Abstract
Accurate mapping of soil physical and chemical properties (SPCPs) constitutes a fundamental prerequisite for parameterizing the terrestrial water cycle in Earth system models, but continental-scale prediction is hindered by sparse observations and noisy legacy soil maps; it remains a formidable scientific bottleneck. Here, we address this challenge through a structure-aware Weakly-Supervised Learning (WSL) framework that treats legacy soil maps not as ground truth, but as imperfect, noisy priors. We introduce SoilViT, a specialized Vision Transformer architecture that integrates a dual-head disentanglement mechanism with hybrid geographic encoding to disentangle latent pedogenic signals from spatially structured errors. By enforcing edge-preserving total variation regularization, the model autonomously identifies and discards interpolation striping while preserving genuine high-frequency soil texture across diverse biogeographical gradients. Quantitative assessment reveals that our approach achieves an average R2 of 0.8630 and RMSE of 0.0248. Notably, when validated against external profile data, the framework yields an R2 improvement of up to 17.35%, with markedly reduced spatial autocorrelation of residuals. We demonstrate the framework’s robust performance by generating a 500 m resolution SPCPs dataset across China, which inherently corrects the systematic striping errors found in legacy inventories. These results demonstrate a scalable, resource-efficient pathway for global environmental monitoring in data-scarce regions.
Acknowledgements
This research is partly supported by Jing-Jin-Ji Regional Integrated Environmental Improvement-National Science and Technology Major Project (2025ZD1208302), National Natural Science Foundation of China (42405046, 42501588), Natural Science Foundation of Guangdong Province (2026A1515010090), the Youth Innovation Team of China Meteorological Administration (Grant No. CMA20230N15), Suzhou Key Laboratory of Artificial Intelligence and Social Governance Technologies (SZS2023007), Smart Social Governance Technology and Innovative Application Platform (YZCXPT2023101), and Public Computing Cloud, Renmin University of China.
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Zhang, L., Ouyang, