Inversion model of soil salinity in alfalfa covered farmland based on sensitive variable selection and machine learning algorithms
Based on our findings, the variable selection combined with machine learning algorithm is an effective method to improve the accuracy of soil salinity remote sensing inversion, which provides a new approach for timely and accurate acquisition of crops covered farmland soil salinity information.
Authors
- Hong Mā (ORCID: https://orcid.org/0000-0001-8717-4422)
- Congcong Li (ORCID: https://orcid.org/0000-0002-0081-8553)
- Fangfang Ma (ORCID: https://orcid.org/0009-0000-0640-4485)
- Wenju Zhao (ORCID: https://orcid.org/0000-0002-7244-2276)
- Zongli Li
- Weicheng Duan
Institutions
- Lanzhou University of Technology (CN)
- China Institute of Water Resources and Hydropower Research (CN)
- Jiuquan Iron & Steel (China) (CN)
- Ministry of Agriculture and Rural Affairs (CN)
- Ministry of Water Resources of the People's Republic of China (CN)
Publication Details
- Journal
- PeerJ
- Published
- 2024-09-26
- DOI
- https://doi.org/10.7717/peerj.18186
- Citations
- 6
- Primary Topic
- Soil Geostatistics and Mapping
- Type
- article
- Field-Weighted Citation Impact
- 1.23
Funders
- National Natural Science Foundation of China