Derivation of temporal windows for accurate crop discrimination in heterogeneous croplands of Uzbekistan using multitemporal RapidEye images

• We tested multitemporal RapidEye data for crop mapping in Khorezm, Uzbekistan.
• Overall accuracy levelled at 85% when using five temporal windows or more.
• Transition phases between crop growing seasons are relevant for crop mapping.
• Accurate crop maps can be derived before main irrigation phases in Khorezm.
• NDVI temporal profiles show potentials for classifying different crop classes.

Recently launched and upcoming satellite systems are expected to make crop mapping increasingly accurate and operational. This study aims at an evaluation of the optimum number of acquisition dates and most suitable temporal windows for the discrimination of crops over heterogeneous agricultural landscapes utilizing multitemporal data from the RapidEye satellite constellation. For a case study site in West-Uzbekistan, which covers 230,000 ha of irrigated land, nine 6.5 m RapidEye mosaics were obtained throughout the vegetation period of 2009. The acquisition window of one mosaic was maximum five days. The optimum number of acquisition dates and acquisition windows relevant for accurate crop identification was assessed by applying a random forest algorithm to all possible combinations of the available RapidEye mosaics.Overall accuracy increased with the number of acquisition dates selected for the classification. Classifying all nine mosaics resulted in an overall accuracy of 85.7%. This accuracy level could be already achieved by using five or more acquisitions during distinctive phases of the vegetation period, among others short before winter-wheat harvest and in initial growing phases of summer crops. It could be shown that accurate early season crop maps at field level can be derived enabling water managers to update water distribution schedules in the study region. All 53,302 fields of the irrigation system including very small parcels with a size of 0.05 ha could be classified due to the high geometric resolution of RapidEye. Detailed NDVI temporal profiles demonstrated the potential of RapidEye for including minor crops into classifications of heterogeneous agricultural areas. Class-wise accuracy pointed to the challenge of classifying sub-tree cultivation and indicated that classification of minor crops could also depend on the spatial resolution of satellite images. The case study suggests identifying temporal windows for crop mapping serving as substantial support for land and water management in different agro-environmental settings.

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