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Sensors 2017, 17(11), 2537; doi:10.3390/s17112537

Maize Cropping Systems Mapping Using RapidEye Observations in Agro-Ecological Landscapes in Kenya

1
International Center for Insect Physiology and Ecology (ICIPE), P.O. Box 30772, 00100 Nairobi, Kenya
2
Department of Agronomy, Faculty of Agriculture, University of Khartoum, Khartoum North 13314, Sudan
3
Crop Health Unit, Kenya Agricultural and Livestock Research Organization, Embu Research Centre, P.O. Box 27, 60100 Embu, Kenya
4
Department of Remote Sensing, University of Würzburg, Oswald-Külpe-Weg 86, 97074 Würzburg, Germany
5
Center for Development Research (ZEF), Department of Ecology and Natural Resources Management, University of Bonn, Walter-Flex-Str. 3, 53113 Bonn, Germany
*
Author to whom correspondence should be addressed.
Received: 29 August 2017 / Revised: 27 September 2017 / Accepted: 20 October 2017 / Published: 3 November 2017
(This article belongs to the Section Remote Sensors)
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Abstract

Cropping systems information on explicit scales is an important but rarely available variable in many crops modeling routines and of utmost importance for understanding pests and disease propagation mechanisms in agro-ecological landscapes. In this study, high spatial and temporal resolution RapidEye bio-temporal data were utilized within a novel 2-step hierarchical random forest (RF) classification approach to map areas of mono- and mixed maize cropping systems. A small-scale maize farming site in Machakos County, Kenya was used as a study site. Within the study site, field data was collected during the satellite acquisition period on general land use/land cover (LULC) and the two cropping systems. Firstly, non-cropland areas were masked out from other land use/land cover using the LULC mapping result. Subsequently an optimized RF model was applied to the cropland layer to map the two cropping systems (2nd classification step). An overall accuracy of 93% was attained for the LULC classification, while the class accuracies (PA: producer’s accuracy and UA: user’s accuracy) for the two cropping systems were consistently above 85%. We concluded that explicit mapping of different cropping systems is feasible in complex and highly fragmented agro-ecological landscapes if high resolution and multi-temporal satellite data such as 5 m RapidEye data is employed. Further research is needed on the feasibility of using freely available 10–20 m Sentinel-2 data for wide-area assessment of cropping systems as an important variable in numerous crop productivity models. View Full-Text
Keywords: RapidEye; bi-temporal; cropping systems; random forest; Kenya RapidEye; bi-temporal; cropping systems; random forest; Kenya
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MDPI and ACS Style

Richard, K.; Abdel-Rahman, E.M.; Subramanian, S.; Nyasani, J.O.; Thiel, M.; Jozani, H.; Borgemeister, C.; Landmann, T. Maize Cropping Systems Mapping Using RapidEye Observations in Agro-Ecological Landscapes in Kenya. Sensors 2017, 17, 2537.

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