Air temperature estimation based on environmental parameters using remote sensing data
This study is aimed at estimating monthly mean air temperature (Ta) using the MODIS Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), latitude, altitude, slope gradient and land use data during 2001-2015. The results showed that despite some spatial similarities between...
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Kharazmi University
2018-03-01
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| Series: | تحقیقات کاربردی علوم جغرافیایی |
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| Online Access: | http://jgs.khu.ac.ir/article-1-2862-en.pdf |
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| author | Chenoor Mohammadi Manouchehr Farajzadeh Yousef Ghavdel Rahimi Abbas Ali Aliakbar Bidokhti |
| author_facet | Chenoor Mohammadi Manouchehr Farajzadeh Yousef Ghavdel Rahimi Abbas Ali Aliakbar Bidokhti |
| author_sort | Chenoor Mohammadi |
| collection | DOAJ |
| description | This study is aimed at estimating monthly mean air temperature (Ta) using the MODIS Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), latitude, altitude, slope gradient and land use data during 2001-2015. The results showed that despite some spatial similarities between annual spatial patterns of Ta and LST, their variations are significantly different, so that the Ta variation coefficient is four times the one of the LST. Our analysis indicated that while in winter latitude is the key factor in explaining the distribution of the differences LST-Ta, in other seasons the role of slope and vegetation become more prominent. After obtaining the spatial patterns of LST and Ta, we estimated Ta using regression models in spatial resolution of 0.125˚. The lowest estimation error was found in the months of November and December with a high explanatory coefficient (R2) of 70% and a standard error of 1 ° C. On the other hand, the maximum error was obtained from May to August with R2 between 59 to 63% and a standard error of 1.6 ° C which is significant at the 0.05 level. In addition, result of evaluation of individual months showed that estimation of Ta is more accurate at the cold months of the year (November, December, January, February, and March). With considering different land uses, the highest R2 was related to waters and urban areas (96 to 99%) in warm months, and the lowest R2 was for mixed forest and grassland (between 15 and 36%) in cold months. |
| format | Article |
| id | doaj-art-db4bcb4b602a47248f099c2a108f3abf |
| institution | Kabale University |
| issn | 2228-7736 2588-5138 |
| language | fas |
| publishDate | 2018-03-01 |
| publisher | Kharazmi University |
| record_format | Article |
| series | تحقیقات کاربردی علوم جغرافیایی |
| spelling | doaj-art-db4bcb4b602a47248f099c2a108f3abf2025-01-31T17:24:18ZfasKharazmi Universityتحقیقات کاربردی علوم جغرافیایی2228-77362588-51382018-03-011848131152Air temperature estimation based on environmental parameters using remote sensing dataChenoor Mohammadi0Manouchehr Farajzadeh1Yousef Ghavdel Rahimi2Abbas Ali Aliakbar Bidokhti3 PhD Student of climatology, Tarbiat Modares University, Tehran. full Professor of Climatology, Tarbiat Modares University, Tehran Assistant Professor of Climatology, Tarbiat Modares University, Tehran. full Professor of Physic, Tehran University, Tehran. This study is aimed at estimating monthly mean air temperature (Ta) using the MODIS Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), latitude, altitude, slope gradient and land use data during 2001-2015. The results showed that despite some spatial similarities between annual spatial patterns of Ta and LST, their variations are significantly different, so that the Ta variation coefficient is four times the one of the LST. Our analysis indicated that while in winter latitude is the key factor in explaining the distribution of the differences LST-Ta, in other seasons the role of slope and vegetation become more prominent. After obtaining the spatial patterns of LST and Ta, we estimated Ta using regression models in spatial resolution of 0.125˚. The lowest estimation error was found in the months of November and December with a high explanatory coefficient (R2) of 70% and a standard error of 1 ° C. On the other hand, the maximum error was obtained from May to August with R2 between 59 to 63% and a standard error of 1.6 ° C which is significant at the 0.05 level. In addition, result of evaluation of individual months showed that estimation of Ta is more accurate at the cold months of the year (November, December, January, February, and March). With considering different land uses, the highest R2 was related to waters and urban areas (96 to 99%) in warm months, and the lowest R2 was for mixed forest and grassland (between 15 and 36%) in cold months.http://jgs.khu.ac.ir/article-1-2862-en.pdfair temperatureland surface temperatureland useestimation model |
| spellingShingle | Chenoor Mohammadi Manouchehr Farajzadeh Yousef Ghavdel Rahimi Abbas Ali Aliakbar Bidokhti Air temperature estimation based on environmental parameters using remote sensing data تحقیقات کاربردی علوم جغرافیایی air temperature land surface temperature land use estimation model |
| title | Air temperature estimation based on environmental parameters using remote sensing data |
| title_full | Air temperature estimation based on environmental parameters using remote sensing data |
| title_fullStr | Air temperature estimation based on environmental parameters using remote sensing data |
| title_full_unstemmed | Air temperature estimation based on environmental parameters using remote sensing data |
| title_short | Air temperature estimation based on environmental parameters using remote sensing data |
| title_sort | air temperature estimation based on environmental parameters using remote sensing data |
| topic | air temperature land surface temperature land use estimation model |
| url | http://jgs.khu.ac.ir/article-1-2862-en.pdf |
| work_keys_str_mv | AT chenoormohammadi airtemperatureestimationbasedonenvironmentalparametersusingremotesensingdata AT manouchehrfarajzadeh airtemperatureestimationbasedonenvironmentalparametersusingremotesensingdata AT yousefghavdelrahimi airtemperatureestimationbasedonenvironmentalparametersusingremotesensingdata AT abbasalialiakbarbidokhti airtemperatureestimationbasedonenvironmentalparametersusingremotesensingdata |