The Azimuth Project
Leaf area index (changes)

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Leaf Area Index (LAI) is a surface characteristic of the Earth, which appears in the modeling of Earth system processes. It is defined as half the total developed area of green leaves (i.e. the photosynthetically active area) per unit horizontal surface area. It characterizes the structure and the functioning of vegetation. It usually varies in the range 1-6.

From LandSAF (see references)

Leaf Area Index (LAI) is a dimensionless variable [], which defines an important structural property of a plant canopy. LAI is defined as one half the total leaf area per unit ground area (Chen and Black, 1992). It provides complementary information to the FVC, accounting for the surface of leaves contained in a vertical column normalized by its cross-sectional area. It defines thus the area of green vegetation that interacts with solar radiation determining the remote sensing signal, and represents the size of the interface between the vegetation canopy and the atmosphere for energy and mass exchanges. LAI is thus a necessary input for Numerical Weather Prediction (NWP), regional and global climate modelling, weather forecasting and global change monitoring. Besides, the LAI is relevant for Land Biosphere Applications such us agriculture and forestry, environmental management and land use, hydrology, natural hazards monitoring and management, vegetation-soil vegetation- dynamics monitoring and drought conditions.soil dynamics monitoring and drought conditions.

Estimation from remote sensing

The monitoring of seasonal and interannual variability of LAI fiels is allowed by remote sensing observations with moderate resolution optical sensors (pixel size 250 m - 7 km). In order to use LAI properly in land surface models, LAI products should be validated by comparing them with in situ measurements. LAI estimation from remote sensing is based on the analysis of multispectral and multidirectional surface reflectance signatures of photosynthetic vegetation elements. To remove residual atmospheric, cloud contamination and view-illumination effects, pre- or post-processing steps can be applied to LAI retrieval.

  • A first approach is based on (semi-)empirical relationships between LAI and vegetation indices (i.e. combination of surface reflectances) designed to maximize sensitivity to the vegetation characteristics and minimize other factors. The relationships are calibrated for distinct vegetation types using in situ LAI and reflectance measurements or simulations from canopy radiation models.

  • A second approach is based on the inversion of a radiative transfer model simulating surface reflectances from canopy structure characteristics, soil and leaf optical properties, and view-illumination geometry.

Main sources of uncertainty

There are three main sources of uncertainty that affect the estimation of LAI from surface reflectances.

  • The inverse problem is ill-posed, thus small variations in surface reflectance can result in large variations in LAI estimations.

  • The representation of the canopy architecture in LAI retrieval algorithms.

  • Application of the retrieval algorithm to a range of vegetation types and environmental conditions. LAI retrieval algoritms are based on empirical assumptions on the distribution of their parameters that can depart significantly from actual canopy and soil characteristics.

Experimental determination

For small areas, destructive techniques can be used to determine LAI. Over large areas, this is too laborious and optical indirect techniques are used. These are based on the analysis of light transmittance through the canopy and an provide effective LAI with uncertainties through foliage clumping (up to 70% underestimation in coniferous forests), insufficient distinguishment of green leaves with respect to other plants elements (which leads to a positive bias in measured LAI) and possible neglect of forest understory. Other measurement errors include saturation of the optical signal in dense canopies, simplification of leaf optical properties and insufficient spatial sampling within a plot.


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category: earth science