Ghassemi Babak, Izquierdo-Verdiguier Emma, Verhegghen Astrid, Yordanov Momchil, Lemoine Guido, Moreno Martínez Álvaro, De Marchi Davide, van der Velde Marijn, Vuolo Francesco, d'Andrimont Raphaël
Institute of Geomatics, University of Natural Resources and Life Sciences, Vienna, Peter-Jordan-Straße 82, 1190, Vienna, Austria.
Joint Research Centre (JRC), European Commission, Ispra, Italy.
Sci Data. 2024 Sep 27;11(1):1048. doi: 10.1038/s41597-024-03884-y.
To provide the information needed for a detailed monitoring of crop types across the European Union (EU), we present an advanced 10-metre resolution map for the EU and Ukraine with 19 crop types for 2022, updating the 2018 version. Using Earth Observation (EO) and in-situ data from Eurostat's Land Use and Coverage Area Frame Survey (LUCAS) 2022, the methodology included 134,684 LUCAS Copernicus polygons, Sentinel-1 and Sentinel-2 satellite imagery, land surface temperature and a digital elevation model. Based on this data, two classification layers were developed using a Random Forest machine learning approach: a primary map and a gap-filling map to address cloud-covered gaps. The combined maps, covering 27 EU countries, show an overall accuracy of 79.3% for seven major land cover classes and 70.6% for all 19 crop types. The trained model was used to derive the 2022 map for Ukraine, demonstrating its robustness even in regions without labelled samples for model training.
为提供详细监测整个欧盟（EU）作物类型所需的信息，我们展示了一张2022年欧盟和乌克兰的先进10米分辨率地图，涵盖19种作物类型，更新了2018年版本。利用地球观测（EO）数据以及欧盟统计局2022年土地利用和覆盖面积框架调查（LUCAS）的实地数据，该方法纳入了134,684个LUCAS哥白尼多边形、哨兵-1和哨兵-2卫星图像、地表温度以及数字高程模型。基于这些数据，采用随机森林机器学习方法开发了两个分类图层：一个主地图和一个用于填补云层覆盖区域空白的填图。覆盖27个欧盟国家的合成地图显示，七种主要土地覆盖类别的总体准确率为79.3%，所有19种作物类型的总体准确率为70.6%。经过训练的模型被用于绘制2022年乌克兰地图，证明了其即使在没有用于模型训练的标记样本的地区也具有稳健性。
