National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data (2020)

This data set contains information on the agricultural land use in Germany for the year 2020.
The map was derived from dense time series of Sentinel-2 and Landsat 8 data, Sentinel-1 monthly composites and addtional environmental data. It is based on the methods described in Blickensdörfer et al. 2022 and can be seen as a continuation of the dataset provided under: https://zenodo.org/record/5153047#.YWFyXn1CREZ.
The maps can be explored online in a webviewer.

Due to specific user needs the class catalogue was slightly modified but a translation key (Table 1) and a translated map version (*_V1.tif) is provided. However, it has to be noted that some rather small classes in the previous maps were not differentiated anymore (e.g., onions, carrots, asparagus).Thus, the classes 34, 43, 92, 130, 140, 181 and 182 were excluded from the raster and legend files.

Table 1: Updated class catalogue and translation key to the class catalogue used in Blickensdörfer et al. 2022.

New class code (V2)

Class name (V2)

Class code (V1)

Class name (V1)

1101

Winter wheat

31

Winter wheat

34

Other winter cereals

1102

Winter barley

33

Winter barley

1103

Winter rye

32

Winter rye

1201

Spring barley

41

Spring barley

43

Other spring cereals

1202

Oat

42

Spring oat

1300

Maize

91

Maize (silage)

92

Maize (grain)

1401

Potatoe

100

Potatoe

1402

Sugar beet

80

Sugar beet

1501

Rapeseed

50

Winter rapeseed

1502

Sunflower

70

Sunflower

1611

Peas

60

Legume

1612

Broad beans

1613

Lupine

1614

Soy

1603

Vegetables

120

Strawberry

130

Asparagus

140

Onion

181

Carrot

182

Other leafy vegetables

1602

Cultivated grassland

10

Grassland

200

Permanent grassland

3003

Fallow land

3001

Small woody features

555

Small woody features

3002

Other areas

999

Other agricultural areas

4001

Grapevine

110

Grapevine

4002

Hops

150

Hops

4003

Orchard

160

Orchards

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; https://force-eo.readthedocs.io/en/latest/ last accessed: 12. April 2022), before environmental and SAR data were included in the ARD cube. 

The models were trained in FORCE and applied to all areas in Germany that were defined as agricultural land, small woody features, heathland or peatland in ATKIS DLM 2020 (Geobasisdaten: © GeoBasis-DE / BKG (2020)). 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 and hops areas that were not labelled as the respective permanent crop in ATKIS DLM (BKG (2020); labelled as other agricultural areas in the final map). 

The maps are provided as GeoTiff files together with QGIS legend files for visualization. 

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 (2018). 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.

National-scale crop type maps for Germany © 2022 by Schwieder, Marcel; Erasmi, Stefan; Nendel, Claas; Hostert, Patrick is licensed under CC BY 4.0.