Localising soil spectroscopic modelling with a global library

Curtin MLS Symposium, 03/12/2021.

Presented a new Deep Transfer Learning approach to improve the accuracy of local spectroscopic models for estimating soil organic carbon with a large global soil spectral library.

The study was later published: (Shen et al., 2022)

References

2022

P&RS

Deep transfer learning of global spectra for local soil carbon monitoring

Zefang Shen, Leonardo Ramirez-Lopez, Thorsten Behrens, and 8 more authors

ISPRS Journal of Photogrammetry and Remote Sensing, 2022

Bib HTML

@article{shen2022deep,
  title = {Deep transfer learning of global spectra for local soil carbon monitoring},
  author = {Shen, Zefang and Ramirez-Lopez, Leonardo and Behrens, Thorsten and Cui, Lei and Zhang, Mingxi and Walden, Lewis and Wetterlind, Johanna and Shi, Zhou and Sudduth, Kenneth A and Baumann, Philipp and others},
  journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
  volume = {188},
  pages = {190--200},
  year = {2022},
  publisher = {Elsevier},
}