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dc.date.accessioned2019-09-10T15:26:33Z
dc.date.available2019-09-10T15:26:33Z
dc.date.issued2019-06-12
dc.identifierdoi:10.17170/kobra-20190910674
dc.identifier.urihttp://hdl.handle.net/123456789/11316
dc.description.sponsorshipGefördert durch den Publikationsfonds der Universität Kasselger
dc.language.isoengeng
dc.subjectagricultural land-covereng
dc.subjectmulti-spectraleng
dc.subjectgeneraltized modeleng
dc.subjectmachine learningeng
dc.subjectcrop type mappingeng
dc.subjectIntegrated Administration and Control Systemeng
dc.subjectremote sensingeng
dc.subject.ddc630
dc.titleMulti-Temporal Agricultural Land-Cover Mapping Using Single-Year and Multi-Year Models Based on Landsat Imagery and IACS Dataeng
dc.typeAufsatz
dcterms.abstractThe spatial distribution and location of crops are necessary information for agricultural planning. The free availability of optical satellites such as Landsat offers an opportunity to obtain this key information. Crop type mapping using satellite data is challenged by its reliance on ground truth data. The Integrated Administration and Control System (IACS) data, submitted by farmers in Europe for subsidy payments, provide a solution to the issue of periodic field data collection. The present study tested the performance of the IACS data in the development of a generalized predictive crop type model, which is independent of the calibration year. Using the IACS polygons as objects, the mean spectral information based on four different vegetation indices and six Landsat bands were extracted for each crop type and used as predictors in a random forest model. Two modelling methods called single-year (SY) and multiple-year (MY) calibration were tested to find out their performance in the prediction of grassland, maize, summer, and winter crops. The independent validation of SY and MY resulted in a mean overall accuracy of 71.5% and 77.3%, respectively. The field-based approach of calibration used in this study dealt with the ‘salt and pepper’ effects of the pixel-based approach.eng
dcterms.accessRightsopen access
dcterms.creatorKyere, Isaac
dcterms.creatorAstor, Thomas
dcterms.creatorGraß, Rüdiger
dcterms.creatorWachendorf, Michael
dc.relation.doidoi:10.3390/agronomy9060309
dc.type.versionpublishedVersion
dcterms.source.identifierISSN 2073-4395
dcterms.source.issueIssue 6
dcterms.source.journalAgronomyeng
dcterms.source.pageinfo309
dcterms.source.volumeVolume 9


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