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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #389365

Research Project: Sustainable Intensification of Cropping Systems on Spatially Variable Landscapes and Soils

Location: Cropping Systems and Water Quality Research

Title: Deep transfer learning of global spectra for local soil carbon monitoring

item SHEN, ZEFANG - Curtin University
item RAMIREZ-LOPEZ, LEONARDO - Buchi Laboratories
item BEHRENS, THORSTEN - Bern University Of Applied Sciences
item CUI, LEI - Curtin University
item ZHANG, MINGXI - Curtin University
item WALDEN, LEWIS - Curtin University
item WETTERLIND, JOHANNA - Swedish University Of Agricultural Sciences
item SHI, ZHOU - Zhejiang University
item Sudduth, Kenneth - Ken
item BAUMANN, PHILIPP - Bern University Of Applied Sciences
item SONG, YONGZE - Curtin University
item CATAMBAY, KEVIN - Curtin University
item VISCARRA ROSSEL, RAPHAEL - Curtin University

Submitted to: Journal of Photogrammetry and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/14/2022
Publication Date: 4/26/2022
Citation: Shen, Z., Ramirez-Lopez, L., Behrens, T., Cui, L., Zhang, M., Walden, L., Wetterlind, J., Shi, Z., Sudduth, K.A., Baumann, P., Song, Y., Catambay, K., Viscarra Rossel, R.A. 2022. Deep transfer learning of global spectra for local soil carbon monitoring. Journal of Photogrammetry and Remote Sensing. 188:190-200.

Interpretive Summary: Interest in soil carbon sequestration as a way to mitigate climate change has led to the need to measure and monitor soil organic carbon (SOC) levels. One rapid and non-destructive approach to SOC measurement is visible and near infrared (vis-NIR) spectroscopy. Various modeling approaches have been used to relate vis-NIR soil spectral reflectance to SOC, including models based on soil spectral libraries. These spectral libraries generally consist of data from samples collected over national, continental, or global scales. Because of scale differences, it is often difficult to obtain good local (farm or field) estimates of SOC. In this research, we applied machine learning methods called "deep transfer learning" (DTL) to a global soil spectral library and developed local SOC estimates for individual farms or fields in four countries. The DTL methods successfully extracted information that was most directly applicable for estimating SOC in the test fields and provided improved results compared to other methods. This new DTL approach will enhance the value of existing soil spectral libraries for estimating SOC at local scales as needed for accurate soil carbon monitoring.

Technical Abstract: There is global interest in spectroscopy and the development of There is global interest in spectroscopy and the development of large and diverse soil spectral libraries (SSL) to model soil organic carbon (SOC) and measure, monitor, report, and verify 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). DTL can leverage large SSLs by transferring instances and representations from convolutional neural networks trained on the SSLs. We used one global and four country-specific SSLs with DTL to improve the modelling and localise the SOC estimates in individual fields or farms in each country. Transferring instances effectively used information from the global SSL to most accurately estimate SOC in each site. Our results highlight the effectiveness of DTL and the value of diverse, global SSLs for accurate local predictions.