August 19, 2021 Dataset Restricted Access

Blickensdörfer, Lukas; Schwieder, Marcel; Pflugmacher, Dirk; Nendel, Claas; Erasmi, Stefan; Hostert, Patrick

MARC21 XML Export

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    <subfield code="a">&lt;p&gt;Detailed maps of agricultural landscapes are a valuable data source for manifold applications, such as environmental modelling, biodiversity monitoring or the support of agricultural statistics. Satellites from the European Copernicus program, especially, Sentinel-1 and Sentinel-2, as well as the Landsat missions operated by NASA/USGS, acquire data with a spatial resolution (10 m to 30 m) that is sufficient to identify field structures in complex agricultural landscapes. Time series of combined Sentinel-2 and Landsat data facilitate to differentiate crop types with a high thematic detail based on differences in land surface phenology. However, large data gaps due to frequent cloud cover may hamper such classification approaches.&amp;nbsp;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;We thus combined dense interpolated times series of Sentinel-2A/B and Landsat data with monthly composites of Sentinel-1 backscatter data to overcome periods with high cloud contamination. To further account for regional variations along the agroecological gradient within Germany, we additionally included a broad set of spatially explicit environmental data in a random forest classification model.&amp;nbsp;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;All optical satellite data were downloaded, 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; &lt;a href="https://force-eo.readthedocs.io/en/latest/"&gt;https://force-eo.readthedocs.io/en/latest/&lt;/a&gt; last accessed: 19. August 2021), before environmental and SAR data were included in the ARD cube.&amp;nbsp;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;For each year (2017, 2018 and 2019) we trained an individual random forest model with 24 agricultural classes. Each model was independently validated with area adjusted overall accuracies of 80% (2017), 79% (2018), and 78% (2019). Further details regarding the data and methods used as well as class wise accuracies can be found in Blickensd&amp;ouml;rfer et al. (2022).&amp;nbsp;&lt;/p&gt;

&lt;p&gt;The final models were applied to areas in Germany that were defined as agricultural land in ATKIS DLM 2018 (Geobasisdaten: &amp;copy; GeoBasis-DE / BKG (2018)). Post-processing of the final maps included applying a sieve filter, the exclusion of classes other than grasslands and small woody features above 900 m (based on the Digital Elevation Model for Germany BKG (2015)) and the exclusion of grapevine/hops areas that were not labelled as the respective permanent crop in ATKIS DLM (labelled as other agricultural areas in the final map).&amp;nbsp;&lt;/p&gt;

&lt;p&gt;The maps are provided as GeoTiff files together with a QGIS legend file for visualization.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;Class catalogue:&lt;/p&gt;

&lt;p&gt;10 &amp;nbsp;&amp;nbsp; &amp;nbsp;Grassland&lt;br&gt;
31 &amp;nbsp;&amp;nbsp; &amp;nbsp;Winter wheat&lt;br&gt;
32 &amp;nbsp;&amp;nbsp; &amp;nbsp;Winter rye&lt;br&gt;
33 &amp;nbsp;&amp;nbsp; &amp;nbsp;Winter barley&lt;br&gt;
34 &amp;nbsp;&amp;nbsp; &amp;nbsp;Other winter cereal&lt;br&gt;
41 &amp;nbsp;&amp;nbsp; &amp;nbsp;Spring barley&lt;br&gt;
42 &amp;nbsp;&amp;nbsp; &amp;nbsp;Spring oat&lt;br&gt;
43 &amp;nbsp;&amp;nbsp; &amp;nbsp;Other spring cereal&lt;br&gt;
50 &amp;nbsp;&amp;nbsp; &amp;nbsp;Winter rapeseed&lt;br&gt;
60 &amp;nbsp;&amp;nbsp; &amp;nbsp;Legume&lt;br&gt;
70 &amp;nbsp;&amp;nbsp; &amp;nbsp;Sunflower&lt;br&gt;
80 &amp;nbsp;&amp;nbsp; &amp;nbsp;Sugar beet&lt;br&gt;
91 &amp;nbsp;&amp;nbsp; &amp;nbsp;Maize&lt;br&gt;
92 &amp;nbsp;&amp;nbsp; &amp;nbsp;Maize (grain)&lt;br&gt;
100&amp;nbsp;&amp;nbsp; &amp;nbsp;Potato&lt;br&gt;
110&amp;nbsp;&amp;nbsp; &amp;nbsp;Grapevine&lt;br&gt;
120&amp;nbsp;&amp;nbsp; &amp;nbsp;Strawberry&lt;br&gt;
130&amp;nbsp;&amp;nbsp; &amp;nbsp;Asparagus&lt;br&gt;
140&amp;nbsp;&amp;nbsp; &amp;nbsp;Onion&lt;br&gt;
150&amp;nbsp;&amp;nbsp; &amp;nbsp;Hops&lt;br&gt;
160&amp;nbsp;&amp;nbsp; &amp;nbsp;Orchard&lt;br&gt;
181&amp;nbsp;&amp;nbsp; &amp;nbsp;Carrot&lt;br&gt;
182&amp;nbsp;&amp;nbsp; &amp;nbsp;Other vegetables&lt;br&gt;
555&amp;nbsp;&amp;nbsp; &amp;nbsp;Small woody features&lt;br&gt;
999&amp;nbsp;&amp;nbsp; &amp;nbsp;Other agricultural areas&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;Blickensd&amp;ouml;rfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., &amp;amp; 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&lt;/p&gt;

&lt;p&gt;BKG, Bundesamt f&amp;uuml;r Kartographie und Geod&amp;auml;sie (2015). Digitales Gel&amp;auml;ndemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 19. August 2021).&amp;nbsp;&lt;/p&gt;

&lt;p&gt;BKG, Bundesamt f&amp;uuml;r Kartographie und Geod&amp;auml;sie (2018). Digitales Basis-Landschaftsmodell.&amp;nbsp;&lt;br&gt;
https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 19. August 2021).&lt;/p&gt;

&lt;p&gt;Frantz, D. (2019). FORCE&amp;mdash;Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&lt;a href="https://zenodo.org/record/5153047#.YhYwgpYxmUn"&gt;National-scale crop type maps for Germany &lt;/a&gt;&amp;copy; 2021 by Blickensd&amp;ouml;rfer, Lukas; Schwieder, Marcel; Pflugmacher, Dirk; Nendel, Claas; Erasmi, Stefan; Hostert, Patrick is licensed under &lt;a href="http://creativecommons.org/licenses/by/4.0/?ref=chooser-v1"&gt;CC BY 4.0. &lt;/a&gt;&lt;/p&gt;</subfield>
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    <subfield code="u">Geography Department, Humboldt Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany</subfield>
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    <subfield code="u">Leibniz Centre for Agricultural Landscape Research, Eberswalder Straße 84, 15374 Müncheberg, Germany</subfield>
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