiri, Ali Tavili, Hamidreza KeshtkarList of authors in order
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https://doi.org/10.1016/j.jag.2024.104341Publisher landing page
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goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.jag.2024.104341Direct OA link when available
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Sky, Series (stratigraphy), Geography, Climatology, Fourier series, Remote sensing, Environmental science, Meteorology, Fourier analysis, Cartography, Fourier transform, Geology, Mathematics, Mathematical analysis, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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| abstract_inverted_index.that | 140 |
| abstract_inverted_index.time | 87 |
| abstract_inverted_index.used | 12, 74 |
| abstract_inverted_index.were | 193 |
| abstract_inverted_index.with | 5, 62 |
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| abstract_inverted_index.(TIR) | 45 |
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| abstract_inverted_index.Error | 145 |
| abstract_inverted_index.Iran. | 184 |
| abstract_inverted_index.MODIS | 84, 104, 175, 214 |
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| abstract_inverted_index.data. | 46 |
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| abstract_inverted_index.model | 112, 138, 171 |
| abstract_inverted_index.polar | 35 |
| abstract_inverted_index.using | 115, 172, 211 |
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| abstract_inverted_index.Kelvin | 179 |
| abstract_inverted_index.Square | 144 |
| abstract_inverted_index.annual | 85 |
| abstract_inverted_index.assess | 18 |
| abstract_inverted_index.clouds | 28, 81 |
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| abstract_inverted_index.sites. | 131 |
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| abstract_inverted_index.highest | 221 |
| abstract_inverted_index.series. | 88 |
| abstract_inverted_index.spatial | 9 |
| abstract_inverted_index.sunrise | 226 |
| abstract_inverted_index.sunset. | 228 |
| abstract_inverted_index.surface | 1, 23, 123, 200, 206 |
| abstract_inverted_index.thermal | 43 |
| abstract_inverted_index.Analysis | 70 |
| abstract_inverted_index.However, | 27 |
| abstract_inverted_index.Moderate | 57 |
| abstract_inverted_index.Notably, | 219 |
| abstract_inverted_index.Singular | 68 |
| abstract_inverted_index.Spectrum | 69 |
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| abstract_inverted_index.averaged | 177 |
| abstract_inverted_index.balance, | 25 |
| abstract_inverted_index.changes, | 20 |
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| abstract_inverted_index.infrared | 44 |
| abstract_inverted_index.observed | 224 |
| abstract_inverted_index.orbiting | 36 |
| abstract_inverted_index.research | 48 |
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| abstract_inverted_index.temporal | 7 |
| abstract_inverted_index.(M−SSA) | 71 |
| abstract_inverted_index.Ameriflux | 130, 186 |
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| abstract_inverted_index.evaluated | 114 |
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| abstract_inverted_index.satellite | 160 |
| abstract_inverted_index.Meteosat-9 | 120, 159 |
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| abstract_inverted_index.collection | 40 |
| abstract_inverted_index.evaluation | 133 |
| abstract_inverted_index.resolution | 10 |
| abstract_inverted_index.satellites | 37 |
| abstract_inverted_index.throughout | 183 |
| abstract_inverted_index.coefficient | 148 |
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| abstract_inverted_index.reconstruct | 76 |
| abstract_inverted_index.temperature | 2, 125, 202, 208 |
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| abstract_inverted_index.observations | 105, 176, 215 |
| abstract_inverted_index.simultaneous | 174, 213 |
| abstract_inverted_index.Multi-channel | 67 |
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| abstract_inverted_index.determination | 150 |
| abstract_inverted_index.geostationary | 33 |
| abstract_inverted_index.interactions, | 22 |
| abstract_inverted_index.observations. | 65 |
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| abstract_inverted_index.respectively. | 218 |
| abstract_inverted_index.Spectroradiometer | 60 |
| abstract_inverted_index.land–atmosphere | 21 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.8399999737739563 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.58771888 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |