Next Article in Journal
Improving an Extreme Rainfall Detection System with GPM IMERG data
Previous Article in Journal
Imaging Thermal Anomalies in Hot Dry Rock Geothermal Systems from Near-Surface Geophysical Modelling
Previous Article in Special Issue
Multi-Temporal Satellite Images on Topsoil Attribute Quantification and the Relationship with Soil Classes and Geology
Article Menu
Issue 6 (March-2) cover image

Export Article

Open AccessReview

Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review

1
Centre of Research and Technology—Hellas (CERTH), Institute for Bio—Economy and Agri—Technology (iBO), Thessaloniki, 57001 Thermi, Greece
2
Laboratory of Remote Sensing, Spectroscopy, and GIS, Department of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
3
Interbalkan Environment Center (i-BEC), 18 Loutron Str., 57200 Lagadas, Greece
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(6), 676; https://doi.org/10.3390/rs11060676
Received: 28 February 2019 / Revised: 14 March 2019 / Accepted: 17 March 2019 / Published: 21 March 2019
  |  
PDF [1219 KB, uploaded 26 March 2019]
  |  

Abstract

Towards the need for sustainable development, remote sensing (RS) techniques in the Visible-Near Infrared–Shortwave Infrared (VNIR–SWIR, 400–2500 nm) region could assist in a more direct, cost-effective and rapid manner to estimate important indicators for soil monitoring purposes. Soil reflectance spectroscopy has been applied in various domains apart from laboratory conditions, e.g., sensors mounted on satellites, aircrafts and Unmanned Aerial Systems. The aim of this review is to illustrate the research made for soil organic carbon estimation, with the use of RS techniques, reporting the methodology and results of each study. It also aims to provide a comprehensive introduction in soil spectroscopy for those who are less conversant with the subject. In total, 28 journal articles were selected and further analysed. It was observed that prediction accuracy reduces from Unmanned Aerial Systems (UASs) to satellite platforms, though advances in machine learning techniques could further assist in the generation of better calibration models. There are some challenges concerning atmospheric, radiometric and geometric corrections, vegetation cover, soil moisture and roughness that still need to be addressed. The advantages and disadvantages of each approach are highlighted and future considerations are also discussed at the end. View Full-Text
Keywords: soil spectroscopy; soil organic carbon; VNIR–SWIR; machine learning; earth observation soil spectroscopy; soil organic carbon; VNIR–SWIR; machine learning; earth observation
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Angelopoulou, T.; Tziolas, N.; Balafoutis, A.; Zalidis, G.; Bochtis, D. Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review. Remote Sens. 2019, 11, 676.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top