**6. Semi-empirical modelling of satellite data (vegetation indices) to ground data (crop canopy parameters)**

The commonly accepted equation for estimating evapotranspiration, according to the sche‐ matization of Monteith (Monteith and Unsworth, 1990), is a function of climate data such as temperature (T), humidity (RH%), solar radiation (Rs) and wind speed (U) and crop parame‐ ters, such as the surface albedo (*a*)*,* the leaf area index (LAI) and the crop height (CH):

$$\text{ETc} = f\left(a, LAI, \text{ch}, T, RH\%, Rs, Ll\right) \tag{1}$$

Remote sensing techniques can be used for monitoring these vegetation characteristics. An analytical elaboration performed on Landsat reflectance values evidenced the possibility of retrieving the surface albedo (Brest and Goward, 1987), the leaf area index (Price, 1992) and the crop height (Moran and Jackson, 1991). Since these parameters directly affect the reflec‐ tance of cropped areas, it has been demonstrated that it is possible to establish a correlation between multispectral measurements of canopy reflectance and the corresponding canopy parameter's values (Bausch and Neale, 1987). In this study, the required crop parameters, *a,* LAI, CH have been derived from direct measurements and were correlated to reflectance measurements of the crops in each case.

Many studies have illustrated the need and the know-how for modeling or correlating LAI and Crop Height to remote sensing data and mainly to the vegetation indices inferred from handheld sensors. Leaf Area Index is an important structural property of crop canopy. High correlations were found between reflectance factor and LAI by Ahlrichs et al., (1983). Strong correlations between spectral data from crops and various characteristics of crops have been elucidated in numerous studies (Serrano et al, 2000; Goel et al., 2003; Lee et al., 2004). Dar‐ vishzadeh et al., (2008) examined the utility of hyper spectral remote sensing in predicting canopy characteristics by using a spectral radiometer. Among the various models investigat‐ ed, they found that canopy chlorophyll content was estimated with the highest accuracy. Some studies used multispectral image sensor system to measure crop canopy characteris‐ tics (Inoue et al., 2000)

Spectroradiometric data

Vegetation

Indices LAI/CH

Flow Cart of Modelling LAI/CH to Vegetation Indices

Modelling

LAI/CH MAPS

Evaluation of models

SEBAL is a thermodynamically based model, using the partitioning of sensible heat flux and latent heat of vaporization flux as described by Bastiaanssen et al., (1998) who developed the algorithm. In the SEBAL model, ETc is computed from satellite images and weather data us‐ ing the surface energy balance as illustrated in Figure 9. Remotely sensed data in the visible, near-infrared and thermal infrared bands are used to derive the energy balance components along with ground measured solar radiation, if available. The other ground measurements

**Figure 8.** Method for mapping LAI and Crop Height using in situ and satellite remotely data

**7. Algorithms application and results**

**7.1. SEBAL algorithm**

Ground measurements http://dx.doi.org/10.5772/39305

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Remote Sensing for Determining Evapotranspiration and Irrigation Demand for Annual Crops

Quantification of the canopy leaf area index (LAI) and its spatial distribution provides (Figure 7) an avenue to improve the interpretation of remotely sensed data over vegetat‐ ed areas. The purpose is to test the existing relation between vegetation indices with LAI and crop height and their prediction from remotely sensed data. It allow us to compare, on a consistent basis, the performance of a set of indices found in international litera‐ ture, in the prediction of LAI and CH which are basic parameters in the algorithms of estimating ETc. The method for mapping LAI and Crop Height for specific crops is shown in Figure 8.

**Figure 7.** Production of LAI (B) and CH (C) maps (in pseudo color) using a Landsat image (A) (Papadavid, 2011)

**Figure 8.** Method for mapping LAI and Crop Height using in situ and satellite remotely data
