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Proceedings Paper

Potentials of RapidEye time series for improved classification of crop rotations in heterogeneous agricultural landscapes: experiences from irrigation systems in Central Asia
Author(s): Christopher Conrad; Miriam Machwitz; Gunther Schorcht; Fabian Löw; Sebastian Fritsch; Stefan Dech
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Paper Abstract

In Central Asia, more than eight Million ha of agricultural land are under irrigation. But severe degradation problems and unreliable water distribution have caused declining yields during the past decades. Reliable and area-wide information about crops can be seen as important step to elaborate options for sustainable land and water management. Experiences from RapidEye classifications of crop in Central Asia are exemplarily shown during a classification of eight crop classes including three rotations with winter wheat, cotton, rice, and fallow land in the Khorezm region of Uzbekistan covering 230,000 ha of irrigated land. A random forest generated by using 1215 field samples was applied to multitemporal RapidEye data acquired during the vegetation period 2010. But RapidEye coverage varied and did not allow for generating temporally consistent mosaics covering the entire region. To classify all 55,188 agricultural parcels in the region three classification zones were classified separately. The zoning allowed for including at least three observation periods into classification. Overall accuracy exceeded 85 % for all classification zones. Highest accuracies of 87.4 % were achieved by including five spatiotemporal composites of RapidEye. Class-wise accuracy assessments showed the usefulness of selecting time steps which represent relevant phenological phases of the vegetation period. The presented approach can support regional crop inventory. Accurate classification results in early stages of the cropping season permit recalculation of crop water demands and reallocation of irrigation water. The high temporal and spatial resolution of RapidEye can be concluded highly beneficial for agricultural land use classifications in entire Central Asia.

Paper Details

Date Published: 7 October 2011
PDF: 9 pages
Proc. SPIE 8174, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII, 817412 (7 October 2011); doi: 10.1117/12.898345
Show Author Affiliations
Christopher Conrad, Julius-Maximilians-Univ. Würzburg (Germany)
Miriam Machwitz, Zentrum für Entwicklungsforschung (Germany)
Gunther Schorcht, Julius-Maximilians-Univ. Würzburg (Germany)
Fabian Löw, Julius-Maximilians-Univ. Würzburg (Germany)
Sebastian Fritsch, Julius-Maximilians-Univ. Würzburg (Germany)
Zentrum für Entwicklungsforschung (Germany)
Stefan Dech, Julius-Maximilians-Univ. Würzburg (Germany)
Deutsches Zentrum für Luft- und Raumfahrt e.V. (Germany)


Published in SPIE Proceedings Vol. 8174:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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