# Maize Cropping Systems Mapping Using RapidEye Observations in Agro-Ecological Landscapes in Kenya
## 6 Conclusions  
This study evaluated the potential of 5-m RapidEye multispectral data and the advanced RF classification technique in mapping maize cropping systems in a complex, dynamic and heterogeneous landscape. To the best of our knowledge, we produced the first cropping patterns map for maize-based cropping systems in Kenya. We conclude that RapidEye imagery acquired during stem elongation and flowering phenological development stages give satisfactory results for separating mono- and mixed maize cropping systems.  
We suggest that data on cropping systems mapping, using high resolution time-series data, are useful baseline information feeds to monitor and understand seasonal cropping pattern changes as a function of climatic variability and climate change in Africa. This information, especially if linked with yields and crop pest and disease infestation levels may be very useful for better agricultural risk projections. Upholding the widely recognized role of small-scale farming for food security in Africa, the extent, distribution and dynamics of cropping systems, as one of the important variables in crop productivity models, should further be investigated. This will result in increased farm sizes hence making it more feasible to monitor cropping systems using freely available remote sensing data sets.  
However, temporal availability of high resolution data is still restricted by the high cost of imagery from semi-commercial sensors such as RapidEye and frequent cloud cover, especially in the tropics. Upscaling the results from this study to wide-area monitoring of cropping patterns is thus still challenging. However, freely available multi-temporal data from Landsat-8 combined with Sentinel 2a and Sentinel 2b data should be further investigated and exploited to improve cropping systems mapping in Africa and beyond. Overall, the relatively accurate classification results obtained in this study provide dependable information that could be used to complement region or field-specific yield data to aid decision making in terms of improved crop productivity and food supply management.  
We gratefully acknowledge the financial support of this research by the following organizations and agencies: the Centre for International Migration and Development (CIM) of the German Development Agency (GIZ); the UK's Department for International Development (DFID); the Swedish International Development Cooperation Agency (Sida); the Swiss Agency for Development and Cooperation (SDC); and the Kenyan Government. We appreciate funding from Germany--Ministry for Economic Cooperation and Development (BMZ) and the German Agency Advisory Service on Agricultural Research for Development GIZ/BEAF for the project \"Better implementation of crop season breaks for management of Maize Lethal Necrosis (MLN) Virus in East Africa--Can remote sensing be an option?\". We are grateful to Julius-Maximilians-University Wurzburg, Department of Remote Sensing for their cooperation and help with data preprocessing. The views expressed herein do not necessarily reflect the official opinion of the donors. Our appreciation also extends to the department of plant health at the International Center for Insect Physiology and Ecology (_icipe_) for helping in field identification and data collection with the help of [PERSON], a visiting student from Germany.  
[PERSON], [PERSON] and [PERSON] designed the study and wrote the paper, [PERSON] was the project entomologist while [PERSON] and [PERSON] processed the data. [PERSON] and [PERSON] guided the project and also revised the final version of the paper.  
The authors declare no conflict of interest.