Journal of Geo-information Science ›› 2021, Vol. 23 ›› Issue (7): 1155-1168.doi: 10.12082/dqxxkx.2021.200609

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Scaling Effects of the True and Effective Leaf Area Index (LAI and LAIe) and Clumping Index (CI)

FANG Hongliang1,2,*()   

  1. 1. State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2020-10-16 Revised:2021-01-20 Online:2021-07-25 Published:2021-09-25
  • Contact: FANG Hongliang
  • Supported by:
    The National Key Research and Development Program of China(2016YFA0600201)


Scaling effects describe the observational differences caused by different observation standards. It is an important research topic in Earth science and quantitative remote sensing. Current studies mainly focus on estimating the errors caused by the scaling effects, but different opinions still exist about the scaling effects in some critical vegetation structural parameters and their scale transformation methods. This paper analyzes the scaling effects and the scale transformation methods of three key vegetation structural parameters, namely Leaf Area Index (LAI), Effective LAI (LAIe), and Clumping Index (CI), based on their definitions and acquisition methods. By definition, LAI is free from the scaling effects, whereas LAIe and CI have scaling effects. The scaling effects of CI is introduced by LAIe (CI=LAIe/LAI). LAI, LAIe, and CI can be obtained through field measurement and remote sensing inversion methods. In field measurements, LAI is obtained through the direct destructive method or the indirect optical method. LAI obtained through the destructive method has no scaling effects. The indirect optical method estimates the three parameters based on the Beer-Lambert equation with the canopy gap fraction. LAI-2200, digital hemispherical photography, and photosynthetic active radiation sensors are commonly used instruments. The non-liner gap fraction model has scaling effects in deriving these parameters. Remote sensing technology uses passive optical methods, Light Detection and Ranging (LiDAR) technology, and Synthetic Aperture Radar (SAR) methods to estimate LAI, LAIe, and CI. The classic passive optical methods can be divided into the empirical vegetation index estimation methods and the physical model inversion methods. The vegetation index methods establish an empirical relationship between vegetation structural parameters and vegetation indices. The physical model inversion methods are based on physical radiative transfer models. The scaling effects of the remote sensing methods depend on the linear or nonlinear characteristics of these methods. Currently, the major global LAI, LAIe, and CI remote sensing products are acquired from nonlinear inversion models, thus these inversion methods are subject to scaling effects. Nevertheless, the nonlinearity in the inversion process does not necessarily mean that the LAI products have scaling effects. The scaling effects of the LAI products still follow the basic LAI definition. Therefore, current remote sensing LAI products do not subject to the scaling effects at pixel level. On the other hand, both LAIe and CI products do have scaling effects, but the scaling effects are often ignored in practice. In the validation of the remote sensing products, the scaling effects need to be considered while homogeneous areas are preferred in the validation studies. In conclusion, attention should be paid to distinguishing the different scaling effects displayed by the parameters in their definitions, the field measurement and remote sensing inversion methods, and remote sensing products. For the scale transformation, it is more important to investigate the most suitable and efficient method rather than a universal method.

Key words: Leaf Area Index (LAI), true LAI, effective LAI, Clumping Index (CI), scaling effects, scale transformation