The dataset contains a map of the main classes of agricultural land use (dominant crop types and other land use types) in Germany for the year 2023. It complements a series of maps that 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 as agricultural land, 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).

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 URL that will be provided on request. 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.

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Mailing list

If you do not want to miss the latest updates, please enroll to our mailing list.

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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).

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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).

Germany DE: Agricultural Land: % of Land Area data was reported at 47.501 % in 2022. This records an increase from the previous number of 47.486 % for 2021. Germany DE: Agricultural Land: % of Land Area data is updated yearly, averaging 49.728 % from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 55.951 % in 1965 and a record low of 47.486 % in 2021. Germany DE: Agricultural Land: % of Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Environmental: Land Use, Protected Areas and National Wealth. Agricultural land refers to the share of land area that is arable, under permanent crops, and under permanent pastures. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded. Land under permanent crops is land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee, and rubber. This category includes land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for wood or timber. Permanent pasture is land used for five or more years for forage, including natural and cultivated crops.;Food and Agriculture Organization, electronic files and web site.;Weighted average;

Agricultural land (% of land area) in Germany was reported at 47.5 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Germany - Agricultural land (% of land area) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Germany DE: Agricultural Irrigated Land: % of Total Agricultural Land data was reported at 3.052 % in 2020. This records an increase from the previous number of 2.712 % for 2016. Germany DE: Agricultural Irrigated Land: % of Total Agricultural Land data is updated yearly, averaging 2.209 % from Dec 2006 (Median) to 2020, with 5 observations. The data reached an all-time high of 3.052 % in 2020 and a record low of 1.384 % in 2006. Germany DE: Agricultural Irrigated Land: % of Total Agricultural Land data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Environmental: Land Use, Protected Areas and National Wealth. Agricultural irrigated land refers to agricultural areas purposely provided with water, including land irrigated by controlled flooding.;Food and Agriculture Organization, electronic files and web site.;Weighted average;

The dataset contains a map of the main classes of agricultural land use (dominant crop types and other land use types) in Germany for the year 2022. It complements a series of maps that 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 as agricultural land, 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). The final post-processing step comprises the aggregation of the gridded data to homogeneous objects (fields) based on the approach that is described in Tetteh et al. (2021) and Tetteh et al. (2023).

The maps are available in FlatGeobuf format, 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 URL to the datasets that will be provided on request. 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.

Mailing list

If you do not want to miss the latest updates, please enroll to our mailing list.

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.

Tetteh, G.O., Gocht, A., Erasmi, S., Schwieder, M., & Conrad, C. (2021). Evaluation of Sentinel-1 and Sentinel-2 Feature Sets for Delineating Agricultural Fields in Heterogeneous Landscapes. IEEE Access, 9, 116702-116719.

Tetteh, G.O., Schwieder, M., Erasmi, S., Conrad, C., & Gocht, A. (2023). Comparison of an Optimised Multiresolution Segmentation Approach with Deep Neural Networks for Delineating Agricultural Fields from Sentinel-2 Images. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science

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).

In this study selected results of the main kinds of agricultural land use and other kinds of land use are summarized.

The entire land is roughly arranged into „agricultural used land “, „forest area, forests and felling trees “, „boondocks, waste land“, „building and open area “, „industrial area “, „traffic area (roads, railways, air traffic) “, „water area “, park, ornamental gardens, cemeteries “and „sport, flight, and military exercise areas “.

The agriculturally used land is arranged according to the main cultural kinds „field “, „garden area “, „fruit plants “, „tree nurseries“, „continuous grassland “, „Rebland “, „Osiers plants, poplar plants, fir tree cultures “.

The land use of agriculture differentiates between the cultivation of main field fruits: Grain, leguminous plants, root crops, vegetables, commercial plants and fodder plants.

