Fabrizi Alessandro, Fiener Peter, Jagdhuber Thomas, Van Oost Kristof, Wilken Florian
Institute of Geography, University of Augsburg, Augsburg, Germany.
Microwaves and Radar Institute, German Aerospace Center, Wessling, Germany.
Sci Rep. 2025 Apr 2;15(1):11339. doi: 10.1038/s41598-025-93658-2.
The use of plastic films has been growing in agriculture, benefiting consumers and producers. However, concerns have been raised about the environmental impact of plastic film use, with mulching films posing a greater threat than greenhouse films. This calls for large-scale monitoring of different plastic film uses. We used cloud computing, freely available optical and radar satellite images, and machine learning to map plastic-mulched farmland (PMF) and plastic cover above vegetation (PCV) (e.g., greenhouse, tunnel) across Germany. The algorithm detected 103 10 ha of PMF and 37 10 ha of PCV in 2020, while a combination of agricultural statistics and surveys estimated a smaller plasticulture cover of around 100 10 ha in 2019. Based on ground observations, the overall accuracy of the classification is 85.3%. Optical and radar features had similar importance scores, and a distinct backscatter of PCV was related to metal frames underneath the plastic films. Overall, the algorithm achieved great results in the distinction between PCV and PMF. This study maps different plastic film uses at a country scale for the first time and sheds light on the high potential of freely available satellite data for continental monitoring.
塑料薄膜在农业中的使用一直在增加，这使消费者和生产者都受益。然而，人们对塑料薄膜使用所产生的环境影响表示担忧，其中地膜造成的威胁比温室薄膜更大。这就需要对不同塑料薄膜的使用情况进行大规模监测。我们利用云计算、免费获取的光学和雷达卫星图像以及机器学习技术，绘制了德国各地的地膜覆盖农田（PMF）和植被上方的塑料覆盖物（PCV）（如温室、隧道）分布图。该算法在2020年检测到103×10公顷的PMF和37×10公顷的PCV，而农业统计和调查相结合的结果估计，2019年塑料栽培覆盖面积约为100×10公顷，比算法检测结果小。基于地面观测，分类的总体准确率为85.3%。光学和雷达特征的重要性得分相似，PCV独特的后向散射与塑料薄膜下方的金属框架有关。总体而言，该算法在区分PCV和PMF方面取得了很好的效果。本研究首次在国家尺度上绘制了不同塑料薄膜的使用情况分布图，并揭示了免费卫星数据在大陆监测方面的巨大潜力。
