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Research article - Peer-reviewed, 2022

Deep transfer learning of global spectra for local soil carbon monitoring

Shen, Zefang; Ramirez-Lopez, Leonardo; Behrens, Thorsten; Cui, Lei; Zhang, Mingxi; Walden, Lewis; Wetterlind, Johanna; Shi, Zhou; Sudduth, Kenneth A.; Song, Yongze; Catambay, Kevin; Rossel, Raphael A. Viscarra;

Abstract

There is global interest in spectroscopy and the development of large and diverse soil spectral libraries (SSL) to model soil organic carbon (SOC) and monitor, report, and verify (MRV) its changes. The reason is that increasing SOC can improve food production and mitigate climate change. However, 'global' modelling of SOC with such diverse and hyperdimensional SSLs do not generalise well locally, e.g. at a field scale. To address this challenge, we propose deep transfer learning (DTL) to leverage useful information from large-scale SSLs to assist local modelling. We used one global, three country-specific SSLs and data from three local sites with DTL to improve the modelling and localise the SOC estimates in individual fields or farms in each country. With DTL, we transferred instances from the SSLs, representations from one-dimensional convolutional neural networks (1DCNNs) trained on the SSLs, and both instances and representations to improve local modelling. Transferring instances effectively used information from the global SSL to most accurately estimate SOC in each site, reducing the root mean square error (RMSE) by 25.8% on average compared with local modelling. Our results highlight the effectiveness of DTL and the value of diverse, global SSLs for accurate local SOC predictions. Applying DTL with a global SSL one could estimate SOC anywhere in the world more accurately, rapidly, and cost-effectively, enabling MRV protocols to monitor SOC changes.

Keywords

Soil organic carbon; Visible -near-infrared spectra; Transfer learning; Deep learning; Spectral library

Published in

ISPRS Journal of Photogrammetry and Remote Sensing

2022, volume: 188, pages: 190-200
Publisher: ELSEVIER

Authors' information

Shen, Zefang
Curtin University
Ramirez-Lopez, Leonardo
BUCHI Labortechn AG
Behrens, Thorsten
Bern University of Applied Sciences
Cui, Lei
Curtin University
Zhang, Mingxi
Curtin University
Walden, Lewis
Curtin University
Swedish University of Agricultural Sciences, Department of Soil and Environment
Shi, Zhou
Zhejiang University
Sudduth, Kenneth A.
United States Department of Agriculture (USDA)
Song, Yongze
Curtin University
Catambay, Kevin
Curtin University
Rossel, Raphael A. Viscarra
Curtin University

Sustainable Development Goals

SDG13 Climate action

UKÄ Subject classification

Remote Sensing
Soil Science

Publication Identifiers

DOI: https://doi.org/10.1016/j.isprsjprs.2022.04.009

URI (permanent link to this page)

https://res.slu.se/id/publ/117328