Employing a Multi-Input Deep Convolutional Neural Network to Derive Soil Clay Content from a Synergy of Multi-Temporal Optical and Radar Imagery Data

Employing a Multi-Input Deep Convolutional Neural Network to Derive Soil Clay Content from a Synergy of Multi-Temporal Optical and Radar Imagery Data

Earth observation (EO) has an immense potential as being an enabling tool for mapping spatial characteristics of the topsoil layer. Recently, deep learning based algorithms and cloud computing infrastructure have become available with a great potential to revolutionize the processing of EO data. Thi...

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Bibliographic Details
Main Authors: Nikolaos Tziolas, Nikolaos Tsakiridis, Eyal Ben-Dor, John Theocharis, George Zalidis
Format: Article
Language:English
Published: MDPI AG 2020-04-01
Series:Remote Sensing
Subjects:
deep learning
Copernicus data
soil texture mapping
earth observation
spectral signatures
SAR data
Online Access:https://www.mdpi.com/2072-4292/12/9/1389

Internet

https://www.mdpi.com/2072-4292/12/9/1389

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