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| MODIS大气廓线产品在青海省空气温度遥感估算中的应用研究 | |
| 引用本文： | 田圣戎,朱文彬,周世健.MODIS大气廓线产品在青海省空气温度遥感估算中的应用研究[J].地理科学进展,2021,40(8):1386-1396. |
| 作者姓名： | 田圣戎 朱文彬 周世健 |
| 作者单位： | 1.中国科学院地理科学与资源研究所,北京 100101 2.东华理工大学测绘工程学院,南昌 330013 3.南昌航空大学,南昌 330063 |
| 基金项目： | 青海省重大科技专项(2019-SF-A4);青海省基础研究计划项目(2020-ZJ-715);青海三江源生态保护和建设二期工程科研和推广项目(2018-S-3);国家自然科学基金项目(42071032);中国科学院青年创新促进会资助项目(2020056) |
| 摘 要： | 气温是反映生态环境的重要参数之一,准确估算气温的时空分布对于气候变化研究具有重要意义。论文基于2011—2019年青海省气温实测数据、MODIS产品和SRTM DEM数据,在像元尺度分别开展了晴天条件和有云条件下瞬时空气温度的遥感估算研究,并评价了不同气温估算方法的精度差异,进而通过多元回归模型生成研究区高精度月空气温度产品,对青海省气温的时空分布格局进行分析。研究结果表明,在未使用气温实测数据进行校准的情况下,通过将MOD07_L2大气廓线产品反演的空气温度与MOD06_L2地表温度平均的方法,能够显著提高气温的估算精度。晴天条件下相关系数(r)为0.93,均方根误差(RMSE)为4.71 ℃;有云条件下r为0.89,RMSE为5.16 ℃。在使用气温观测值进行校准的情况下,通过引入高程参数,多元回归模型月尺度空气温度估算的决定系数(R2)和RMSE总体分别保持在0.8以上和2.5 ℃以下。将上述回归模型应用到栅格尺度,从而生成整个青海省高精度卫星过境时刻的逐月气温产品,进而分析其时空分布格局。具体来说,青海省月最高气温出现在7月,全省平均气温为13.59 ℃,最低气温出现在1月,全省平均气温为-9.44 ℃;气温的空间分布主要受海拔控制,全省平均气温直减率为4 ℃/km。上述研究表明MODIS大气廓线产品在全天气气温估算方面具有独特优势,特别是在地面气温实测数据的支持下能够有效降低遥感估算的系统性误差,实现大尺度复杂地形条件下气温的高精度估算。 |
| 关 键 词： | 气温估算 多元回归模型 MODIS 遥感 青海省 |
| 收稿时间： | 2020-11-06 |
| 修稿时间： | 2021-04-20 |
Near surface air temperature estimation based on MODIS atmospheric profile product over Qinghai Province |
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| TIAN Shengrong,ZHU Wenbin,ZHOU Shijian.Near surface air temperature estimation based on MODIS atmospheric profile product over Qinghai Province[J].Progress in Geography,2021,40(8):1386-1396. | |
| Authors: | TIAN Shengrong ZHU Wenbin ZHOU Shijian |
| Institution: | 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China 2. Faculty of Geomatics, East China University of Technology, Nanchang 330013, China 3. Nanchang Hangkong University, Nanchang 330063, China |
| Abstract: | Near surface air temperature is one of the most essential variables in a wide range of disciplines such as hydrology, meteorology, and ecology. Accurate estimation of air temperature for continuous spatial and temporal coverage is also crucial to climate change studies. Considering the sparse distribution of meteorological stations in Qinghai Province of China, this study estimated its near surface air temperature (Ta) based on meteorological observations, MODIS products, and SRTM DEM data from 2011 to 2019. First, daily instantaneous Ta under clear and cloudy sky conditions was retrieved from MOD07_L2 and MOD06_L2 products without the support of meteorological observations. Three parameterization schemes were used in this process and their results were compared. Second, these daily Ta estimates with highest accuracy were combined with meteorological observations and SRTM DEM data to generate high-precision monthly air temperature through the multiple regression method. Finally, the spatial and temporal distribution of Ta in Qinghai Province was analyzed. The results show that the accuracy of daily instantaneous Ta estimates can be improved significantly through averaging the near surface air temperature retrieved from MOD07_L2 and land surface temperature retrieved from MOD06_L2 without the calibration of meteorological observations. Specifically, the correlation coefficient (r) and root mean square error (RMSE) under clear sky conditions was 0.93 and 4.71 ℃, respectively; and the r and RMSE under cloudy sky conditions was 0.89 and 5.16 ℃, respectively. The multiple regression model with the calibration of meteorological observations provided a good estimation of monthly air temperature. The cross-validation results indicate that the determination coefficient (R2) and RMSE were generally above 0.8 and below 2.5 ℃, respectively. The high accuracy of monthly Ta estimation made it possible for us to investigate the spatial and temporal distribution of Ta in Qinghai Province. To be specific, the maximum and minimum monthly air temperature occurred in July and January, which was 13.59 ℃ and -9.44 ℃, respectively. Its spatial distribution was dominated by elevation. The average adiabatic lapse rate over the whole province was 4 ℃/km, which agreed well with the ground-based meteorological observations. The analysis indicates that MODIS atmospheric profile product holds great potential for all-weather air temperature estimation. The introduction of ground-based observations can reduce significantly the systematic error of air temperature estimation. The combination of MODIS products and ground-based observations proves to be useful for accurate air temperature estimation over large areas with complex topography. |
| Keywords: | air temperature estimation multiple regression model MODIS remote sensing Qinghai Province |
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