Leaf Area Index (LAI) is an important structural feature of our ecosystem as it affects energy, carbon, and water exchanges between the land surface and overlying atmosphere. Global scale LAI datasets have been obtained by regression, heuristic data driven, and radiative transfer models using remotely sensed land surface reflectance data. However, the estimation of LAI from remotely sensed data is limited only to clear sky conditions. Also, it is problematic to estimate LAI in forests by using conventional remote sensing image analysis of multi-spectral data. Due to the above-mentioned shortcomings of estimating LAI from remotely sensed data, this study obtained LAI from meteorological data using the Gene Expression Programming (GEP) technique. The new approach was tested in different forest sites with broad-leaf and needle-leaf trees in USA. The results showed that the GEP technique can accurately estimate LAI from meteorological data in different forest sites.

中文翻译：

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使用气象数据估算森林叶面积指数：启发式模型评估

叶面积指数 (LAI) 是我们生态系统的一个重要结构特征，因为它影响地表和上覆大气之间的能量、碳和水交换。全球尺度的 LAI 数据集是通过回归、启发式数据驱动和使用遥感地表反射数据的辐射传输模型获得的。然而，从遥感数据估计 LAI 仅限于晴朗的天空条件。此外，通过使用多光谱数据的常规遥感图像分析来估计森林中的 LAI 也存在问题。由于上述从遥感数据估计LAI的缺点，本研究使用基因表达编程（GEP）技术从气象数据中获得LAI。这种新方法在美国不同的阔叶树和针叶树林地进行了测试。