1. Introduction Total population of the Kenya at 2014 was 45.5 Million individuals that the share of rural and urban area was 74.4% and 25.6% respectively. Within the crop income category, maize with horticultural crops, covering 14% of total household income in Kenya. At the same time, coffee and tea account for a combined 5.6% of total gross income. In the mid-1970s maize accounted for about 35% of the value of total crop production in Kenya while in the late 1990s, it reduced to 28% of the value of total crop production (Olwande, Sikei et al. 2009). Since 2008 the area under cereal production increased ~ 647,762 ha and the cereal production reached 1,446,328 tonnes. Maize farming is the backbone of food security in Kenya. Maize fields increased from 1,700,000 ha at 2008 to 2,159,322 ha at 2012, which is the highest since 1961. Amid 2008 and 2012, maize production increased 1,382,643 tonnes. At 2014 around Kenya harvested 3,513,171 tonnes maize although it An Investigation into Maize Lethal Necrosis Severity Mapping in Kenya using RapidEye and Landsat-8 Hosein Jafari Jozani (H.J.), Michael Thiel (M.T.), Michael Hahn (M.H.), Kyalo Richard (K.R.), and Tobias Landmann (T.L.) Department of Remote Sensing, University of Würzburg, Oswald-Külpe-Weg 86, 97074 Würzburg, Germany; michael.thiel@uni-wuerzburg.de (M.T.) Department of Geomatics, University of Applied Science Stuttgart (HFT), Schellingstraße 24, 70174 Stuttgart, Germany; michael.hahn@hft-stuttgart.de (M.H.); hoseinjafary@aol.com (H.J.) International Center for Insect Physiology and Ecology (ICIPE), P.O. Box 30772, 00100 Nairobi, Kenya; rkyalo@icipe.org (K.R.); Center for Development Research (ZEF), Department of Ecology and Natural Resources Management, University of Bonn, Walter-Flex-Str. 3, 53113 Bonn, Germany; cb@uni-bonn.de Abstract The spatial information of crops and cropping systems can provide useful information about disease outbreak mechanism in croplands. In September 2011, a severe outbreak of Maize Lethal Necrosis (MLN) reported at lower elevations (1,900 meter above sea level) in Southern Rift Valley, Longisa division of Bomet County, Kenya. The heterogeneous small scale farms and cloud cover in study area are some of challenges against application of remote sensing in this region. Aims of this study are; to classify maize fields, to discriminate mono/intercropping, continuous/rotation cropping systems, to identify severe MLN occurrence and finally to determine if any relevance between severe MLN occurrences with cropping system and ecological variables exist. Infield data collection was accomplished by African insect for food and health (icipe) (December 2014, January and August 2015), which provided GPS data, crop type, crop conditions (Type, Physical condition, growth stage, and MLN severity), cropping system (mono/inter cropping) and crop rotation (continuous/rotation). High-resolution RapidEye (RE) images, medium resolution Synthetic Aperture RADAR (SAR) Sentinel-1 (S1), Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) of 30 meter resolution and 12-monthes of Landsat-8 (L8) images were acquired. Several vegetation indices were calculated from RE and L8 adopted for classification. The Random Forest (RF) and One Class Classifier (OCC) were performed in the absence of complete and representative validation data of Land Cover Land Use (LCLU) samples, and the results compared. The RE and L8 images covered two cropping seasons. Also Normalized Difference Vegetation Index (NDVI) in regional-level time series calculated out of 12 L8 images to derive and analysed. As first step, RF classifier employed to produce a LULC map. Secondly, mono and intercropping maize fields identified via maize fields reclassification. The third step, severe MLN classified via adopting OCC classifier. As fourth step, cropping system determined (continuous/rotational) through applying raster analysis. Finally the relationship among cropping system, ecological variables and MLN severity was investigated if any. Key words: Cropping systems, RF, OCC, MLN severity, NDVI, Ecological variables. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 4 May 2018 doi:10.20944/preprints201805.0095.v1 © 2018 by the author(s). Distributed under a Creative Commons CC BY license.