EGU2020-13549
https://doi.org/10.5194/egusphere-egu2020-13549
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

A multi-dimensional Sentinel-based Soil Monitoring Scheme (S2MoS) for soil clay content estimation

Nikolaos Tziolas1, Nikolaos Tsakiridis2, Eyal Ben Dor3, John Theocharis2, and George Zalidis1
Nikolaos Tziolas et al.
  • 1School of Agriculture, Faculty of Agriculture, Forestry, and Natural Environment, Aristotle University of Thessaloniki, Thessaloniki, Greece (ntziolas@auth.gr)
  • 2Department of Electrical and Computer Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece (tsakirin@auth.gr)
  • 3The Remote Sensing and GIS laboratory, Department of Geography, School of Earth Science, Tel-Aviv University, Tel-Aviv, Israel (bendor@tauex.tau.ac.il)

Earth Observation (EO) has an immense potential as an enabling tool for mapping the spatial variation of the topsoil layer. Additionally, machine learning based algorithms deployed on cloud computing infrastructures have a great potential to revolutionize the processing of EO data. This paper aims to present a multi-dimensional Sentinel-based Soil Monitoring Scheme (S2MoS) based on open-access Copernicus Sentinel data and the Google Earth Engine platform to map soil properties. Building on key results from existing data mining approaches to extract bare soil reflectance values the current study presents i) preliminary insights on the synergistic use of open access SAR and optical images obtained from Sentinel-1 and Sentinel-2 sensors; and ii) evaluate the efficiency of machine learning algorithms to predict soil attributes based on multi-temporal analysis. In that regard, this study evaluated, based on Sentinel images extending over a 3 years period (2017-2019), the performance of two state of the art machine learning approaches, namely random forest and neural networks. Spatial thresholds values of 0.25 and 0.075 for Normalized Difference Vegetation Index and Normalized Burn Ratio 2 indices respectively were applied to mask bare soil pixels. In this study, we used 5000 soil data belonging to cropland land use from the European LUCAS topsoil database. We calibrated the models based on 4000 soil samples and then validated this approach with the rest 1000 samples  predict soil clay content. A higher prediction performance (R2=0.53) was achieved by the inclusion of both types (SAR and optical) of observations using the neural network model, demonstrating an improvement of about 5% in overall accuracy compared to the R2 using the multi-year median optical composite.

How to cite: Tziolas, N., Tsakiridis, N., Ben Dor, E., Theocharis, J., and Zalidis, G.: A multi-dimensional Sentinel-based Soil Monitoring Scheme (S2MoS) for soil clay content estimation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13549, https://doi.org/10.5194/egusphere-egu2020-13549, 2020

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