Transfer Learning-Based Interpretable Soil Lead Prediction in the Gejiu Mining Area, Yunnan.

Ping He, Xianfeng Cheng, Xingping Wen, Yan Yi, Zailin Chen, Yu Chen

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Ping HeFaculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China.

Xianfeng ChengSchool of Earth and Environmental Sciences, Yunnan Land and Resources Vocational College, Kunming 652501, China.

Xingping WenFaculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China.

Yan YiSchool of Fine Art and Design, Kunming University, Kunming 650214, China.

Zailin ChenSchool of Earth and Environmental Sciences, Yunnan Land and Resources Vocational College, Kunming 652501, China.

Yu ChenInternational Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China.ORCID 0000-0002-9095-243X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate prediction of soil lead (Pb) content in small sample scenarios is often limited by data scarcity and variability in soil properties, with traditional spectral modeling methods yielding suboptimal precision. To address this, we propose a transfer learning-based framework integrated with SHAP analysis for predicting soil Pb content in the Gejiu mining area, Yunnan. Using pH data from the European LUCAS soil database as the source domain, spectral features were extracted via a 1D-ResNet model and transferred to the target domain (130 soil samples from Gejiu) for Pb prediction. SHAP analysis was applied to clarify the role of spectral characteristics in cross-component transfer learning, uncovering shared and adaptive features between pH and Pb predictions. The transfer learning model (ResNet-pH-Pb) significantly outperformed direct modeling methods (PLS-Pb, SVM-Pb, and ResNet-Pb), with an R

Indexed as

SHAP analysissmall sample predictionsoil lead (Pb)transfer learning

Identifiers

PMID40648463

DOI10.3390/s25134209

PMCPMC12252230

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.