# Maize Cropping Systems Mapping Using RapidEye Observations in Agro-Ecological Landscapes in Kenya
## 5 Discussion  
The classification results from this study demonstrated the usefulness of the bi-temporal RapidEye imagery and RF classification tool for mapping the two major maize cropping systems in heterogeneous agro-ecological landscapes. This demonstrates the capability of high spatial resolution data with better spectral coverage such as RapidEye to distinguish different cropping systems, given both the size and shape of fields in African agro-ecological systems. The two images could capture the spectral (phenological) profiles of the two cropping systems thus resulting in a better discriminatory power between the two cropping systems [53]. In addition, RF selected NDVI from the first RapidEye acquisition (RE1) among the most significant variable in separating the two cropping systems. This could be due to the fact that at the stem elongation phenological development stage, mono- and mixed cropping systems are easily distinguishable, while at the flowering phenological growth stage the two cropping systems seem to have similar morphological and spectral properties [54]. We observed that the most important spectral indices selected by RF from the two acquisitions are commonly related to plant biophysical properties such as Leaf Area Index (LAI) and net primary productivity [55]. It is expected that mono- and mixed maize cropping systems can differ considerably in these traits since mono-cropped maize is known to exhibit a lower LAI, especially during the flowering stage, than maize mixed with legumes (among other factors, largely due to the absence of bare soil) [56]. It is interesting to note that the RE2_NDVI was not amongst the selected most important variables. That could be due to fact that the second RE2 acquisition corresponded to the maize flowering stage in which the spectral contributions are not overly characterized by chlorophyll activities (NDVI) but more by spectral contributions from non-chlorophyll plant components, i.e., the cobs, wilting leaves and the maize flowers. Moreover, the RF backward variable selection process showed that the inclusion of the red-edge bands and spectral indices that use the red-edge bands (i.e., DVI-edge) were relevant for the maize cropping systems mapping result. Similarly, [PERSON] et al. [18] found that the red-edge bands improved land use classification by 2.7%. The spatial cropping system differences we observed between the low and high altitude areas (Figure 6) could be due to more favorable climatic conditions for crop production within mountainous regions as single (mono) crop production is more feasible in upland areas that receive higher rainfall [57]. Farmers in drier areas often opt to combine maize with leguminous crops on the same field (mixed cropping) to improve soil fertility and soil moisture in order to attain permissible yields [58]. The crop systems patterns also showed considerable differences in field size between the lower and higher lying areas within the study area (approximately 0.8 ha in the low altitudinal areas versus an average field size of 0.2 ha in the higher lying areas). These field size differences could be confirmed from the field observations. The crop system mapping results indicated comparatively high OA of 85.7% and individual class accuracies (PA and UA) > 84% [59]. The high accuracies could be due to, primarily, the optimal acquisition dates of the imagery, i.e., during the stem elongation and flowering crop development stages, respectively. These critical crop stages are known to produce better separation between agricultural fields and surrounding natural vegetation [60]. Specific confusions between the two \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Mono Cropping** & **Mixed Cropping** & **Total** & **UA (\%)** \\ \hline Mono maize cropping & 486 & 74 & 560 & 84.97 \\ Mixed maize cropping & 86 & 474 & 560 & 86.50 \\ Total & 572 & 548 & 1120 & \\ PA (\%) & 86.79 & 84.64 & & \\ OA (\%) & 85.71 & & & \\ QD (\%) & 1.00 & & & \\ AD (\%) & 13.00 & & & \\ \hline \hline \end{tabular}
\end{table}