<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Ottosen, Thor-Bjørn</dc:creator>
  <dc:creator>Lommen, Suzanne T. E.</dc:creator>
  <dc:creator>Skjøth, Carsten Ambelas</dc:creator>
  <dc:date>2019-02-01</dc:date>
  <dc:description xmlns:ns0="xml" ns0:lang="en">Maps of cropping practice, including the level of weed infestation, are useful planning  tools e.g. for the assessment of the environmental impact of the crops, and Northern  Italy is an important example due to the large and diverse agricultural production and  the high weed infestation. Sentinel-2A is a new satellite with a high spatial and  temporal resolution which potentially allows the creation of detailed maps of cropping  practice including weed infestation. To explore the applicability of Sentinel-2A for  mapping cropping practice, we analysed the Normalised Differential Vegetation Index  (NDVI) time series from five weed-infested crop fields as well as the areas designated  as non-irrigated agricultural land in Corine Land Cover, which also contributed to an  increased understanding of the cropping practice in the region. The analysis of the  case studies showed that the temporal resolution of Sentinel-2A was high enough to  distinguish the gross features of the cropping practice, and that high weed infestations  can be detected at this spatial resolution. The analysis of the entire region showed the  potential for mapping cropping practice using Sentinel-2. In conclusion, Sentinel-2A is  to some extent applicable for mapping cropping practice with reasonable thematic  accuracy.</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>https://folia.unifr.ch/global/documents/307590</dc:identifier>
  <dc:identifier>https://folia.unifr.ch/documents/307590/files/lom_rsc.pdf</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compag.2018.12.031</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>License undefined</dc:rights>
  <dc:source>Computers and Electronics in Agriculture. - 2019, vol. 157, p. 232–238</dc:source>
  <dc:subject>info:eu-repo/classification/udc/57</dc:subject>
  <dc:title xmlns:ns1="xml" ns1:lang="en">Remote sensing of cropping practice in Northern Italy using time-series from Sentinel-2</dc:title>
  <dc:type>http://purl.org/coar/resource_type/c_6501</dc:type>
</oai_dc:dc>
