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首页> 外文会议>Remote Sensing for Agriculture, Ecosystems, and Hydrology >Potentials of RapidEye time series for improved classification of crop rotations in heterogeneous agricultural landscapes: Experiences from irrigation systems in Central Asia
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Potentials of RapidEye time series for improved classification of crop rotations in heterogeneous agricultural landscapes: Experiences from irrigation systems in Central Asia

机译：Rapideye时间序列改善异构农业景观中作物旋转分类的潜力：中亚灌溉系统的经验

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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 85percent for all classification zones. Highest accuracies of 87.4percent 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.
机译：在中亚，超过八万公顷的农业用地灌溉。但严重的退化问题和不可靠的水分布在过去几十年中导致产量下降。有关作物的可靠和领域广泛的信息可以视为详细阐述可持续土地和水管理选择的重要步骤。中亚作物分类的经验在八个作物课程的分类期间示例性地显示，其中包括冬小麦，棉花，米饭和乌兹别克斯坦的Khorezm地区的三个轮落，占地230,000公顷的灌溉土地。通过使用1215个域样品产生的随机森林被应用于在2010年植被期间获得的多发性缩醛数据。但是Rapideye覆盖范围的变化，并且不允许在覆盖整个区域的时间上产生时间上一致的马赛克。为了分类区域中的所有55,188个农业包裹，分别分类。分区允许将至少三个观察期分类为分类。所有分类区域的总体精度超过8555。 87的最高精度通过包括raphideye的五种时滞复合材料来实现。 Class-Wise的精度评估显示了选择时间步骤的有用性，其代表植被期的相关酚类阶段。提出的方法可以支持区域作物库存。准确的分类导致农业季节早期阶段允许重新计算作物水需求和灌溉水分分配。 Rapideye的高时和空间分辨率可以在整个中亚的农业土地使用分类方面结束。

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