The detection of irrigated areas by means of remote sensing is essential to improve agricultural water resource management. Currently, data from the Sentinel constellation offer new possibilities for mapping irrigated areas at the plot scale. Until now, few studies have used Sentinel-1 (S1) and Sentinel-2 (S2) data to provide approaches for mapping irrigated plots in temperate areas. This study proposes a method for detecting irrigated and rainfed plots in a temperate area (southwestern France) jointly using optical (Sentinel-2), radar (Sentinel-1) and meteorological (SAFRAN) time series, through a classification algorithm. Monthly cumulative indices calculated from these satellite data were used in a Random Forest classifier. Two data years have been used, with different meteorological characteristics, allowing the performance of the method to be analysed under different climatic conditions. The combined use of the whole cumulative data (radar, optical and weather) improves the irrigated crop classifications (Overall Accuary (OA) ≈ 0.7) compared to the classifications obtained using each data separately (OA < 0.5). The use of monthly cumulative rainfall allows a significant improvement of the Fscore of irrigated and rainfed classes. Our study also reveals that the use of cumulative monthly indices leads to performances similar to those of the use of 10-day images while considerably reducing computational resources.

中文翻译：

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使用Sentinel-1和Sentinel-2时间序列检测温带地区的灌溉和雨养作物

通过遥感检测灌溉区域对于改善农业水资源管理至关重要。当前，来自Sentinel星座的数据提供了以绘图比例绘制灌溉面积的新可能性。到目前为止，很少有研究使用Sentinel-1（S1）和Sentinel-2（S2）数据来提供在温带地区绘制灌溉图的方法。这项研究提出了一种通过分类算法，使用光学（Sentinel-2），雷达（Sentinel-1）和气象（SAFRAN）时间序列联合检测温带地区（法国西南部）的灌溉区和雨水区的方法。根据这些卫星数据计算出的每月累积指数用于随机森林分类器。使用了两个具有不同气象特征的数据年，允许在不同气候条件下分析该方法的性能。与分别使用每个数据获得的分类（OA <0.5）相比，整体累积数据（雷达，光学和天气）的组合使用改善了灌溉作物的分类（总体精度（OA）≈0.7）。使用每月累积降雨量可以显着提高灌溉和雨养类的Fscore。我们的研究还表明，使用累积月度索引可以产生与使用10天图像相似的性能，同时大大减少了计算资源。7）与分别使用每个数据获得的分类进行比较（OA <0.5）。使用每月累积降雨量可显着提高灌溉和雨养类的Fscore。我们的研究还表明，使用累积月度索引可以产生与使用10天图像相似的性能，同时大大减少了计算资源。7）与分别使用每个数据获得的分类进行比较（OA <0.5）。使用每月累积降雨量可显着提高灌溉和雨养类的Fscore。我们的研究还表明，使用累积月度索引可以产生与使用10天图像相似的性能，同时大大减少了计算资源。