## 5 Agro-Climatic Mapping
### Estimation of Crop Potential Areas  
As it is shown in Figure 4, once the range of values of the agro-climatic conditions were defined from the accumulated area curves, we verified, in the different agro-climatic layers (altitude, monthly temperature, annual precipitation, and dominant soil type), that the value of each pixel be within the ranges; if this was the case, we assigned a value of 1; in a contrary case, we assigned a value of 0. Finally, we computed the product among the _binary layers_, obtaining a single layer that represents with a value of 1 the pixels/areas where the agro-climatic conditions are fulfilled. In order to limit the analysis to agricultural/vegetation areas, we sought cropland area maps that could be used as a mask, discarding classes without vegetation: urban, water bodies or any other class having no relation to agriculture. The selected product was _MODIS MCD12Q1 (Land Cover Type Yearly L3 Global 500 m)_, which supplies global maps of land cover at annual time steps and 500 m spatial resolution for 2001 to 2013 (37) (Table 4). The _MCD12Q1_ classification incorporates five different land cover class schemes, derived through a supervised decision-tree classification method [38]. In addition, an assessment of the relative classification quality (scaled from 0-100) is provided at each pixel, along with quality assurance information and an embedded land/water mask. The five classification schemes are as follows:  
1. The International Geosphere Biosphere Programme (IGBP) scheme, in which seventeen land covers were identified, eleven of them being vegetation, three terrain classes and three more \begin{table}
\begin{tabular}{l l} \hline \hline
**Temporal coverage (V051)** & 2001–2013 \\
**Earth-grafted tile area** & \(\sim\)1200 \(\times\) 1200 km (\(\sim\)10\({}^{\circ}\)\(\times\) 10\({}^{\circ}\) at the equator) \\
**Image dimensions** & 2400 \(\times\) 2400 rows/columns \\
**File size** & \(\sim\)88 MB \\
**Resolution** & 500 meters \\
**Projection** & Sinusoidal \\
**Data type** & 8-bit unsigned integer \\
**Data format** & HDF-EOS \\
**Science Data Set (SDS) layers** & 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Dataset characteristics of MCD12Q1. \begin{table}
\begin{tabular}{c} _Eutric Nitosols,_ \\ _Vitric Andosols,_ \\ _Eutric Cleysols_ \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ranges of values of agro-climatic conditions estimated for the selected crops in Costa Rica. vegetation-free classes. Their stated objective was to provide a global land cover dataset that was more up-to-date, of known accuracy and with higher spatial resolution and greater internal consistency than any other existing dataset. The scheme is based on definitions of three canopy components: above-ground biomass, leaf longevity, and leaf type process. The land-cover categories identified by the IGBP are related to the needs of gas exchange studies; vegetation attributes for modeling Net Primary Production (NPP); burn emissions and gas exchange; wetlands cover and wetland water regimes; changes in vegetation/land-cover over time; biological attributes; physical attributes, and landscape characteristics [38; 39; 40; 41]. 2. The University of Maryland land cover classification (UMD) dataset, with fourteen classes, two of which have no vegetation. The approach taken involved a hierarchy of pairwise class trees where a logic based on vegetation form was applied until all classes were depicted. Minimum annual red reflectance, peak annual Normalized Difference Vegetation Index (NDVI), and minimum channel three brightness temperature were among the most used multitemporal metrics. Depictions of forests and woodlands, and areas of mechanized agriculture are in general agreement with other sources of information, while classes such as low biomass agriculture and high-latitude broadleaf forest are not [8]. 3. The LAI/FPAR scheme, with nine vegetation classes and two vegetation- free ones.