# Maize Cropping Systems Mapping Using RapidEye Observations in Agro-Ecological Landscapes in Kenya
## 3 Methodology
### Image Acquisition and Preprocessing  
Two RapidEye images were acquired for the study area on the 3 January 2015 and the 27 January 2015, during the maize stem elongation (RE1) and flowering (RE2) crop phenological development stages, respectively. These two maize phenological development stages are characterized using the BBCH (Biologicale Bundesanstalt, Bundessortenamt und Chemische Industrie) scale [26]. The images were acquired at two different acquisition windows to assess the effect of crop phenology (the crop life cycle) on mapping cropping systems with an assumption that the spectral features of the crops (mainly bean and cowpea) that are mainly planted with maize are distinguishable during the maize flowering stage.  
RapidEye provides images with spatial resolution of 5 m and five spectral bands (wavelength regions) which are located at blue (440-550 nm), green (520-590 nm), red (630-685 nm), red-edge (690-730 nm) and near infrared (760-850 nm) regions of the electromagnetic spectrum. The RapidEye Ortho product (Level 3A) was utilized. To retrieve surface reflectance, atmospheric correction was applied using the atmospheric-topographic correction (ATCOR3) software to remove haze and other atmospheric interferences. ATCOR3 is an extension of model ATCOR2 that permits extended  
Figure 2: Flow chart of the hierarchical classification approach using random forest (RF) classifier and Bi-temporal RapidEye data.  
three-dimensional topographic corrections by inclusion of digital elevation model (DEM) data to remove illumination difference due to topography effects [27]. Parameters, such as satellite azimuth, illumination elevation, azimuth and incidence angle, which are used for atmospheric corrections, were obtained from respective metadata files for each image. To reduce illumination effects caused by the terrain on RapidEye imagery, topographic corrections were performed using Shuttle Radar Topographic Mission (SRTM) digital elevation (DEM) data. The 30 m SRTM DEM data, used in topographic correction processes, were re-sampled to 5 m pixel resolution using a bilinear interpolation technique. Due to different date and time acquisitions of each tile in the RapidEye mosaic, a tile specific stepwise normalization technique using multivariate alteration detection (IMAD) was used to normalize the tiles via a central normalization reference tile. The image data sets were geo-referenced to Universal Transverse Mercator (UTM, zone 36 south).  
Subsequently, mosaicking was applied on all the co-registered normalized tiles and two mosaics having a size of \(\sim\)61 by 61 km each were produced initially. To align all the corresponding pixels, the two mosaicked images were co-registered to each other using image-to-image co-registration to ascertain the alignment of corresponding pixels. Finally, regions that were covered by clouds have been masked out. We utilized 30 vegetation indices calculated from each RapidEye data set (Table 1) together with the respective five RapidEye bands as input into the RF classification algorithm. The inclusion of vegetation indices that are related to vegetation biochemical and biophysical traits like chlorophyll activity and leaf area index, together with the individual spectral bands, has proved to significantly improve crop classification accuracies in heterogeneous landscapes [10].