@article{RichardAbdelRahmanSubramanianetal.2017,
  author    = {Richard, Kyalo and Abdel-Rahman, Elfatih M. and Subramanian, Sevgan and Nyasani, Johnson O. and Thiel, Michael and Jozani, Hosein and Borgemeister, Christian and Landmann, Tobias},
  title     = {Maize cropping systems mapping using RapidEye observations in agro-ecological landscapes in Kenya},
  series = {Sensors},
  volume    = {17},
  journal   = {Sensors},
  number    = {11},
  doi       = {10.3390/s17112537},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-173285},
  year      = {2017},
  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.},
  language  = {en}
}
@article{ForkuorConradThieletal.2014,
  author    = {Forkuor, Gerald and Conrad, Christopher and Thiel, Michael and Ullmann, Tobias and Zoungrana, Evence},
  title     = {Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa},
  doi       = {10.3390/rs6076472},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-113070},
  year      = {2014},
  abstract  = {Crop mapping in West Africa is challenging, due to the unavailability of adequate satellite images (as a result of excessive cloud cover), small agricultural fields and a heterogeneous landscape. To address this challenge, we integrated high spatial resolution multi-temporal optical (RapidEye) and dual polarized (VV/VH) SAR (TerraSAR-X) data to map crops and crop groups in northwestern Benin using the random forest classification algorithm. The overall goal was to ascertain the contribution of the SAR data to crop mapping in the region. A per-pixel classification result was overlaid with vector field boundaries derived from image segmentation, and a crop type was determined for each field based on the modal class within the field. A per-field accuracy assessment was conducted by comparing the final classification result with reference data derived from a field campaign. Results indicate that the integration of RapidEye and TerraSAR-X data improved classification accuracy by 10\%-15\% over the use of RapidEye only. The VV polarization was found to better discriminate crop types than the VH polarization. The research has shown that if optical and SAR data are available for the whole cropping season, classification accuracies of up to 75\% are achievable.},
  language  = {en}
}