欧洲联盟 2022 年作物地图:地球观测以 10 米的深度深入欧洲的作物织锦。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-09-27 DOI:10.1038/s41597-024-03884-y
Babak Ghassemi, Emma Izquierdo-Verdiguier, Astrid Verhegghen, Momchil Yordanov, Guido Lemoine, Álvaro Moreno Martínez, Davide De Marchi, Marijn van der Velde, Francesco Vuolo, Raphaël d'Andrimont
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引用次数: 0

摘要

为了提供对整个欧盟(EU)作物类型进行详细监测所需的信息,我们在 2018 年版本的基础上,为欧盟和乌克兰提供了一张先进的 10 米分辨率地图,其中包含 2022 年的 19 种作物类型。该方法使用来自欧盟统计局 2022 年土地利用和覆盖区框架调查(LUCAS)的地球观测(EO)和原位数据,包括 134,684 个 LUCAS 哥白尼多边形、哨兵-1 和哨兵-2 卫星图像、地表温度和数字高程模型。在这些数据的基础上,利用随机森林机器学习方法开发了两个分类层:一个是主要地图,另一个是填补云层空白的地图。综合地图覆盖了 27 个欧盟国家,显示七种主要土地覆被类别的总体准确率为 79.3%,所有 19 种作物类型的准确率为 70.6%。经过训练的模型被用于绘制乌克兰的 2022 年地图,这表明即使在没有用于模型训练的标记样本的地区,该模型也具有很强的鲁棒性。
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European Union crop map 2022: Earth observation's 10-meter dive into Europe's crop tapestry.

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.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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