August 19, 2021 Dataset Restricted Access

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

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{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<p>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.&nbsp;&nbsp;</p>\n\n<p>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.&nbsp;&nbsp;</p>\n\n<p>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; <a href=\"https://force-eo.readthedocs.io/en/latest/\">https://force-eo.readthedocs.io/en/latest/</a> last accessed: 19. August 2021), before environmental and SAR data were included in the ARD cube.&nbsp;&nbsp;</p>\n\n<p>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&ouml;rfer et al. (2022).&nbsp;</p>\n\n<p>The final models were applied to areas in Germany that were defined as agricultural land in ATKIS DLM 2018 (Geobasisdaten: &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).&nbsp;</p>\n\n<p>The maps are provided as GeoTiff files together with a QGIS legend file for visualization.&nbsp;</p>\n\n<p>Class catalogue:</p>\n\n<p>10 &nbsp;&nbsp; &nbsp;Grassland<br>\n31 &nbsp;&nbsp; &nbsp;Winter wheat<br>\n32 &nbsp;&nbsp; &nbsp;Winter rye<br>\n33 &nbsp;&nbsp; &nbsp;Winter barley<br>\n34 &nbsp;&nbsp; &nbsp;Other winter cereal<br>\n41 &nbsp;&nbsp; &nbsp;Spring barley<br>\n42 &nbsp;&nbsp; &nbsp;Spring oat<br>\n43 &nbsp;&nbsp; &nbsp;Other spring cereal<br>\n50 &nbsp;&nbsp; &nbsp;Winter rapeseed<br>\n60 &nbsp;&nbsp; &nbsp;Legume<br>\n70 &nbsp;&nbsp; &nbsp;Sunflower<br>\n80 &nbsp;&nbsp; &nbsp;Sugar beet<br>\n91 &nbsp;&nbsp; &nbsp;Maize<br>\n92 &nbsp;&nbsp; &nbsp;Maize (grain)<br>\n100&nbsp;&nbsp; &nbsp;Potato<br>\n110&nbsp;&nbsp; &nbsp;Grapevine<br>\n120&nbsp;&nbsp; &nbsp;Strawberry<br>\n130&nbsp;&nbsp; &nbsp;Asparagus<br>\n140&nbsp;&nbsp; &nbsp;Onion<br>\n150&nbsp;&nbsp; &nbsp;Hops<br>\n160&nbsp;&nbsp; &nbsp;Orchard<br>\n181&nbsp;&nbsp; &nbsp;Carrot<br>\n182&nbsp;&nbsp; &nbsp;Other vegetables<br>\n555&nbsp;&nbsp; &nbsp;Small woody features<br>\n999&nbsp;&nbsp; &nbsp;Other agricultural areas</p>\n\n<p>&nbsp;</p>\n\n<p>Blickensd&ouml;rfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., &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</p>\n\n<p>BKG, Bundesamt f&uuml;r Kartographie und Geod&auml;sie (2015). Digitales Gel&auml;ndemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 19. August 2021).&nbsp;</p>\n\n<p>BKG, Bundesamt f&uuml;r Kartographie und Geod&auml;sie (2018). Digitales Basis-Landschaftsmodell.&nbsp;<br>\nhttps://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 19. August 2021).</p>\n\n<p>Frantz, D. (2019). FORCE&mdash;Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.</p>\n\n<p>&nbsp;</p>\n\n<p><a href=\"https://zenodo.org/record/5153047#.YhYwgpYxmUn\">National-scale crop type maps for Germany </a>&copy; 2021 by Blickensd&ouml;rfer, Lukas; Schwieder, Marcel; Pflugmacher, Dirk; Nendel, Claas; Erasmi, Stefan; Hostert, Patrick is licensed under <a href=\"http://creativecommons.org/licenses/by/4.0/?ref=chooser-v1\">CC BY 4.0. </a></p>", 
  "creator": [
    {
      "affiliation": "Th\u00fcnen Institute of Forest Ecosystems, Alfred-Moeller-Stra\u00dfe 1, 16225 Eberswalde, Germany", 
      "@type": "Person", 
      "name": "Blickensd\u00f6rfer, Lukas"
    }, 
    {
      "affiliation": "Th\u00fcnen Institute of Farm Economics, Bundesallee 63, 38116 Braunschweig, Germany", 
      "@type": "Person", 
      "name": "Schwieder, Marcel"
    }, 
    {
      "affiliation": "Geography Department, Humboldt Universit\u00e4t zu Berlin, Unter den Linden 6, 10099 Berlin, Germany", 
      "@type": "Person", 
      "name": "Pflugmacher, Dirk"
    }, 
    {
      "affiliation": "Leibniz Centre for Agricultural Landscape Research, Eberswalder Stra\u00dfe 84, 15374 M\u00fcncheberg, Germany", 
      "@type": "Person", 
      "name": "Nendel, Claas"
    }, 
    {
      "affiliation": "Th\u00fcnen Institute of Farm Economics, Bundesallee 63, 38116 Braunschweig, Germany", 
      "@type": "Person", 
      "name": "Erasmi, Stefan"
    }, 
    {
      "affiliation": "Geography Department, Humboldt Universit\u00e4t zu Berlin, Unter den Linden 6, 10099 Berlin, Germany", 
      "@type": "Person", 
      "name": "Hostert, Patrick"
    }
  ], 
  "url": "https://zenodo.org/record/5153047", 
  "datePublished": "2021-08-19", 
  "keywords": [
    "Agriculture", 
    "Remote sensing", 
    "Land cover", 
    "Machine learning", 
    "Maps", 
    "crop type", 
    "Germany"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.5153047", 
  "@id": "https://doi.org/10.5281/zenodo.5153047", 
  "@type": "Dataset", 
  "name": "National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data (2017, 2018 and 2019)"
}