Proximal and remote sensor data fusion for in-depth salinization mapping in the Brazilian semiarid via machine learning.
Mapping the salinization in irrigated cropland is a challenging practice. As an alternative, data from proximal and remote sensors have been implemented together via datafusion and machine learning algorithms. The present work was carried out on a farm with 11 ha and used data from the proximal sensor EM38-MK2 associated with radar C-band data obtained by the Sentinel1 satellite. The salinization classes were created from electrical conductivity data measured at 35 points using a 50 x 50 m sampling grid and at three depths: 0 ? 10, 10 ? 30, and 30 ? 50 cm using conventional laboratory approach. The accuracy values of the class prediction models presented values between 0.66 and 0.74 and Kappa values between 0.43 and 0.59 using Random Forest. The salinization decreased in layers 0 - 10 and 10 - 30 cm due to implementing a surface drainage system but the depth 30 - 50 cm had the highest occurrence of Salic classes, with a potentially harmful effect on the roots.
| Principais autores: | , , , , |
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| Outros Autores: | |
| Formato: | Anais e Proceedings de eventos biblioteca |
| Idioma: | Ingles English |
| Publicado em: |
2022-02-22
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| Assuntos: | Sensoriamento Remoto, Remote sensing, |
| Acesso em linha: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1140303 |
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