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Mapping Paddy Rice Using Sentinel-1 SAR Time Series in Camargue, France

机译:在法国卡玛格使用Sentinel-1 SAR时间序列绘制水稻图

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This study proposes an effective method to map rice crops using the Sentinel-1 SAR (Synthetic Aperture Radar) time series over the Camargue region, Southern France. First, the temporal behavior of the SAR backscattering coefficient over 832 plots containing different crop types was analyzed. Through this analysis, the rice cultivation was identified using metrics derived from the Gaussian profile of the VV/VH time series (3 metrics), the variance of the VV/VH time series (one metric), and the slope of the linear regression of the VH time series (one metric). Using the derived metrics, rice plots were mapped through two different approaches: decision tree and Random Forest (RF). To validate the accuracy of each approach, the classified rice map was compared to the available national data. Similar high overall accuracy was obtained using both approaches. The overall accuracy obtained using a simple decision tree reached 96.3%, whereas an overall accuracy of 96.6% was obtained using the RF classifier. The approach, therefore, provides a simple yet precise and powerful tool to map paddy rice areas.
机译:这项研究提出了一种使用Sentinel-1 SAR(合成孔径雷达)时间序列在法国南部Camargue地区绘制水稻作物的有效方法。首先,分析了包含不同作物类型的832个样地上SAR反向散射系数的时间行为。通过此分析,使用从VV / VH时间序列的高斯分布(3个度量),VV / VH时间序列的方差(一个度量)以及线性回归的斜率得出的度量来识别水稻栽培。 VH时间序列(一个指标)。使用得出的指标,通过两种不同的方法对稻田进行了映射:决策树和随机森林(RF)。为了验证每种方法的准确性,将水稻分类图与现有的国家数据进行了比较。使用这两种方法都获得了相似的高总体精度。使用简单决策树获得的总体准确性达到96.3%,而使用RF分类器获得的总体准确性为96.6%。因此,该方法提供了一个简单而又精确而强大的工具来绘制水稻区域。

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