Using Legacy Soil Data to Plan New Data Collection: Study Case of Rio de Janeiro State: Brazil
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-64579-2_8
Reference12 articles.
1. Ferreira, A. C. de, S., Ceddia, M. B., Costa, E. M., Pinheiro, É. F. M., do Nascimento, M. M., & Vasques, G. M. (2022). Use of airborne radar images and machine learning algorithms to map soil clay, silt, and sand contents in remote areas under the Amazon rainforest. Remote Sensing, 14, 5711. https://doi.org/10.3390/rs14225711
2. Ferreira, A. C. S., Pinheiro, É. F. M., Costa, E. M., & Ceddia, M. B. (2023). Predicting soil carbon stock in remote areas of the Central Amazon region using machine learning techniques. Geoderma Regional, 32, e00614. https://doi.org/10.1016/j.geodrs.2023.e00614
3. Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37, 4302–4315. https://doi.org/10.1002/joc.5086
4. Gower, J. C. (1971). A general coefficient of similarity and some of its properties. Biometrics, 27, 857. https://doi.org/10.2307/2528823
5. Grunwald, S., Yu, C., & Xiong, X. (2018). Transferability and scalability of soil Total carbon prediction models in Florida, USA. Pedosphere, 28, 856–872. https://doi.org/10.1016/S1002-0160(18)60048-7
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