# Maize Cropping Systems Mapping Using RapidEye Observations in Agro-Ecological Landscapes in Kenya
3, 53113 Bonn, Germany; [EMAIL_ADDRESS]  
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[PERSON]  
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[PERSON]  
4 Department of Remote Sensing, University of Wurzburg, Oswald-Kulpe-Weg 86, 97074 Wurzburg, Germany; [EMAIL_ADDRESS] (M.T. ); [EMAIL_ADDRESS] (H.J.) 1  
[PERSON]  
4 Department of Remote Sensing, University of WurzburgThe cropping system is defined as the planting sequence of crops applied to an agricultural area or field over a certain period. In agronomical terms, an agricultural field can be mono-cropped, inter-cropped, relay cropped, mixed-cropped or under crop rotation (i.e., planting different crops in sequential years) [5]. Mixed-cropping is a common practice on small-scale farms in developing countries like Kenya. In Kenya, maize (_Zea mays_ L.) is the staple food, and it is common to find it mixed with bean (_Phaseolus vulgaris_ L.) [6]. The degree of mixed cropping is often determined by the need for diversification against a backdrop of increased climate variability and the need to increase soil fertility and soil moisture regimes to sustain or increase crop productivity [7]. In this study, we define mixed cropping specifically as maize grown in a spatial arrangement with other leguminous crops on the same field within the same growing season and mono-cropping as maize grown as a single crop within the same time frame and field. High and medium spatial resolution satellite data have been widely used for agricultural land use mapping in different agro-ecological zones in Africa and beyond [8; 9]. However, many studies alluded to the challenges of accurately mapping crops and cropping systems in Africa on a landscape scale primarily due to the small scale and highly fragmented nature of cropping patterns as well as their intra- and inter-annual dynamics [10]. The temporal and spatial high variability of cropping systems is often a result of incoherent farmers decisions (the planting date often varies from one season or year to the next) and other localized and hard to quantify socio-economic factors [11]. Moreover, rain fed crops are largely indiscriminate from some natural vegetation communities such as grassland during the wet or growing season when both (the crops and some natural vegetation types) have the same phenological growing cycles [12]. Thus, landscape scale crop mapping mechanisms using medium resolution data such as 30 m Landsat, a frequently used type of dataset for crop mapping in Africa [13], has resulted in high spectral heterogeneity and poor mapping results [14]. Essentially, remotely sensed data can provide spatially coherent information only on crop acreage and crop vitality on landscape scales with an advantage over traditional conventional surveying methods that are often tedious and costly (ineffective), especially if crop assessments are performed over larger areas [15]. Relatively newly available 5 m RapidEye data are suitable for crop mapping in highly fragmented and dynamic landscapes because of the higher and enhanced geometrical resolution of the satellite system, particularly in small-scale farming systems in Africa where the size of field is relatively small (\(\leq\)1.25 ha) [8; 10; 16]. The enhanced spectral resolution of RapidEye data in the red-edge waveband domain, for instance, allows for significantly enhanced land use classification and improved crop discrimination. This could be due to strong correlations between the vegetation spectral features at the red-edge band and chlorophyll content, and the sensitivity of the red-edge band to differences in leaf structure [17; 18; 19]. Combined with state-of-the-art and hierarchical classification approaches using robust machine learning classification algorithms and RapidEye data from different time steps, including their derived vegetation indices, explicit and permissible accurate crop type mapping results even in complex African landscapes can be generated [8; 10]. Various types of non-parametric machine learning classification methods like random forest (RF) have been successfully applied to mapping crops in Africa [8; 10]. [PERSON] et al.