Random Forest-Based Fusion of Proximal and Orbital Remote Sensor Data for Soil Salinity Mapping in a Brazilian Semi-arid Region
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-64579-2_14
Reference23 articles.
1. Barreto, A. C., Neto, M. F., Oliveira, R. P., Moreira, L. C. J., Medeiros, J. F., & Sá, F. V. S. (2023). Comparative analysis of spectral indexes for soil salinity mapping in irrigated areas in a semi-arid region, Brazil. Journal of Arid Environments, 209, 104888.
2. Barros, M. F. C., Fontes, M. P. F., & Alvarez, V. H. (2004). Recuperação de solos afetados por sais pela aplicação de gesso de jazida e calcário no Nordeste do Brasil. Revista Brasileira Engenharia Agrícola e Ambiental, 8, 59–64.
3. Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.
4. Cohen, J. A. (1960). Coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37–46.
5. Ding, J., & Yu, D. (2014). Monitoring and evaluating spatial variability of soil salinity in dry and wet seasons in the Werigan-Kuqa Oasis, China, using remote sensing and electromagnetic induction instruments. Geoderma, 235–236, 316–322.
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