A statistical method based on remote sensing for the estimation of air temperature in China
文献类型：期刊论文
| 作者 | Chen, Fengrui1; Liu, Yu1; Liu, Qiang1; Qin, Fen1 |
| 刊名 | INTERNATIONAL JOURNAL OF CLIMATOLOGY
 |
| 出版日期 | 2015 |
| 卷号 | 35期号:8页码:6558-6575 |
| 关键词 | air temperature land surface temperature regression analysis remote sensing |
| 通讯作者 | Qin, F (reprint author), Henan Univ, Key Lab Geospatial Technol, Middle & Lower Yellow River Reg, Minist Educ, Kaifeng, Henan, Peoples R China. |
| 英文摘要 | Environmental applications require accurate air temperature (T-air) datasets with different temporal and spatial resolutions. Existing methods generally improve the estimation accuracy of T-air using environmental variables as auxiliary data to overcome problems related to sparse metrological stations. However, these data are always fixed and do not comprehensively explain the variations in T-air values at all temporal and spatial scales. Moreover, these methods seldom consider the spatial heterogeneity of relationships between T-air and auxiliary data. This heterogeneity is often caused by several factors, such as land type, topography, and climate. This study proposes an estimation method to produce maximum, minimum, and mean T-air (T-max, T-min, and T-mean) datasets at different temporal and spatial resolutions using satellite-derived digital elevation model data and both nighttime and daytime land surface temperature data as auxiliary data. The method is based on the assumption that the relationships between T-air and the chosen auxiliary data vary spatially. These relationships were further explored using geographically weighted regression with adaptive bi-square kernel function. The derived relationships were used to construct a T-air estimation model. Monthly T-air data with 5-km resolution and 8-day T-air data with 1-km resolution were produced for 2010. The results show that the proposed method can accurately represent the variations in T-air; the R-2 values were in the range of 0.95-0.99 for the monthly T-air data and 0.93-0.99 for the 8-day T-air data. The root mean square errors (RMSEs) for the monthly and 8-day T-max, T-min, and T-mean data of the year 2010 were 1.29 and 1.45 degrees C, 1.24 and 1.29 degrees C, and 0.8 and 1.2 degrees C, respectively. These results were compared with those from other estimation methods, specifically the estimation of T-air based on multiple linear regression (EATMLR) and regression kriging (EATRK). The proposed method was found to produce RMSEs that were 25-26% smaller than EATMLR and 34-42% smaller than EATRK. |
| 研究领域[WOS] | Meteorology & Atmospheric Sciences |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000357737000033 |
| 源URL | [http://ir.ceode.ac.cn/handle/183411/38423]  |
| 专题 | 遥感与数字地球研究所_SCI/EI期刊论文_期刊论文 |
| 作者单位 | 1.[Chen, Fengrui 2.Qin, Fen] Henan Univ, Key Lab Geospatial Technol, Middle & Lower Yellow River Reg, Minist Educ, Kaifeng, Henan, Peoples R China 3.[Liu, Yu] Henan Univ, Coll Comp & Informat Engn, Kaifeng, Henan, Peoples R China 4.[Liu, Qiang] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Fengrui,Liu, Yu,Liu, Qiang,et al. A statistical method based on remote sensing for the estimation of air temperature in China[J]. INTERNATIONAL JOURNAL OF CLIMATOLOGY,2015,35(8):6558-6575. |
| APA | Chen, Fengrui,Liu, Yu,Liu, Qiang,&Qin, Fen.(2015).A statistical method based on remote sensing for the estimation of air temperature in China.INTERNATIONAL JOURNAL OF CLIMATOLOGY,35(8),6558-6575. |
| MLA | Chen, Fengrui,et al."A statistical method based on remote sensing for the estimation of air temperature in China".INTERNATIONAL JOURNAL OF CLIMATOLOGY 35.8(2015):6558-6575. |
入库方式： OAI收割
来源：遥感与数字地球研究所
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