Topics

Data-Tables in HISTAT: 1. Economic land by main types of use, German Reich (1883-1939); 2. Economic land by main types of use, German Reich, former federal territory (1883-1989); 3a. Total area by types of use, former federal territory (1938-1978); 3b. Total area by types of use, former federal territory, Germany (1979-2001); 4. Agricultural land, farmland, main grups of fruits, former federal territory, Germany (1950-2001).

Germany DE: Agricultural Land data was reported at 165,910.000 sq km in 2021. This records a decrease from the previous number of 165,950.000 sq km for 2020. Germany DE: Agricultural Land data is updated yearly, averaging 173,730.000 sq km from Dec 1961 (Median) to 2021, with 61 observations. The data reached an all-time high of 195,340.000 sq km in 1965 and a record low of 165,910.000 sq km in 2021. Germany DE: Agricultural Land data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Environmental: Land Use, Protected Areas and National Wealth. Agricultural land refers to the share of land area that is arable, under permanent crops, and under permanent pastures. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded. Land under permanent crops is land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee, and rubber. This category includes land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for wood or timber. Permanent pasture is land used for five or more years for forage, including natural and cultivated crops.;Food and Agriculture Organization, electronic files and web site.;Sum;

Agriculture and Fishery Because of providing the population with food, agriculture occupies a central position in the economic context of a country as well as for the process of industrialization. In 1960 Walt W. Rostow pointed out that the availability of sufficient food reserves was the basis and prerequisite for a sustainable economic growth (see: Stadien wirtschaftlichen Wachstums. Göttingen, 1960). Rationalization and progress in the field of agricultural technology induced an increase of agricultural net-production. Furthermore, the agricultural workforce was made redundant, so that this workforce could be employed in industrial production (Jean Fourastié or William Patty: The ‘Three-Sector-Hypothesis. See: Fourastié J.: Die große Hoffnung des 20. Jahrhunderts. Köln 1954, S. 135f.). „Das wichtigste Kennzeichen der Entwicklung der Landwirtschaft in den heute industrialisierten Ländern ist der relative Rückgang des Gewichts der Landwirtschaft – im Verhältnis zur Summe der anderen Wirtschaftsbereiche – und das zur gleichen Zeit zu beobachtende Ansteigen der Arbeitsproduktivität der landwirtschaftlichen Bevölkerung, …“ (Friedrich Wilhelm Henning (1968), Stadien und Typen in der Entwicklung der Landwirtschaft in den heutigen Industrieländern. In: Th. Heidhues et. al: Die Landwirtschaft in der volks- und weltwirtschaftlichen Entwicklung. BLV, München, S. 42). It is important to underline, that in a first stage of development the yield increases were achieved by improved utilization of agricultural land, with new farming techniques and crop rotation as well as through improved feeding in animal breeding, but not by the use of new machines. „Der Einsatz ganz neuer, wissenschaftsbasierter, industrieller Inputs wie sie die moderne Agrarentwicklung seit Ende des 19. Jahrhunderts zunehmend charakterisiert, so daß man für das 20. Jahrhundert vom Übergang zur industrialisierten Landwirtschaft sprechen kann, spielte für neuzeitliches Agrarwachstum so gut wie keine Rolle. … Ganz im Gegenteil, während der neuzeitlichen Agrarrevolutionen kamen quasi alle Ressourcen für Agrarwachstum, von der Arbeit bis zum Wissen immer noch aus dem landwirtschaftlichen Sektor selbst. … (Es kam während der) neuzeitlichen Agrarrevolutionen zu einem … langanhaltenden Ertrags- und Produktivitätszuwachs nur mit den Mitteln traditioneller, vorindustrieller Technologie: höhere Arbeitsintensivität, flächendeckende Anwendung von schon lange bekannter hochintensiver Fruchtfolgen, graduelle Verbesserung althergebrachter Arbeitsgeräte, verbesserte organische Düngung und vermehrter Einsatz tierischer Zugkraft“ (vergl. Kopsidis, Michael (2006): Agrarentwicklung. Historische Agrarrevolutionen und Entwicklungsökonomie. S. 9). Those actions and measures allowed the agriculture to increase its production sustainably and therefore to meet the increasing demand for food caused by the ongoing urbanization process, the constant population growth and the changing occupational pattern in the 19th century. With the exception of the water-soluble phosphate fertilizer, developed by Liebig between 1846 and 1849, other technical developments have been used to a very limited extent. A much more important role took the access of the individual regions to central markets in urban areas. The obtainment of surplus of crops makes only economic sense if it is possible to offer the surplus on central markets. It was much later, in the 20th century, when research and technical development took a significant influence on the way of agricultural production, which merged into industrialized agriculture. The aim of the datacompilation at hand is to describe the quantitative development of the following different agricultural areas of agricultural landuse, planting and harvesting of field crops, fruit-growing, livestock-breeding, and the production of animal products for a long period. The datacompilation encompasses timeseries which cover the period from the start of the official statistics in the year of 1870 up to today’s Federal Republic of Germany and the year 2010. Because of changes of Germany’s borders over time, the consequences of the first and the second world war, as well as changes in survey classifications it is not possible to provide continuous time series data for the desired period of time for all collected variables. Changes of data-classifications or breaks in the timeseries are described in the notes of the data tables. Time Series in the Online database histat: • A Size of company, economic and agricultural productive land - A.01: Agricultural land by company size, ownership disregarded (1871-2010) - A.02: Economic land by primary use and types of cultivated plants (1871-2010)• B Plant production - B.01: Areas under cultivation by crop types (1871-2010) - B.02: Harvest amounts of important crops (1871-2010) - B.03: Yield per hectare of important crops (1871-2010) - B.04: Fruit trees and ...

