气温是各种植物生理、水文、气象、环境等模式或模型中的一个非常重要的近地表气象参数.多年来气温数据以离散的常规气象站点观测为主,连续分布的格点气温数据则以站点资料插值而得到,分辨率低,无法反映地形等下垫面因素对局地气温的影响,在农业气候区划等研究中具有一定的局限性.随着卫星遥感地表温度算法的日趋成熟,为探讨卫星遥感地表温度数据在气温观测中的可能性和可行性,利用全中国2340个站点1998 2007年的逐旬平均最高气温数据,以及相应时段的NOAA/AVHRR旬最高地表温度数据,以线性回归及拟合模型为主,通过考虑植被指数、土地覆盖类型、季节、风速、气压、降水等各类影响因子,建立了旬最高地表温度与旬平均最高气温间的推算模型,并利用未参与建模的2002-2003年的常规气象站点气温数据,同时与推算气温和插值气温结果进行对比分析.结果表明,利用卫星遥感地表温度数据推算的旬值气温数据可取得较高的精度,尤其在地形复杂地区以及站点稀疏地区精度明显高于插值气温结果.%Near-surface atmosphere temperature is an important parameter in the many fields such as plant physiology, hydrology, meteorology and environics. Traditionally, near-surface temperature is obtained from the discrete meteorological observation, and the continuous regional temperatures are obtained by applying interpolation to the discrete station-observed results, which is limited in complicating topographical conditions. Satellite observation provides new technology for obtaining atmosphere temperatures with the development of the land surface temperature retrieval approach. A new methodology is suggested to obtain atmosphere temperatures with satellite data and the station-observed data in this paper. The formula describing land surface temperatures from the satellite- and station-observed near-surface atmosphere temperatures in the light of linear regression method was created by using the datasets from 2340 stations from 1998 to 2007 and the corresponding surface temperature data from the NOAA/AVHRR , with the various factors, including vegetation cover, land cover type, season, wind, air pressure and precipitation. The simulated results from the satellite data by this method are tested with the station-observed atmosphere temperature data between 2002 and 2003, which are not employed in the regression model. Additionally, the results are also compared with the interpolated air temperatures with the same resolution in a pixel-to-pixel way. especially in mountain regions and regions with sparse stations.
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