叶片最大碳羧化速率(Vmax, maximum carboxylation rate)是森林生态系统碳循环模型中计算光合作用能力的关键参数。但至今仍缺乏高空间分辨率Vmax的分布数据，致使碳循环模型估算存在很大误差。本研究提出了一种新的高光谱遥感估算Vmax的方法，结合了光合作用的机理公式，利用阳叶总初级生产力GPP反推Vmax，并分析不同植被类型Vmax的变动差异。这种算法避免了以往采用植被化学参数叶片氮含量、叶片叶绿素含量等线性估算方法估算Vmax的局限性，提高了光合作用计算的准确性，为碳循环模型的深入研究提供数据基础。

Leaf maximum carboxylation rate is the key parameter in calculating photosynthetic capacity for forest ecosystem carbon cycle modeling. Since the lack of high spatial resolution data for Vmax, there are large errors in carbon budget estimates by carbon cycle models. A new remote sensing algorithm was developed combing with photosynthetic process equation to estimate Vmax. Vmax was retrieved by the variable of sunlit leaf GPP and its variations and differences among different vegetation types were analyzed. This method efficiently avoided the disadvantages of former algorithm using leaf chemical parameters in linear model to estimate Vmax, such as leaf nitrogen content, leaf chlorophyll content, and improved the accuracy of photosysthesis calculation. The result of this study can provide basic data for further study of carbon cycle model.