DE: Arable Land: % of Land Area data was reported at 33.367 % in 2022. This records an increase from the previous number of 33.367 % for 2021. DE: Arable Land: % of Land Area data is updated yearly, averaging 34.218 % from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 35.001 % in 1961 and a record low of 32.844 % in 1992. DE: Arable Land: % of Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Environmental: Land Use, Protected Areas and National Wealth. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded.;Food and Agriculture Organization, electronic files and web site.;Weighted average;

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.

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.

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: 19. August 2021), before environmental and SAR data were included in the ARD cube.

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 Blickensdörfer et al. (2022).

The final models were applied to areas in Germany that were defined as agricultural land in ATKIS DLM 2018 (Geobasisdaten: © 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).

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

Class catalogue:

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

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: 19. August 2021).

BKG, Bundesamt für Kartographie und Geodäsie (2018). Digitales Basis-Landschaftsmodell.
https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 19. August 2021).

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

National-scale crop type maps for Germany © 2021 by Blickensdörfer, Lukas; Schwieder, Marcel; Pflugmacher, Dirk; Nendel, Claas; Erasmi, Stefan; Hostert, Patrick is licensed under CC BY 4.0.

Many applications that target dynamic land surface processes require a temporal observation frequency that is not easily satisfied using data from a single optical sensor. Sentinel-2 and Landsat provide observations of similar nature and offer the opportunity to combine both data sources to increase time-series temporal frequency at high spatial resolution. Multi-sensor image compositing is one way for performing pixel-level data integration and has many advantages for processing frameworks, especially if analyses over larger areas are targeted. Our compositing approach is optimized for narrow temporal-intervals and allows the derivation of time-series of consistent reflectance composites that capture field level phenologies. We processed more than a years' worth of imagery acquired by Sentinel-2A MSI and Landsat-8 OLI as available from the NASA Harmonized Landsat-Sentinel dataset. We used all data acquired over Germany and integrated observations into composites for three defined temporal intervals (10-day, monthly and seasonal). Our processing approach includes generation of proxy values for OLI in the MSI red edge bands and temporal gap filling on the 10-day time-series. We then derive a national scale crop type and land cover map and compare our results to spatially explicit agricultural reference data available for three federal states and to the results of a recent agricultural census for the entire country. The resulting map successfully captures the crop type distribution across Germany at 30m resolution and achieves 81% overall accuracy for 12 classes in three states for which reference data was available. The mapping performance for most classes was highest for the 10-day composites and many classes are discriminated with class specific accuracies >80%. For several crops, such as cereals, maize and rapeseed our mapped acreages compare very well with the official census data with average differences between mapped and census area of 11%, 2% and 3%, respectively. Other classes (grapevine and forest classes) perform slightly less well, likely, because the available reference data does not fully capture the variability of these classes across Germany. The inclusion of the red edge bands slightly improved overall accuracies in all cases and improved class specific accuracies for most crop classes. […]

