Predicting soil clay content from NIR, gamma-ray and XRF curves.
In this study, data from NIR, gamma ray and XRF curves, and three multivariate methods (partial least squares regression - PLS, random forest - RF, and support vector machine - SVM) were used to predict soil clay content at 0-10-cm depth. Training and validation data included 103 and 25 samples, respectively. Gamma ray and XRF data were taken in situ at the soil surface, using portable sensors, whereas NIR reflectance curves (800-2500 nm) were measured from airdried fine earth samples in the laboratory.
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| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Anais e Proceedings de eventos biblioteca |
| Language: | English eng |
| Published: |
2019-11-25
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| Subjects: | Sensoriamento Remoto, |
| Online Access: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1115064 |
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