The DOI matches its Crossref record; 87 of the 87 references it cites were found in a registry or an index.
Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review
Open accessAbstract
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.
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Cites
THE VERTICAL DISTRIBUTION OF SOIL ORGANIC CARBON AND ITS RELATION TO CLIMATE AND VEGETATION
Jobbágy, E. G., & Jackson, R. B. (2000) · Ecological Applications · doi:10.1890/1051-0761(2000)010[0423:tvdoso]2.0.co;2 · Cited by 5,428
Infrared Spectroscopy: Fundamentals and Applications
Stuart, B. H. (2004) · Analytical Techniques in the Sciences · doi:10.1002/0470011149 · Cited by 4,427
Machine Learning in Agriculture: A Review
Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018) · Sensors · doi:10.3390/s18082674 · Cited by 3,441
Self-citationClimate-smart agriculture for food security
Lipper, L., Thornton, P., Campbell, B. M., Baedeker, T., Braimoh, A., Bwalya, M., Caron, P., Cattaneo, A., Garrity, D., Henry, K., et al. (2014) · Nature Climate Change · doi:10.1038/nclimate2437 · Cited by 2,194
Viscarra Rossel, R. A., Walvoort, D. J. J., McBratney, A. B., Janik, L. J., & Skjemstad, J. O. (2006) · Geoderma · doi:10.1016/j.geoderma.2005.03.007 · Cited by 2,165
The application of small unmanned aerial systems for precision agriculture: a review
Zhang, C., & Kovacs, J. M. (2012) · Precision Agriculture · doi:10.1007/s11119-012-9274-5 · Cited by 2,012
Global soil carbon: understanding and managing the largest terrestrial carbon pool
Scharlemann, J. P., Tanner, E. V., Hiederer, R., & Kapos, V. (2014) · Carbon Management · doi:10.4155/cmt.13.77 · Cited by 1,612
Keesstra, S. D., Bouma, J., Wallinga, J., Tittonell, P., Smith, P., Cerdà, A., Montanarella, L., Quinton, J. N., Pachepsky, Y., van der Putten, W. H., et al. (2016) · SOIL · doi:10.5194/soil-2-111-2016 · Cited by 1,560
Visible and near infrared spectroscopy in soil science
Stenberg, B., Viscarra Rossel, R. A., Mouazen, A. M., & Wetterlind, J. (2010) · Advances in Agronomy · doi:10.1016/s0065-2113(10)07005-7 · Cited by 1,549
Organic Carbon in Soils of the World
Eswaran, H., Van Den Berg, E., & Reich, P. (1993) · Soil Science Society of America Journal · doi:10.2136/sssaj1993.03615995005700010034x · Cited by 1,458
Metadata
- Authors
- Angelopoulou, T., Tziolas, N., Balafoutis, A., Zalidis, G., & Bochtis, D.
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2019-03-21
- Volume
- 11
- Issue
- 6
- Pages
- 676
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W2923714778
- Cited by
- 382
- References
- 87
- FWCI
- 14.10
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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