Iron-removal learning machine for multicolor determination of soil organic carbon-Reference-Cited by-同舟云学术

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Iron-removal learning machine for multicolor determination of soil organic carbon

Published:2024-03-12 Issue:5 Volume:24 Page:2058-2067
ISSN:1439-0108
Container-title:Journal of Soils and Sediments
language:en
Short-container-title:J Soils Sediments

Author:

Dai Ziqiang,Liu Xixi^ORCID,Ding Yanliu

Funder

National Natural Science Foundation of China

Open Fund of Research Platform of Grain Information Processing Center of Henan University of Technology

National Key R&D Program of China

Key Projects of Science and Technology Research of Henan Province

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11368-024-03770-5.pdf

Reference56 articles.

1. Aichi H, Fouad Y, Causeur D, Walter C (2020) Organic carbon and total iron effect on soil vis-SWNIR spectra and quantification of their contents using PLS R models. Commun Soil Sci Plant Anal 51:1253–1267

2. Aitkenhead M, Cameron C, Gaskin G, Choisy B, Coull M, Black H (2018) Digital RGB photography and visible-range spectroscopy for soil composition analysis. Geoderma 313:265–275. https://doi.org/10.1016/j.geoderma.2017.11.020

3. Allo M, Todoroff P, Jameux M, Stern M, Paulin L, Albrecht A (2020) Prediction of tropical volcanic soil organic carbon stocks by visible-near- and mid-infrared spectroscopy. CATENA 189:1–14. https://doi.org/10.1016/j.catena.2020.104452

4. Allory V, Cambou A, Moulin P, Schwartz C, Cannavo P, Vidal-Beaudet L, Barthes BG (2019) Quantification of soil organic carbon stock in urban soils using visible and near infrared reflectance spectroscopy (VNIRS) in situ or in laboratory conditions. Sci Total Environ 686:764–773. https://doi.org/10.1016/j.scitotenv.2019.05.192

5. Belgiu M, Dragut L (2016) Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogramm Remote Sens 114:24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011

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