Using process-oriented model output to enhance machine learning-based soil organic carbon prediction in space and time.
- DOI
- 10.1016/j.scitotenv.2024.170778
- Published
- 2024 Apr 20
- Container
- The Science of the total environment
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.scitotenv.2024.170778,
title = {Using process-oriented model output to enhance machine learning-based soil organic carbon prediction in space and time.},
author = {Zhang L and Heuvelink GBM and Mulder VL and Chen S and Deng X and Yang L},
year = {2024},
journal = {The Science of the total environment},
doi = {10.1016/j.scitotenv.2024.170778},
url = {https://doi.org/10.1016/j.scitotenv.2024.170778}
}RIS
TY - JOUR TI - Using process-oriented model output to enhance machine learning-based soil organic carbon prediction in space and time. AU - Zhang L AU - Heuvelink GBM AU - Mulder VL AU - Chen S AU - Deng X AU - Yang L PY - 2024 JO - The Science of the total environment DO - 10.1016/j.scitotenv.2024.170778 UR - https://doi.org/10.1016/j.scitotenv.2024.170778 ER -
APA
L, Z., GBM, H., VL, M., S, C., X, D., & L, Y. (2024). Using process-oriented model output to enhance machine learning-based soil organic carbon prediction in space and time.. The Science of the total environment. https://doi.org/10.1016/j.scitotenv.2024.170778
Source records
- pubmed · retrieved 2026-09-26T08:13:22.088Z