遥感学报
遙感學報
요감학보
JOURNAL OF REMOTE SENSING
2009年
6期
989-1009
,共21页
徐同仁%刘绍民%秦军%梁顺林
徐同仁%劉紹民%秦軍%樑順林
서동인%류소민%진군%량순림
MODIS温度产品%通用陆面模式%集合卡尔曼滤波%地表水热通量
MODIS溫度產品%通用陸麵模式%集閤卡爾曼濾波%地錶水熱通量
MODIS온도산품%통용륙면모식%집합잡이만려파%지표수열통량
MODIS land surface temperature products%common land model%ensemble kalman filter%sensible and latent heat flux
基于集合卡尔曼滤波和通用陆面模型(CLM 1.0)发展了一个地表温度的同化系统.这个系统同化了MODIS温度产品,并将MODIS的叶面积指数引入CLM模型中,主要用于改进地表水热通量的估算精度.将CLM输出的地表温度与MODIS地表温度建立关系,并作为同化系统的观测算子.将MODIS地表温度与实测地表温度进行了比较,将其均方差(Root Mean Square Error,RMSE)作为观测误差.选取3个美国通量网站点(Blackhill、 Bondville、Brookings)作为实验数据,结果表明:同化结果中地表温度、显热通量的估算精度均有提高.其中Blackhill站的估算精度改进最大,均方差由81.5W·m~(-2)减小到58.4W·m~(-2),Bondville站均方差由47.0W·m~(-2)减小到31.8W·m~(-2),Brookings站均方差由46·5W·m~(-2)减小到45.1W·m~(-2).潜热通量估算精度在Bondville站均方差由88.6W·m~(-2)减小到57.7W·m~(-2),Blackhill站均方差由53.4W·m~(-2)减小到47.2W·m~(-2).总之,结合陆面过程模型同化MODIS温度产品估算地表水热通量是可行的.
基于集閤卡爾曼濾波和通用陸麵模型(CLM 1.0)髮展瞭一箇地錶溫度的同化繫統.這箇繫統同化瞭MODIS溫度產品,併將MODIS的葉麵積指數引入CLM模型中,主要用于改進地錶水熱通量的估算精度.將CLM輸齣的地錶溫度與MODIS地錶溫度建立關繫,併作為同化繫統的觀測算子.將MODIS地錶溫度與實測地錶溫度進行瞭比較,將其均方差(Root Mean Square Error,RMSE)作為觀測誤差.選取3箇美國通量網站點(Blackhill、 Bondville、Brookings)作為實驗數據,結果錶明:同化結果中地錶溫度、顯熱通量的估算精度均有提高.其中Blackhill站的估算精度改進最大,均方差由81.5W·m~(-2)減小到58.4W·m~(-2),Bondville站均方差由47.0W·m~(-2)減小到31.8W·m~(-2),Brookings站均方差由46·5W·m~(-2)減小到45.1W·m~(-2).潛熱通量估算精度在Bondville站均方差由88.6W·m~(-2)減小到57.7W·m~(-2),Blackhill站均方差由53.4W·m~(-2)減小到47.2W·m~(-2).總之,結閤陸麵過程模型同化MODIS溫度產品估算地錶水熱通量是可行的.
기우집합잡이만려파화통용륙면모형(CLM 1.0)발전료일개지표온도적동화계통.저개계통동화료MODIS온도산품,병장MODIS적협면적지수인입CLM모형중,주요용우개진지표수열통량적고산정도.장CLM수출적지표온도여MODIS지표온도건립관계,병작위동화계통적관측산자.장MODIS지표온도여실측지표온도진행료비교,장기균방차(Root Mean Square Error,RMSE)작위관측오차.선취3개미국통량망참점(Blackhill、 Bondville、Brookings)작위실험수거,결과표명:동화결과중지표온도、현열통량적고산정도균유제고.기중Blackhill참적고산정도개진최대,균방차유81.5W·m~(-2)감소도58.4W·m~(-2),Bondville참균방차유47.0W·m~(-2)감소도31.8W·m~(-2),Brookings참균방차유46·5W·m~(-2)감소도45.1W·m~(-2).잠열통량고산정도재Bondville참균방차유88.6W·m~(-2)감소도57.7W·m~(-2),Blackhill참균방차유53.4W·m~(-2)감소도47.2W·m~(-2).총지,결합륙면과정모형동화MODIS온도산품고산지표수열통량시가행적.
In this paper, a land surface temperature data assimilation scheme is developed based on Ensemble Kalman Filter (EnKF) and Common Land Model version 1.0 (CLM), which is mainly used to improve the estimation of the sensible and latent heat fluxes by assimilating MODIS land surface temperature (LST) products. Leaf area index (LAI) is also updated dynamically by MODIS LAI products. In this study, the relationship between the MODIS LST and the CLM surface temperature is determined and taken as the observation operator of the assimilation scheme. Meanwhile, the MODIS LST is compared with the ground-measured surface temperature, and the Root Mean Square Error (RMSE) is taken as the observation error. The scheme is tested and validated based on measurements in three observation stations (Blackhill, Bondville and Brookings) of Ameriflux. Results indicate that data assimilation method improves the estimation of surface temperature and sensible heat flux. The RMSE of sensible heat flux reduced from 81.5W·m~(-2) to 58.4W·m~(-2) at the Blackhill site, from 47.0W·m~(-2) to 31.8W·m~(-2) at the Bondville site, from 46.5W·m~(-2) to 45.1W·m~(-2) at the Brookings site. The RMSE of latent heat fluxes reduced from 88.6W·m~(-2) to 57.7W·m~(-2) at the Bondville site, from 53.4W·m~(-2) to 47.2W·m~(-2) at the Blackhill site. In addition, it is a practical way to improve the estimation of sensible and latent heat flux by assimilating MODIS LST into land surface model.