Food and Agriculture Organization of the United Nations (2017). Food and Agriculture Organization Statistics: Emissions, Land Use - Total | Country: Germany | Item: Land Use total | Element: Net emissions/removals (CO2eq) - Gigagrams, 1990-2014. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. [Data-file]. Dataset-ID: 067-001-041. Dataset: Land Use Total contains all Greenhouse gas (GHG) emissions and removals produced in the different Land Use subdomains, representing the Intergovernmental Panel on Climate Change (IPCC) Land Use categories, also collectively referred to as emissions/removals from the Forestry and Other Land Use (FOLU) sector. FOLU emissions consist of CO2 (carbon dioxide), CH4(methane) and N2O (nitrous oxide) associated with land management activities. CO2 emissions/removals are derived from estimated net carbon stock changes in above and below-ground biomass pools of forest land, including forest land converted to other land uses. CH4 and N2O, and additional CO2 emissions are estimated for fires and drainage of organic soils. Based on FAOSTAT and FRA activity data as well as on geospatial information analysis, data are computed at Tier 1 and Approach 1 of the 2006 IPCC Guidelines for National GHG Inventories. The time-series and cross-sectional data provided here are from the FAOSTAT database of the Food and Agriculture Organization of the United Nations. Statistics include measures related to the food supply; forestry; agricultural production, prices, and investment; and trade and use of resources, such as fertilizers, land, and pesticides. As available, data are provided for approximately 245 countries and 35 regional areas from 1961 through the present. The data are typically supplied by governments to FAO Statistics through national publications and FAO questionnaires. Official data have sometimes been supplemented with data from unofficial sources and from other national or international agencies or organizations. In particular, for the European Union member countries, with the exception of Spain, data obtained from EUROSTAT have been used. Category: Natural Resources and Environment, Agriculture and Food Source: Food and Agriculture Organization of the United Nations Established in 1945 as a specialized agency of the United Nations, the Food and Agricultural Organization’s mandate is to raise levels of nutrition, improve agricultural productivity, better the lives of rural populations, and contribute to the growth of the world economy. Staff experts in seven FAO departments serve as a knowledge network to collect, analyze, and disseminate data, sharing policy expertise with member countries and implementing projects and programs throughout the world aimed at achieving rural development and hunger alleviation goals. The Statistics Division of the Food and Agricultural Organization collates and disseminates food and agricultural statistics globally. http://www.fao.org/ Subject: Agricultural Land, Agricultural Production, Climate Change, Air Pollutants, Land Use, Agriculture, Air Pollution

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.

Sources:

Official statistics of the German Empire, official statistics of prussia, scientific publications.

Grasslands provide a wide range of important ecosystem services. Mapping and assessing the status and use intensity of grasslands is thus important for environmental monitoring. We here provide maps with detected mowing events, as a proxy for grassland use intensity, for grassland areas across Germany for the year 2022.

The dataset contains maps of grassland mowing activity in Germany, which have been produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire grassland area, i.e. permanent grassland, potentially permanent grassland (e.g. fodder crops) and other extensive areas. They are derived from dense time series of Sentinel-2, Landsat 8 (and 9) data. Map production is based on the methods described in Schwieder et al. (2022). The algorithm used to derive the maps is available as a user-defined function for the FORCE environment (Frantz, D., 2019).

