**Step 7: Estimation of sugarcane productivity**

The sugarcane yield estimation model over the growing season, on a biweekly basis, is accomplished by using an agrometeorological model integrated to ILWIS according to [46]:

$$\mathbf{Y\_e = Y\_p \prod} \mathbf{1} - \mathbf{ky} (\mathbf{1} - \mathbf{ET\_r / ET\_p}) \mathbf{J}$$

**Step 2: Computation of Fractional Vegetation Cover (FVC) from NDVI**

albedo, and other fluxes crucial to land–atmosphere interactions.

**Step 3: Computation of Leaf Area Index (LAI) from FVC**

ration, interception, photosynthesis and respiration.

**Step 4: Computation of growth factor from LAI**

**Step 5: Computation of maximum yield potential (Yp)**

(APF) and production of dry matter (DMP) product.

where Yp is the maximum yield potential (kg ha-1).

product of reference evapotranspiration and the crop coefficient.

FVC= 1.1101\*NDVI – 0.0857.

106 Environmental Change and Sustainability

LAI= –2Ln (1 – FVC).

equation is used:

CGF= 0.515 – e –0.667 – (0.515\*LAI)

Yp=CGF\*BF\*APF\*DMP

ETr

= ETp\* Kc

soil water nutrients are not limiting.

**tion Facility (LSA SAF) ETp product**

For each pixel, the NDVI is converted to Fractional Vegetation Cover (FVC) by means of the the formula of [47]. The FVC is the one biophysical parameter that determines the contribution partitioning between bare soil and vegetation for surface evapotranspiration, photosynthesis,

For each pixel, the FVC is converted to Leaf Area Index (LAI) by means of the formula of [48]. The LAI, defined, as the total one-sided leaf area per unit ground area, is one of the most important parameters characterizing a canopy. Because LAI most directly quantifies the plant canopy structure, it is highly related to a variety of canopy processes, such as evapotranspi‐

[49] developed a simple approach for deriving growth rate equation from LAI. Experimental evidence indicated that the growth rate of several agricultural crop species increases linearly with increasing amounts of LAI, when soil water nutrients are not limiting [46]. The following

where *CGF* = Corrected Growth Factor. Experimental evidence indicated that the growth rate of several agricultural crop species increases linearly with increasing amounts of LAI, when

The final equation that was used to derive maximum yield potential (Yp) includes evaporative fraction corrected growth factor (CGF), respiration factor (BF), agricultural productivity factor

**Step 6: Retrieval of evapotranspiration (ETp) via Land Surface Analysis –Satellite Applica‐**

The crop coefficient is defined as the ratio of crop evapotranspiration, ETr, to reference evapotranspiration, ETp. Kc is crop specific and ranges from zero to over unity, depending on the crop growth stage. Crop evapotranspiration at any time during the growing season is the where *Ye* is the estimated yield (kg ha-1), *Yp* the maximum yield (kg ha-1), *ky* the yield response factor; ET*r* the actual evapotranspiration (mm) and ET*p* the maximum evapotranspiration (mm). Maximum yield (*Yp*) is established by the genetic characteristics of the crop and by the degree of crop adaptation to the environment.

The resulting map of the estimated yield (*Ye*) is clipped to mask the Coruripe municipality boundaries in the State of Alagoas, Brazil. To establish correct coordinates, Map calculation within the ILWIS is used to implement this procedure. Flow diagram of methodology of quantifying sugarcane productivity via satellite products is shown in Figure 12.

**Figure 12.** Flow diagram of methodology of quantifying sugarcane productivity via satellite products.

Figure 12 shows the spatial variations in sugarcane production over the Barretos and Morro Agudo municipalities for 2009/2010 and 2010/2011. The figure clearly indicates high spatial patterns in yield variability. This could be due to the mixing of significant fraction of observed pixels for the "arable pixel" and "non-arable pixel" within the municipalities. The quantified results give sugarcane yield mean range of 50 to 135 Ton ha-1.The results obtained here represents a first step towards an operational use of ILWIS tools in Brazil using NDVI S-10, DMP SPOT and ETo for operational estimating of sugarcane productivity. Overall, the model was able to identify (Figure 12) and quantify (Table 3) the spatial variability of agricultural production over the municipalities analysed. Therefore, the methodology is useful for developing estimates of operational support for the sugarcane productivity [51].

**Figure 13.** Spatial variability of crop yields over the Barretos and Morro Agudo municipalities for 2009/2010 and 2010/2011.


**Table 3.** Comparison between the productivity of sugarcane using an agrometeorological spectral model and harvested crop yield from National Food Supply Company (CONAB).
