A Method for Deriving Relative Humidity From MODIS Data Under All-Sky Conditions
文献类型：期刊论文
| 作者 | Liao, Qian-Yu2,3; Leng, Pei2; Li, Zhao-Liang2; Ren, Chao4; Sun, Ya-Yong5; Gao, Mao-Fang2; Duan, Si-Bo2; Shang, Guo-Fei1 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2021-11-01 |
| 卷号 | 59期号:11页码:8992-9006 |
| 关键词 | Clouds Land surface temperature Satellites MODIS Mathematical model Humidity Remote sensing All-sky atmospheric profile land surface temperature (LST) precipitable water vapor (PWV) relative humidity (RH) |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2020.3036248 |
| 通讯作者 | Leng, Pei(lengpei@caas.cn) |
| 英文摘要 | Relative humidity (RH) is one of the key variables for understanding the water, energy, and carbon exchange between the Earth and the atmosphere. Traditional methods for deriving RH from remotely sensed data usually require ground meteorological observations or are limited to clear-sky conditions, thereby making it a significant challenge to obtain spatially complete RH under all-sky conditions, especially over the regions with sparse meteorological instruments for observation. To this end, a new approach for deriving all-sky RH entirely based on Moderate Resolution Imaging Spectroradiometer (MODIS) data was proposed in the present study. Two key assumptions in the approach under cloudy conditions are that the actual water vapor is linearly related to the total precipitable water vapor (PWV) and that air temperature is linearly related to land surface temperature (LST). Results from a total of 30 AmeriFlux stations proved the aforementioned assumptions based on MODIS data collected over a study period of three years from 2009 to 2011. For different aridity conditions, RH retrieval revealed reasonable accuracy with a root-mean-square error (RMSE) of approximately 15.3% over an arid and semiarid region, whereas a comparable RMSE of 17.0% was obtained over a humid area. Further results also indicated that the aforementioned linear relationships were generally temporally stable, thereby indicating that the proposed method can be used to obtain all-sky RH at a regional or global scale entirely based on MOD06_L2-derived LST and MOD05_L2-derived PWV data given that the assumed linear relationships can be easily determined by historical MOD07_L2-derived atmospheric profiles. |
| WOS关键词 | VAPOR-PRESSURE DEFICIT ; MINIMUM AIR-TEMPERATURE ; NEAR-SURFACE AIR ; WATER-VAPOR ; CLIMATE VARIABILITY ; PRECIPITABLE WATER ; DAILY MAXIMUM ; EVAPOTRANSPIRATION ; MOISTURE ; TRENDS |
| 资助项目 | National Natural Science Foundation of China[41921001] ; National Natural Science Foundation of China[41601397] ; IWHR Research and Development Support Program[JZ0145B042020] ; Second Tibetan Plateau Scientific Expedition and Research (STEP) Program[2019QZKK0304] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000711850900010 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; IWHR Research and Development Support Program ; Second Tibetan Plateau Scientific Expedition and Research (STEP) Program |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/167731]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Leng, Pei |
| 作者单位 | 1.Hebei GEO Univ, Sch Land Resources & Urban Rural Planning, Shijiazhuang 050031, Hebei, Peoples R China 2.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 4.Guilin Univ Technol, Coll Geomat & Geoinformat, Guilin 530001, Peoples R China 5.China Inst Water Resources & Hydropower Res, Beijing 100038, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liao, Qian-Yu,Leng, Pei,Li, Zhao-Liang,et al. A Method for Deriving Relative Humidity From MODIS Data Under All-Sky Conditions[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2021,59(11):8992-9006. |
| APA | Liao, Qian-Yu.,Leng, Pei.,Li, Zhao-Liang.,Ren, Chao.,Sun, Ya-Yong.,...&Shang, Guo-Fei.(2021).A Method for Deriving Relative Humidity From MODIS Data Under All-Sky Conditions.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,59(11),8992-9006. |
| MLA | Liao, Qian-Yu,et al."A Method for Deriving Relative Humidity From MODIS Data Under All-Sky Conditions".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 59.11(2021):8992-9006. |
入库方式： OAI收割
来源：地理科学与资源研究所
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