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Title: Classifying Soil Type Using Radar Satellite Images
Authors: Ahmed, MD Sajib
Gonçalves, Teresa
Rato, Luís
Marques da Silva, José Rafael
Vieira, Filipe
Paixão, Luís
Salgueiro, Pedro
Keywords: Remote Sensing
Soil Electrical Conductivity
Sentinel-1, Machine Learning
Random Forest
Issue Date: 30-Oct-2020
Abstract: The 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.
Type: article
Appears in Collections:INF - Artigos em Livros de Actas/Proceedings

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