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DOI | 10.1002/joc.4766 |
A statistical framework for estimating air temperature using MODIS land surface temperature data | |
Janatian, Nasime1; Sadeghi, Morteza2; Sanaeinejad, Seyed Hossein1; Bakhshian, Elham3; Farid, Ali1; Hasheminia, Seyed Majid1; Ghazanfari, Sadegh4 | |
2017-03-15 | |
发表期刊 | INTERNATIONAL JOURNAL OF CLIMATOLOGY |
ISSN | 0899-8418 |
EISSN | 1097-0088 |
出版年 | 2017 |
卷号 | 37期号:3 |
文章类型 | Article |
语种 | 英语 |
国家 | Iran; USA |
英文摘要 | Remote sensing has shown an immense capability for large-scale estimation of air temperature (T-air), one of the most important environmental state variables, using land surface temperature (LST) data. Following recent investigations on the T-air-LST relationship, in this article, we propose an advanced statistical approach to this realm. We tested the approach for estimation of T-air in eastern part of Iran using MODIS daytime and nighttime LST products and 11 auxiliary variables including Julian day, solar zenith angle, extraterrestrial solar radiation, latitude, altitude, reflectance at various visible and infrared bands and vegetation indices. Fourteen statistical models constructed through a stepwise regression analysis were evaluated along a 5-year period (2000-2004) using MODIS and meteorological station data. Results of this study indicated that the statistical approach performed reasonably well, where our final proposed model could estimate average T-air with validation mean absolute error of 2.3 and 1.8 degrees C at daily and weekly scales, respectively. Nighttime LST, Julian day, altitude and solar zenith angle indicated to be the most effective variables capturing most variations of T-air in the study region. Variables influenced by land surface and land cover properties including reflectance at different bands and vegetation indices showed a negligible effect on the T-air-LST relationship within the study area. It was indicated that the proposed models generally performed better for lower altitude regions. |
英文关键词 | remote sensing MODIS air temperature land surface temperature statistical models |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000395349500005 |
WOS关键词 | AVHRR DATA ; SATELLITE IMAGERY ; SOIL-MOISTURE ; DAILY MAXIMUM ; LST DATA ; MODEL ; EVAPOTRANSPIRATION ; PRODUCTS ; INTERPOLATION ; VALIDATION |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/37214 |
专题 | 气候变化 |
作者单位 | 1.Ferdowsi Univ Mashhad, Dept Water Engn, Mashhad, Iran; 2.Utah State Univ, Dept Plants Soils & Climate, 4820 Old Main Hill, Logan, UT 84322 USA; 3.Isfahan Univ Technol, Dept Civil Engn, Esfahan, Iran; 4.Grad Univ Adv Technol, Water Engn Dept, Kerman, Iran |
推荐引用方式 GB/T 7714 | Janatian, Nasime,Sadeghi, Morteza,Sanaeinejad, Seyed Hossein,et al. A statistical framework for estimating air temperature using MODIS land surface temperature data[J]. INTERNATIONAL JOURNAL OF CLIMATOLOGY,2017,37(3). |
APA | Janatian, Nasime.,Sadeghi, Morteza.,Sanaeinejad, Seyed Hossein.,Bakhshian, Elham.,Farid, Ali.,...&Ghazanfari, Sadegh.(2017).A statistical framework for estimating air temperature using MODIS land surface temperature data.INTERNATIONAL JOURNAL OF CLIMATOLOGY,37(3). |
MLA | Janatian, Nasime,et al."A statistical framework for estimating air temperature using MODIS land surface temperature data".INTERNATIONAL JOURNAL OF CLIMATOLOGY 37.3(2017). |
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