Classifying Soil Type Using Radar Satellite Images

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dc.contributor.authorAhmed, Md Sajib
dc.contributor.authorGonçalves, Teresa
dc.contributor.authorRato, Luís
dc.contributor.authorMarques da Silva, José Rafael
dc.contributor.authorVieira, Filipe
dc.contributor.authorPaixão, Luís
dc.contributor.authorSalgueiro, Pedro
dc.date.accessioned2022-05-03T14:45:05Z
dc.date.available2022-05-03T14:45:05Z
dc.date.issued2020-10-30
dc.description.abstractThe growth of the crop is dependent on soil type, apart from atmospheric and geo-location characteristics. As of now, there is no direct and costfree method to measure soil property or to classify soil type. In this work, we proposed a machine learning model to classify soil type using Sentinel-1 satellite radar images. Further, the developed classifier achieved 72.17% F1-score classifying sandy, free and clayish on a set of 65003 data points collected over one year (from Oct 2018 to Sep 2019) over 14 corn parcels near Ourique, Portugal.por
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dc.identifier.urihttps://recpad2020.uevora.pt/wp-content/uploads/2020/11/proceedings_recpad2020.pdf
dc.identifier.urihttp://hdl.handle.net/10174/31998
dc.language.isoporpor
dc.peerreviewedyespor
dc.rightsopenAccesspor
dc.subjectRemote Sensingpor
dc.subjectSoil Electrical Conductivitypor
dc.subjectSentinel-1, Machine Learningpor
dc.subjectRandom Forestpor
dc.titleClassifying Soil Type Using Radar Satellite Imagespor
dc.typearticlepor

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