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

Sensors
Kyalo RichardTobias Landmann

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 f...Continue Reading

References

Jan 20, 2004·Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences·Johan Rockström
Jul 16, 2008·BMC Bioinformatics·Carolin StroblAchim Zeileis
May 25, 2012·TheScientificWorldJournal·J RomeoA Ribeiro
Dec 4, 2012·Journal of Environmental Management·Elizabeth BryanMario Herrero

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Citations

Nov 5, 2020·The Science of the Total Environment·Bester Tawona MudereriTobias Landmann

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Software Mentioned

Decision Support System for Agrotechnology Transfer ( DSSAT )
R
RapidEye
ATCOR3 )
varSelRF

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