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Article

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

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School of Agriculture, Faculty of Agriculture, Forestry, and Natural Environment, Aristotle University of Thessaloniki, 54123 Thessaloniki, Greece
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Department of Electrical and Computer Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, 54123 Thessaloniki, Greece
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The Remote Sensing and GIS Laboratory Department of Geography, School of Earth Science, Tel-Aviv University, Tel Aviv-Yafo P.O. Box 39040, Israel
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(9), 1389; https://doi.org/10.3390/rs12091389
Received: 31 March 2020 / Revised: 26 April 2020 / Accepted: 27 April 2020 / Published: 28 April 2020
(This article belongs to the Special Issue Soil Properties Using Imaging Spectroscopy)
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. This paper aims to present a novel EO-based soil monitoring approach leveraging open-access Copernicus Sentinel data and Google Earth Engine platform. Building on key results from existing data mining approaches to extract bare soil reflectance values the current study delivers valuable insights on the synergistic use of open access optical and radar images. The proposed framework is driven by the need to eliminate the influence of ambient factors and evaluate the efficiency of a convolutional neural network (CNN) to effectively combine the complimentary information contained in the pool of both optical and radar spectral information and those form auxiliary geographical coordinates mainly for soil. We developed and calibrated our multi-input CNN model based on soil samples (calibration = 80% and validation 20%) of the LUCAS database and then applied this approach to predict soil clay content. A promising prediction performance (R2 = 0.60, ratio of performance to the interquartile range (RPIQ) = 2.02, n = 6136) was achieved by the inclusion of both types (synthetic aperture radar (SAR) and laboratory visible near infrared–short wave infrared (VNIR-SWIR) multispectral) of observations using the CNN model, demonstrating an improvement of more than 5.5% in RMSE using the multi-year median optical composite and current state-of-the-art non linear machine learning methods such as random forest (RF; R2 = 0.55, RPIQ = 1.91, n = 6136) and artificial neural network (ANN; R2 = 0.44, RPIQ = 1.71, n = 6136). Moreover, we examined post-hoc techniques to interpret the CNN model and thus acquire an understanding of the relationships between spectral information and the soil target identified by the model. Looking to the future, the proposed approach can be adopted on the forthcoming hyperspectral orbital sensors to expand the current capabilities of the EO component by estimating more soil attributes with higher predictive performance. View Full-Text
Keywords: deep learning; Copernicus data; soil texture mapping; earth observation; spectral signatures; SAR data; hyper and multi spectral remote sensing deep learning; Copernicus data; soil texture mapping; earth observation; spectral signatures; SAR data; hyper and multi spectral remote sensing
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MDPI and ACS Style

Tziolas, N.; Tsakiridis, N.; Ben-Dor, E.; Theocharis, J.; Zalidis, G. Employing a Multi-Input Deep Convolutional Neural Network to Derive Soil Clay Content from a Synergy of Multi-Temporal Optical and Radar Imagery Data. Remote Sens. 2020, 12, 1389. https://doi.org/10.3390/rs12091389

AMA Style

Tziolas N, Tsakiridis N, Ben-Dor E, Theocharis J, Zalidis G. Employing a Multi-Input Deep Convolutional Neural Network to Derive Soil Clay Content from a Synergy of Multi-Temporal Optical and Radar Imagery Data. Remote Sensing. 2020; 12(9):1389. https://doi.org/10.3390/rs12091389

Chicago/Turabian Style

Tziolas, Nikolaos, Nikolaos Tsakiridis, Eyal Ben-Dor, John Theocharis, and George Zalidis. 2020. "Employing a Multi-Input Deep Convolutional Neural Network to Derive Soil Clay Content from a Synergy of Multi-Temporal Optical and Radar Imagery Data" Remote Sensing 12, no. 9: 1389. https://doi.org/10.3390/rs12091389

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