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Agricultural data inventory

We are currently making an inventory of agricultural reference datasets. In particular, we are looking for spatially explicit information on land cover (cropland extent), crop type and irrigation practices. If you are willing and able to share a dataset or a part of a dataset, we would kindly like to ask you to fill in this questionnaire.

The aim of this questionnaire is to conduct a data inventory for the WorldCereal project. We would like to get a general overview of the datasets within your organization that can be opened and shared with the WorldCereal project. The minimum datasets for training and validation would be observations of land cover and/or crop type at a certain location and time.

Of course we are also interested in other supporting data like crop calendars, prevailing water management etc. We respect your trust and protect your privacy under the European Union General Data Protection Regulation (GDPR).

By filling in this questionnaire you can help us to gain insight about reference data for the WorldCereal system. It should take less than 10 minutes of your time to complete this online questionnaire.

Thank you for your help.  If you have any questions, please contact Arun Pratihast or Hendrik Boogaard.

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Below a detailed specification of the required reference data:

  • Data that can be shared (data policy and license).
  • Observations of cropland extent1  for a specific location (parcel-polygon and/or location-point) and a specific time (year-season or date). If available information on water management: rain-fed or irrigated.
  • Observation of crop type1 for a specific location (parcel-polygon and/or location-point) and specific time (year-season or date).
  • Covering previous year-seasons, preferably from 2017 onwards but not before 2016.
  • Covering data-poor regions like Africa, Asia (central and east) and South America. 
  • Sufficient spatial accuracy: preferably <10 m.
  • Preferably in co-ordinate system EPSG:4326 (https://epsg.io) but at least a defined projection (EPSG code).
  • Enough information on timing: minimum is year-season (e.g. 2018, long rains), preferably a real date of observation.
  • Spread over classes (land cover and crop type, specifically including crops that have similar reflectance signatures as wheat and maize).
  • Spread over season (different reflectance signatures throughout season).
  • Preferably data sets with a larger number of observations, with supporting documentation e.g. clear names of crop types, time of survey, sufficient spatial accuracy, information on sampling design and validation/curation efforts.

  1We refer to our WorldCereal legend for land cover and crop type based on FAO classification.