Sentinel-2 Satellite Imagery for Retrieving and Mapping Soil Properties Using Machine Learning-Reference-Cited by-同舟云学术

Sentinel-2 Satellite Imagery for Retrieving and Mapping Soil Properties Using Machine Learning

Published:2023 Issue: Volume: Page:281-288
ISSN:2524-342X
Container-title:Springer Proceedings in Earth and Environmental Sciences
language:
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Author:

Amin Mohamed E. S.^ORCID,Abdelfattah M. A.,Mohamed E. S.,Nabil Mohsen,Belal A. A.,Ahmed Sayed,Samir Ehab,Mahmoud Ali G.

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-40447-4_32

Reference22 articles.

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3. Jalhoum ME-s, Hassan MAER, Farag FM, Abdou M, Abd Elsattar E, Emam M, et al. Modeling of Agro-Ecological Zones for Sustainable Agriculture Development in Halayeb Area, Egypt. Egyptian Journal of Soil Science. 2022;62(1):55–71.

4. Castaldi F, Palombo A, Santini F, Pascucci S, Pignatti S, Casa R. Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon. Remote Sensing of Environment. 2016;179:54-65.

5. Rossel RV, Walvoort D, McBratney A, Janik LJ, Skjemstad J. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma. 2006;131(1-2):59-75.