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Conference object . 2019
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Conference object . 2019
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Other literature type . 2019
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Conference object . 2019
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Predicting Soil Organic Matter Content using Machine Learning Models based on Sentinel-2 Imagery

descriptionPublicationkeyboard_double_arrow_right Conference object , Other literature type 01 Jan 2019Publisher:ZenodoFunded by:EC | ANTARES
Authors: Vladimir Ćirić; Sanja Brdar; Predrag Lugonja; Oskar Marko; Vladimir Crnojević;

doi: 10.13140/rg.2.2.23926.55365 , 10.5281/zenodo.3269533 , 10.5281/zenodo.3269534

Predicting Soil Organic Matter Content using Machine Learning Models based on Sentinel-2 Imagery

Abstract

We used machine learning to process Sentinel 2 multispectral images and infer the amount of soil organic matter using satellite soil indices.

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Keywords

Machine Learning, Soil Organic Matter, Sentinel 2

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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