# Maize Cropping Systems Mapping Using RapidEye Observations in Agro-Ecological Landscapes in Kenya
## 4 Results
### Classification Accuracies  
To understand the accuracies of the cropping systems map (level 2 classification) that uses the cropland mask extracted from LULC result (level 1 classification), the accuracies of LULC map are herein reported. The main results of the accuracy assessment for the hierarchical level 1 LULC classification are summarized in Table 2 for the three different combinations which included the RE bands, the RE band with all vegetation indices and RF selected spectral variables (bands and vegetation indices). The most accurate LULC mapping result was obtained from the most important selected vegetation indices and spectral bands listed in Figure 4 using RF backward selection criteria with an overall accuracy of 93.2% and a kappa coefficient of 0.91. Table 3 presents the per-pixel evaluation confusion matrix for the LULC map. Individual accuracies (PA and UA) were consistently over 87% with the F1-score averagely above 0.88 for all classes (Table 3). This suggests a very good concealment of croplands from other LULC classes regardless of cropland being slightly confused with the natural vegetation class.  
Table 4 summarizes classification accuracies for three classifications calibrated for mapping the two maize cropping systems (level 2 classification). The optimized RF cropping system mapping result using only the most relevant spectral variables selected by RF gave an overall accuracy of 85.7% (kappa coefficient of 0.84) whereas the non-optimized result, using all RapidEye bands and the vegetation indices, gave a lower overall accuracy of 73.4%. In addition, individual PA and UA for the optimized RF result (Table 5) were consistently above 84% for both classes with a low QD score of 1% and a relatively high AD score of 13% for both cropping systems, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Analysis** & **Overall Accuracy (\%)** & **Kappa Coefficient** \\ \hline RE (bands) & 80.24 & 0.77 \\ RE (bands) + All RE\_veg indices & 73.38 & 0.70 \\ RF selected spectral variables & 85.71 & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overall accuracies and kappa coefficient of agreement for the two maize mono- and mixed cropping systems.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Analysis** & **Overall Accuracy (\%)** & **Kappa Coefficient** \\ \hline RE (bands) & 87.46 & 0.86 \\ RE (bands) + All RE\_veg indices & 86.41 & 0.84 \\ RF selected spectral variables & 93.20 & 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overall accuracies and kappa coefficient of agreement for the Land Use Land Cover classification.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **Artificial** & **Corpland** & **Natural Vegetation** & **Water Bodies** & **Total** & **UA (\%)** & **F1 Score** \\ \hline Artificial Surface & 904 & 23 & 0 & 18 & 945 & 96.48 & 0.96 \\ Cropland & 11 & 845 & 89 & 0 & 945 & 87.84 & 0.89 \\ Natural Vegetation & 0 & 94 & 851 & 0 & 945 & 90.53 & 0.90 \\ Water Bodies & 22 & 0 & 0 & 923 & 945 & 98.09 & 0.98 \\ Total & 937 & 962 & 940 & 941 & 3780 & \\ PA (\%) & 95.66 & 89.42 & 90.05 & 97.67 & & \\ OA (\%) & 93.20 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Random forest classification confusion matrix for the land use/land cover classes (level 1) using the 15 most important RapidEye spectral variables and 30% of the reference data.