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Evaluation of a Micro-Electro Mechanical Systems Spectral Sensor for Soil Properties Estimation

Author

Listed:
  • Konstantinos Karyotis

    (School of Science and Technology, International Hellenic University, 14th km Thessaloniki-N, Moudania, 57001 Thermi, Greece)

  • Theodora Angelopoulou

    (Laboratory of Remote Sensing, Spectroscopy, and GIS, Department of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
    Centre of Research and Technology—Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (iBO), Thessaloniki, 57001 Thermi, Greece)

  • Nikolaos Tziolas

    (Laboratory of Remote Sensing, Spectroscopy, and GIS, Department of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Evgenia Palaiologou

    (Laboratory of Remote Sensing, Spectroscopy, and GIS, Department of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Nikiforos Samarinas

    (Faculty of Engineering, School of Rural and Surveying Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • George Zalidis

    (Laboratory of Remote Sensing, Spectroscopy, and GIS, Department of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
    Interbalkan Environment Center, 18 Loutron Str., 57200 Lagadas, Greece)

Abstract

Soil properties estimation with the use of reflectance spectroscopy has met major advances over the last decades. Their non-destructive nature and their high accuracy capacity enabled a breakthrough in the efficiency of performing soil analysis against conventional laboratory techniques. As the need for rapid, low cost, and accurate soil properties’ estimations increases, micro electro mechanical systems (MEMS) have been introduced and are becoming applicable for informed decision making in various domains. This work presents the assessment of a MEMS sensor (1750–2150 nm) in estimating clay and soil organic carbon (SOC) contents. The sensor was first tested under various experimental setups (different working distances and light intensities) through its similarity assessment (Spectral Angle Mapper) to the measurements of a spectroradiometer of the full 350–2500 nm range that was used as reference. MEMS performance was evaluated over spectra measured from 102 samples in laboratory conditions. Models’ calibrations were performed using random forest (RF) and partial least squares regression (PLSR). The results provide insights that MEMS could be employed for soil properties estimation, since the RF model demonstrated solid performance over both clay (R 2 = 0.85) and SOC (R 2 = 0.80). These findings pave the way for supporting daily agriculture applications and land related policies through the exploration of a wider set of soil properties.

Suggested Citation

  • Konstantinos Karyotis & Theodora Angelopoulou & Nikolaos Tziolas & Evgenia Palaiologou & Nikiforos Samarinas & George Zalidis, 2021. "Evaluation of a Micro-Electro Mechanical Systems Spectral Sensor for Soil Properties Estimation," Land, MDPI, vol. 10(1), pages 1-16, January.
  • Handle: RePEc:gam:jlands:v:10:y:2021:i:1:p:63-:d:479507
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    References listed on IDEAS

    as
    1. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    2. Antoine Stevens & Marco Nocita & Gergely Tóth & Luca Montanarella & Bas van Wesemael, 2013. "Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-13, June.
    3. Theodora Angelopoulou & Athanasios Balafoutis & George Zalidis & Dionysis Bochtis, 2020. "From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review," Sustainability, MDPI, vol. 12(2), pages 1-24, January.
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