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摘要： 生态系统呼吸（Ecosystem respiration，Re）是陆地生态系统碳收支的重要组成部分，准确模拟Re对研究碳循环具有重要意义。利用3种典型的遥感模型，C-flux（The carbon flux model）、ReRSM （Ecosystem respiration Remote Sensing Model）和TPGPP（Temperature Precipitation Gross Primary Production）模型，基于不同时间尺度（1 d和8 d尺度）的通量观测和遥感数据，对包含5种植被类型（农作物CROP、落叶阔叶林DBF、常绿针叶林ENF、草地GRASS和混交林MF）的24个站点（52个站年）的Re进行了模拟。结果表明：不同模型模拟结果的差异较大，C-Flux模型模拟结果R2和RMSE的范围为0.72~0.96 gCm-2d-1和0.30~3.47 gCm-2d-1，ReRSM模型R2与RMSE的范围为0.70~0.98 gCm-2d-1和0.45~6.07 gCm-2d-1，TPGPP模型R2与RMSE的范围为0.76~0.97 gCm-2d-1和0.41~2.45 gCm-2d-1；1 d和8 d尺度，TPGPP模型模拟效果最好，分别73%和67%的站年的TPGPP模型模拟结果的R2高于其他两种模型，65%和50%的站年的TPGPP模型模拟结果的RMSE低于另两种模型。大部分站年（分别为75%和77%）ReRSM模型模拟的Re与观测Re之间的R2明显高于C-flux模型，然而大部分站年（79%和77%）的RMSE高于C-flux模型，这表明ReRSM模型结构合理，能较好地模拟Re的季节变化趋势但模型参数有待改进。ReRSM模型中，年均生长季平均LSWI（Mean annual growing season of Land surface water index，LSWIsm）与其他站年相比过低，会导致模拟的Re高估，反之则低估。 关键词： 生态系统呼吸;  C?Flux模型;  ReRSM模型;  TPGPP模型     Abstract: Ecosystem respiration (Re) is an important component of terrestrial ecosystem carbon budget, and it was important to simulate Re accurately. In this study, Re was simulated at daily and 8-day time scales at 24 flux sites (52 site years) including 5 vegetation types by using three typical ecological models established based on remote sensing data, C-flux (the carbon flux model), ReRSM (Ecosystem respiration Remote Sensing Model) and TPGPP (Temperature Precipitation Gross Primary Production) model. Results showed that the three models had different performances. At 52 site years, the ranges of R2 and RMSE were 0.72~0.96 and 0.30~3.47 gCm-2d-1 for the C-flux model, 0.70~0.98 and 0.45~6.07 gCm-2d-1 for the ReRSM model, and 0.76~0.97 and 0.41~2.45 gCm-2d-1 for the TPGPP model. The TPGPP performed best compared with the other two models. R2 simulated with the TPGPP model was higher than the other two models at most site years with proportions of 73% and 67% at daily and 8-day scale, respectively. At daily and 8-day scale, R2 simulated with the ReRSM model was higher than that with the C-flux model at most site years with proportions of 75% and 77%, while RMSE with ReRSM model was higher than that with the C-flux model at most site years with proportions of 79% and 76%, respectively. Results indicated that the ReRSM model could simulate the trends of seasonal variations of Re while model parameters had some uncertainties. One important parameter in the ReRSM model, LSWIsm (Mean annual growing season of land surface water index), which was much lower would result in overestimation of Re, and higher LSWIsm would result in Re underestimation.

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