Identification of salt-affected soils using remote sensing data through random forest technique: a case study from India
Author:
Funder
Indian Council of Agricultural Research
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences,General Environmental Science
Link
https://link.springer.com/content/pdf/10.1007/s12517-022-09682-3.pdf
Reference78 articles.
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2. Abdennour MA, Douaoui A, Piccini C, Pulido M, Bennacer A, Bradaï A, Barrena J, Yahiaoui I (2020) Predictive mapping of soil electrical conductivity as a proxy of soil salinity in south-east of Algeria. Environ Sustain Indic 8:100087. https://doi.org/10.1016/j.indic.2020.100087
3. Afrasinei GM, Melis MT, Buttau C, Bradd JM, Arras C, Ghiglieri G (2017) Assessment of remote sensing-based classification methods for change detection of salt-affected areas (Biskra area, Algeria). J Appl Remote Sens 11(1):016025. https://doi.org/10.1117/1.JRS.11.016025
4. Aldakheel YY (2011) Assessing NDVI spatial pattern as related to irrigation and soil salinity management in Al-Hassa Oasis, Saudi Arabia. J Indian Soc Remote 39(2):171–180. https://doi.org/10.1007/s12524-010-0057-z
5. Allbed A, Kumar L, Sinha P (2014) Mapping and modelling spatial variation in soil salinity in the Al Hassa Oasis based on remote sensing indicators and regression techniques. Remote Sens 6(2):1137–57. https://doi.org/10.3390/rs6021137
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