Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021)

The dataset contains maps of the main classes of agricultural land use (dominant crop types and other land use types) in Germany, which are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in Blickensdörfer et al. (2022).

All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated.

The map extent covers all areas in Germany that are defined in the respective year as cropland, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020). 

Version v201:Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015).

Version v202:Additional post-processing was performed to detect and mask additional non-plausible areas that were not adequately covered by the first post-processing (e.g., areas with sparse vegetation, montane forests) based on the „Ökosystematlas Deutschland“ (© Statistisches Bundesamt, Deutschland, 2024). As a consequence, the current version includes a new class “Small woody features on other land”. Furthermore, the class "permanent grassland" was refined. Each pixel that was classified as "cultivated grassland" in at least five years (between 2017 and 2022) was translated to "permanent grassland" in the annual maps.

The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the provided URL to the datasets (right click on the respective data set --> “copy link address”). By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately.

Class-specific accuracies for each year are proveded in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability. 

References:Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831.

BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022).

BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).

Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.

Statistisches Bundesamt, Deutschland (2024). Ökosystematlas Deutschland https://oekosystematlas-ugr.destatis.de/ (last accessed: 08.02.2024).

___________________________________________________________________________National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan;  licensed under CC BY 4.0. 

Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).

The study was financially supported by the European Environment Agency and the European Union’s Horizon Europe Research and Innovation programme under Grant Agreement No 101060423 (LAMASUS).

本数据集包含德国主要农业用地类别（主导作物类型及其他土地利用类型）的地图，由滕嫩研究所（Thünen Institute）基于卫星数据自2017年起每年生成。该地图覆盖全部开阔景观，即农业利用面积（UAA）及未开垦区域等。地图基于Sentinel-1、Sentinel-2、Landsat 8的时间序列数据及额外环境数据生成，制作方法参考Blickensdörfer等人2022年发表的研究。

所有光学卫星数据均通过开源软件FORCE——环境监测业务辐射校正框架（Framework for Operational Radiometric Correction for Environmental monitoring, Frantz, D., 2019）进行管理、预处理并结构化构建为分析就绪数据（Analysis Ready Data, ARD）立方体，该框架集成了合成孔径雷达（Synthetic Aperture Radar, SAR）与环境数据。

该地图的覆盖范围涵盖德国境内所有在对应年份依据ATKIS基础景观模型（Basis-DLM）被划定为耕地、草地、小型木本地物、石南灌丛、泥炭地或无植被区域的范围，数据来源：© GeoBasis-DE / 德国联邦测绘与大地测量局（BKG）, 2020。

v201版本：地图的后处理步骤包括筛滤过滤器，以及基于Basis-DLM与德国数字地形模型（数据来源：© GeoBasis-DE / 德国联邦测绘与大地测量局（BKG）, 2015）构建的规则集，用于剔除不合理区域。

v202版本：新增后处理步骤，基于《德国生态系统地图集》（© 德国联邦统计局, 2024）检测并掩膜首次后处理未充分覆盖的不合理区域（如稀疏植被区域、山地森林区域）。据此，当前版本新增了「其他土地上的小型木本地物」类别，同时对「永久草地」类别进行了细化：2017至2022年间至少有5年被分类为「栽培草地」的像素，在年度地图中将被重分类为「永久草地」。

该地图以云优化GeoTIFF（Cloud Optimized GeoTIFF）格式提供，用户可按需下载完整数据集。通过提供的数据集链接，可直接在QGIS、R、Python或其他兼容软件中访问所有数据，支持加载全地图范围或仅感兴趣区域。用于数据可视化的QGIS图例文件可单独下载。

各年份的类别特定精度已在对应表格中给出。本数据集按「现状」提供，不保证其准确性与完整性，我方不承担任何相关责任。

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基于Sentinel-1、Sentinel-2与Landsat时间序列数据生成的德国国家级作物类型地图（2017至2021） © 2024 Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; 采用CC BY 4.0许可协议发布。

本研究由德国联邦食品与农业部资助，作为联合项目「农业景观生物多样性监测」（MonViA）的一部分。同时获得欧洲环境署以及欧盟地平线欧洲研究与创新计划的资助，项目编号101060423（LAMASUS）。

参考文献：
1. Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). 结合Sentinel-1、Sentinel-2与Landsat 8时间序列数据绘制德国作物类型与作物序列图. 《环境遥感》, 269, 112831.
2. 德国联邦测绘与大地测量局（BKG）. (2015). 10米格网数字地形模型DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (最后访问日期：2022年4月28日).
3. 德国联邦测绘与大地测量局（BKG）. (2020). 数字基础景观模型. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (最后访问日期：2022年4月28日).
4. Frantz, D. (2019). FORCE——Landsat与Sentinel-2分析就绪数据及扩展应用. 《遥感》, 11, 1124.
5. 德国联邦统计局. (2024). 德国生态系统地图集 https://oekosystematlas-ugr.destatis.de/ (最后访问日期：2024年2月8日).