Wetterlind, Johanna
- Department of Soil and Environment, Swedish University of Agricultural Sciences
Abstract
Soil spectroscopy with machine learning (ML) can estimate soil properties. Extensive soil spectral libraries (SSLs) have been developed for this purpose. However, general models built with those SSLs do not generalize well on new 'unseen' local data. The main reason is the different characteristics of the observations in the SSL and the local data, which cause their conditional and marginal distributions to differ. This makes the modelling of soil properties with spectra challenging. General models developed using large 'global' SSLs offer broad, systematic information on the soil-spectra relationships. However, to accurately generalize in a local situation, they must be adjusted to capture the site-specific characteristics of the local observations. Most current methods for 'localizing' spectroscopic modelling report inconsistent results. An understanding of spectroscopic 'localization' is lacking, and there is no framework to guide further developments. Here, we review current localization methods and propose their reformulation as a transfer learning (TL) undertaking. We then demonstrate the implementation of instance-based TL with RS-LOCAL 2.0 for modelling the soil organic carbon (SOC) content of 12 sites representing fields, farms and regions from 10 countries on the seven continents. The method uses a small number of instances or observations (measured soil property values and corresponding spectra) from the local site to transfer relevant information from a large and diverse global SSL (GSSL 2.0) with more than 50,000 records. We found that with
Keywords
Soil spectral library; vis-NIR spectra; Localization; Transfer learning; Soil organic carbon; Spectroscopic modelling; Machine learning; Multivariate statistics
Published in
Earth-Science Reviews
2024, volume: 254, article number: 104797
SLU Authors
Stenberg, Bo
- Department of Soil and Environment, Swedish University of Agricultural Sciences
- Department of Soil and Environment, Swedish University of Agricultural Sciences
UKÄ Subject classification
Analytical Chemistry
Soil Science
Environmental Sciences and Nature Conservation
Publication identifier
- DOI: https://doi.org/10.1016/j.earscirev.2024.104797
Permanent link to this page (URI)
https://res.slu.se/id/publ/130075