The dataset includes seven layers: (1) the number of detected mowing events, (2) the day of year (DOY) of the first to sixth detected mowing event. Ancillary data layers are available on request. The maps include all areas that have at least once been classified as permanent grassland, cultivated grassland or fallow in the maps of agricultural land use between 2017 and 2021 that are provided by Thünen Institute. Please consider to use the respective annual agricultural land use map or any other data source to generate a mask for your purpose.

We provide this dataset "as is" without any warranty regarding the quality or completeness and exclude all liability. Please refer to Schwieder et al. (2022) for the related accuracy assessment and potential limitations and / or contact the authors directly.

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 URL to the datasets that will be provided on request. By doing so the entire map area or only the regions of interest can be accessed.

Mailing list

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References

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

Schwieder, M., Wesemeyer, M., Frantz, D., Pfoch, K., Erasmi, S., Pickert, J., Nendel, C., & Hostert, P. (2022). Mapping grassland mowing events across Germany based on combined Sentinel-2 and Landsat 8 time series. Remote Sensing of Environment, 269, 112795.

_Grassland mowing events across Germany © 2022 by Schwieder, Marcel; Lobert, Felix; Tetteh, Gideon Okpoti; 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).

Germany DE: Arable Land: Hectares per Person data was reported at 0.140 ha in 2021. This records a decrease from the previous number of 0.140 ha for 2020. Germany DE: Arable Land: Hectares per Person data is updated yearly, averaging 0.148 ha from Dec 1961 (Median) to 2021, with 61 observations. The data reached an all-time high of 0.167 ha in 1961 and a record low of 0.140 ha in 2021. Germany DE: Arable Land: Hectares per Person data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Environmental: Land Use, Protected Areas and National Wealth. Arable land (hectares per person) includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded.;Food and Agriculture Organization, electronic files and web site.;Weighted average;

Deutschland hat sich auf internationaler Ebene zur Minderung von Emissionen von Treibhausgasen und luftverschmutzenden Stoffen verpflichtet. Hierbei handelt es sich um die Klimarahmenkonvention (UN Framework Convention on Climate Change, UNFCCC), die Konvention zur Verminderung und Vermeidung grenzüberschreitender Luftverunreinigungen (UNECE Convention on Long-Range Transboundary Air Pollution, CLRTAP) sowie in der Europäischen Union um die Festlegung von Emissionsobergrenzen für einige Stoffe, u. a. Ammoniak (NEC-Richtlinie). Im Rahmen dieser Abkommen müssen die nationalen Emissionen der entsprechenden Gase und Luftschadstoffe jährlich berechnet und in Form des Emissionsinventars an die zuständigen Organisationen übermittelt werden. Die Datendatei beinhaltet Inputdaten und Ergebnisse der Berechnung von gas- und partikelförmigen Emissionen aus der deutschen Landwirtschaft für Deutschland und die Bundesländer in den Jahren 1990 – 2022. Der Bereich Landwirtschaft umfasst dabei die Emissionen aus der Tierhaltung und der Nutzung landwirtschaftlicher Böden sowie aus der Vergärung von Energiepflanzen. Emissionen aus dem Vorleistungsbereich, aus der Nutzung von Energie sowie Landnutzungsänderungen werden den Regelwerken entsprechend an anderer Stelle in den nationalen Inventaren berichtet.

Germany DE: Fertilizer Consumption: per Hectare of Arable Land data was reported at 116.870 kg/ha in 2022. This records a decrease from the previous number of 130.141 kg/ha for 2021. Germany DE: Fertilizer Consumption: per Hectare of Arable Land data is updated yearly, averaging 256.698 kg/ha from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 440.351 kg/ha in 1979 and a record low of 116.870 kg/ha in 2022. Germany DE: Fertilizer Consumption: per Hectare of Arable Land data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Agricultural Production and Consumption. Fertilizer consumption measures the quantity of plant nutrients used per unit of arable land. Fertilizer products cover nitrogenous, potash, and phosphate fertilizers (including ground rock phosphate). Traditional nutrients--animal and plant manures--are not included. For the purpose of data dissemination, FAO has adopted the concept of a calendar year (January to December). Some countries compile fertilizer data on a calendar year basis, while others are on a split-year basis. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded.;Food and Agriculture Organization, electronic files and web site.;Weighted average;

The dataset contains a map of the main classes of agricultural land use (dominant crop types and other land use types) in Germany for the year 2022. It complements a series of maps that 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 as agricultural land, 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 refinded. 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 URL that will be provided on request. 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.

