# CROpland Mapping Using Earth Observation Derived Phenological Metrics +
## 2 Materials and Methods
### Time Series and Temporal Predictors  
Two vegetation indices were selected to derive the main crop types in the study area: the _NDVI_ and the LAI. The _NDVI_ was calculated following Equation (1):  
\[NDVI=\frac{(NIR-RED)}{(NIR+RED)} \tag{1}\]  
where \(RED\) corresponds to the S2 MSI spectral band \(B4\) and \(NIR\) corresponds to the S2 MSI spectral band \(B8\). The Leaf Area Index (LAI) is defined as half of the total green (i.e., photosynthetically active) leaf area per unit of horizontal ground surface area. The biophysical processor [16] available in SNAP software was used to estimate the LAI from the surface reflectance data.  
The time series of the vegetation indices were first gap-filled and interpolated daily using the Stinemann algorithm [17], and later temporally smoothed using the procedure based on second-order weighted polynomial fitting and Whittaker smoothing, as described in [18]. From the NDVI and LAI time series, temporal statistics and phenological metrics, derived following [PERSON] et al. [19], were calculated and used as temporal predictors in the classification model (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Description** & \multicolumn{2}{c}{**Unit**} & \multicolumn{2}{c}{**Importance**} \\ \hline  & & **NDVI** & **LAI** & **NDVI** & **LAI** \\ \hline avg & Annual average value & dl & m\({}^{2}\)/m\({}^{2}\) & 25,089.92 & 1910.118 \\ \hline std & Standard deviation value & dl & m\({}^{2}\)/m\({}^{2}\) & - & 4332.682 \\ \hline min & Annual minimum value & dl & m\({}^{2}\)/m\({}^{2}\) & - & 3486.984 \\ \hline max & Annual maximum value & dl & m\({}^{2}\)/m\({}^{2}\) & - & - \\ \hline delta & Delta value & dl & m\({}^{2}\)/m\({}^{2}\) & - & - \\ \hline djf\_avg & Winter (December, January, February) average value & dl & m\({}^{2}\)/m\({}^{2}\) & - & - \\ \hline djf\_min & Winter (December, January, February) minimum value & dl & m\({}^{2}\)/m\({}^{2}\) & - & - \\ \hline djf\_max & Winter (December, January, February) maximum value & dl & m\({}^{2}\)/m\({}^{2}\) & - & - \\ \hline jja\_avg & Summer (June, July, August) average value & dl & m\({}^{2}\)/m\({}^{2}\) & 26,029.03 & 2817.965 \\ \hline jja\_max & Summer (June, July, August) maximum value & dl & m\({}^{2}\)/m\({}^{2}\) & 24,610.40 & - \\ \hline SoS\_doy & Start of season DoY & DoY & DoY & - & 1890.944 \\ \hline SoS\_value & Start of season value & dl & m\({}^{2}\)/m\({}^{2}\) & - & - \\ \hline SGS\_doy & Start of growing season DoY & DoY & DoY & - & 2243.904 \\ \hline SGS\_value & Start of Growing Season & dl & m\({}^{2}\)/m\({}^{2}\) & 23,572.20 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of time series statistics and phenological metrics used as model predictors. The predictors’ importance resulting from the classification model is expressed as the Gini index value. Variables with no important values were not selected as model predictors. The abbreviation ’dl’ stands for ’dimensionless’.  
All predictors with a Pearson correlation coefficient higher than 0.9 and a variance inflation factor (VIF) higher than 2.0 [20] were removed to avoid multi-collinearity.