**3. Canopy structure and radiation flux interactions**

228 Solar Radiation

of the atmosphere. EVI has been mostly used for images of the Moderate-Resolution

The rationale behind the vegetation indices is that increasing vegetation LAI increases reflectance in the NIR due to a low leaf absorption in this spectral region, while in the red region a decrease occurs. It has been shown that most of the indices saturate for LAIs above around 3 (Tucker, 1979). However, the shape of the curve of fAPAR (the fraction of absorbed PAR) is the same as NDVI versus LAI. Sellers (1987) showed that the SRVI has a nearly linear relationship to fAPAR. Myneni et al. (1992) showed a nearly linear relationship between fAPAR and NDVI and a simple linear model relating fAPAR to top of canopy NDVI has been proposed (Myneni & Williams, 1994). Global datasets of time series of NDVI and EVI are available (e.g, Lhermitte et al., 2011), which make them highly attractive for monitoring grasslands over large areas and for estimating canopy

Canopy radiative transfer models have been developed to simulate bidirectional reflectance factor of vegetation canopies (BRF). One simulated BRF (BRFj(S)) can be represented by a function or algorithm, f, of subsystem characteristics (aj, bj, cj, dj, ej) (Goel & Strebel, 1983,

 BRFj(S)= f(aj, bj, cj, dj, ej) (11) where aj, bj, cj, dj and ej define the source, the atmosphere, the vegetation, the soil and the sensor subsystems, respectively. The source is characterized by the solar zenith and azimuth angles and the total flux intensity (normalized to one), the atmosphere is characterized by the direct plus diffuse radiation, the canopy is characterized by the leaf reflectance (ρ) and transmittance (τ), LAI, LAD and the leaf spatial distribution parameter (λ0), the soil is characterized by the soil reflectance (ρs) and the sensor is characterized by the view zenith

The inversion process consists of deriving a function or algorithm, g, that will yield the set of canopy parameters {cj}, as a function of the observed canopy BRF (BRF(O)) and the other

 {cj} = g(BRFj(O), aj, bj, dj, ej) (12) Soil reflectance {dj} may also be derived through the inversion process along with the vegetation canopy parameters. The numerical inversion of a canopy radiative transfer model involves the minimization of differences between a set of simulated and observed BRF values acquired under different illumination/viewing geometries. Canopy parameter values that give the lowest difference between BRF(S) and BRF(O) are the estimated canopy

One limitation of the inversion process is that the number of observed values must be at least equal to the number of canopy parameters to be retrieved. This makes the process difficult for satellite image applications as most of the sensors collect single illumination/viewing geometry. This also limits the number of estimated parameters,

Imaging Spectroradiometer (MODIS) sensor for monitoring vegetation.

**2.4.2 Canopy radiative transfer model inversion** 

conditions.

Goel, 1988):

values.

(VZA) and view azimuth angles.

subsystem characteristics (Goel & Strebel, 1983):

In this section we initially present a brief description of the morphological characteristics of some forage grasses that are used for dairy and meat production in pasture-based systems, address some features relative to LAI and radiation interception measurements, and finally, we use a stoloniferous (*Cynodon* spp.) and a tufted (*Pennisetum purpureum*) grass for discussing experimental work about the extinction coefficient, leaf and stem area development and canopy angular distributions.