_

Mailing list

If you do not want to miss the latest updates, please enroll to our mailing list.

_

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).

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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).

German agricultural statistics are a vital resource for assessing the environmental impacts of agricultural activities and estimating trends in the sector. However, access to small-scale data is often restricted due to data protection regulations. To address this limitation, the Agraratlas project was launched, aiming to create a comprehensive dataset at the community level, focusing on land use and livestock.

Background and Objective

German agricultural statistics on land use and livestock are crucial indicators for assessing environmental impacts. These statistics form the foundation for estimating trends and evaluating policy impacts within the Thünen-Modellverbund. However, access to detailed data at the municipality level is restricted due to data security concerns. Additionally, changes in regional boundaries and updates to data collection classifications make it challenging to establish consistent comparisons over extended periods.

The Agraratlas project aims to address these challenges by developing a coherent dataset at the community level, spanning from 1999 to the present. This will be achieved by integrating data from the Farm Structure Survey, agricultural statistics, and geo-referenced land use information, while adhering to strict data security requirements to make the dataset publicly available.

The project's outcomes contribute to the Thünen Institute's work on geo-referenced data infrastructure and will be published as part of this initiative.

Approach

The following steps are necessary to create a consistent, unrestricted dataset:

1. Obtain permissions from the federal states to access community-level data.
2. Analyze data at the Forschungsdatenzentrum (FDZ - Research Data Centre) using cluster analysis to comply with data security requirements.
3. Integrate data through a Bayesian estimation procedure, combining:
   • Public agricultural statistics
   • Unconditional data generated by the cluster analysis
   • Geo-referenced (GIS) land use data from the German Landscape Model (DLM)
4. Validate results at the FDZ by comparing them with observed community data.
5. Publish the dataset via the Thünen geo-referenced data infrastructure (GDI).
6. Develop a visualization tool at www.agraratlas.de to facilitate the analysis of land use and livestock densities.

Data and Methods

We employ geographic and geo-referenced methods, integrated with data preparation tools for model analysis, to address structural breaks and new classifications, close data gaps, and create a dataset that remains consistent across time and regions.

Data sources

• 2010 until 2020 – district data of the Farm Structure Survey (FSS) of the statistical offices of the federal states and cluster estimations on basis of the research data centre (RDC), the FSS of the statistical offices of the federation and the federal states (DOI:10.21242/41121.2020.00.01.1.1.0, AFiD-Panel Agrarstruktur 2010/2013/2016/2020, On-Site-Zugang)
• 1999 until 2007 – district data and cluster estimators on basis of the RDC, the statistical offices of the federation and the federal states (DOI:10.21242/41121.2007.00.01.1.1.0, AFiD-Panel Agrarstruktur 1999/2001/2003/2005/2007, On-Site-Zugang)
• 1999 until 2020 Basis-DLM – © GeoBasis-DE/BKG (2020); Terms of use: http://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf
  
  

Preliminary Results

The results of the project are also visually published on the Agraratlas website. Details of the methodological approach are available in the publication listed below.

Explaining the data

Three datasets are provided: crops, animal_heads, and animal_lu. The data is presented in wide format and includes the following columns:

• "reg": Municipality identifier
• "unit": Measurement unit
• "allYear": Year
• Codes for crop or animal activities

The municipality identifiers are based on the 2010 administrative regulation and follow the format "2010_" + the official municipality code ("Amtlicher Gemeindeschlüssel (AGS)"). This ensures that agricultural production activity values for municipalities are trackable over time.

Handling Missing Data
If any values are missing (NA), they can be safely treated as zero.

Units of Measurement

• For crops: LEVL represents 1,000 hectares.
• For animals: LEVL represents 1,000 heads (or 1,000,000 heads for poultry).
• LU represents livestock units measured in 1,000 units.

Data Availability
The datasets include information for the years 1999, 2003, 2007, 2010, 2016, and 2020.

Mapping official and Agraratlas codes

The code of crop and animal production are based on the official agricultural statistics (description of official codes). The following table shows the mapping.

Code official agricultural statistics	Code Agraratlas	Description (German)	Description (English)
C0101	WWHE	Winterweizen einschl. Dinkel und Einkorn	Winter wheat including spelt and einkorn
C0102	SWHE	Sommerweizen (ohne Durum)	Spring wheat (excluding durum)
C0103	SWHE	Hartweizen (Durum)	Durum wheat (hard wheat)
C0104	RYEM	Roggen und Wintermenggetreide	Rye and winter mixed grain
C0105	OCER	Triticale	Triticale
C0106	WBAR	Wintergerste	Winter barley
C0107	SBAR	Sommergerste	Spring barley
C0108	OATS	Hafer	Oats
C0109	OATS	Sommermenggetreide	Spring mixed grain
C0110	MAIZ	Körnermais/Mais zum Ausreifen (einschl. Corn-Cob-Mix)	Grain corn/maize for ripening (including Corn-Cob-Mix)
C0111	OCER	Anderes Getreide zur Körnergewinnung (z.B. Hirse, Sorghum, Kanariensaat)	Other grains for grain harvesting (e.g., millet, sorghum, canary seed)
C0121	OFAR	Getreide zur Ganzpflanzenernte einschl. Teigreife (Verwendung als Futter, zur Biogaserzeugung usw.)	Grains for whole plant harvesting including dough stage (used as fodder, for biogas production, etc.)
C0122	MAIF	Silomais/Grünmais einschl. Lieschkolbenschrot (LKS)	Silage maize/green maize including crushed ear maize (LKS)
C0123	OFAR	Leguminosen zur Ganzpflanzenernte (z.B. Klee, Luzerne, Mischungen ab 80% Leguminosen)	Legumes for whole plant harvesting (e.g., clover, alfalfa, mixtures with at least 80% legumes)
C0124	OFAR	Feldgras/Grasanbau auf dem Ackerland (einschl. Mischungen mit überwiegendem Grasanteil)	Field grass/grass cultivation on arable land (including mixtures predominantly of grass)
C0125	OFAR	andere Pflanzen zur Ganzpflanzenernte (z.B. Phacelia, Sonnenblumen, weitere Mischkulturen)	Other plants for whole plant harvesting (e.g., phacelia, sunflowers, other mixed cultures)
C0131	PULT	Erbsen (ohne Frischerbsen)	Peas (excluding fresh peas)
C0132	PULT	Ackerbohnen	Fava beans
C0133	PULT	Süßlupinen (einschl. Speiselupinen)	Sweet lupins (including edible lupins)
C0134	PULT	andere Hülsenfrüchte und Mischkulturen zur Körnergewinnung	Other legumes and mixed cultures for grain harvesting
C0135	PULT	Sojabohnen	Soybeans
C0140	POTA	Kartoffeln insgesamt	Potatoes in total
C0145	SUGB	Zuckerrüben (auch zur Ethanolerzeugung) ohne Saatguterzeugung	Sugar beets (also for ethanol production) excluding seed production
C0146	ROOF	andere Hackfrüchte ohne Saatguterzeugung (Futter- Runkel-, Kohlrüben, Futterkohl, -möhren)	Other root crops excluding seed production (fodder beet, turnip, kohlrabi, fodder carrot)
C0161	RAPE	Winterraps	Winter rapeseed
C0162	RAPE	Sommerraps, Winter- und Sommerrübsen	Summer rapeseed, winter and summer turnip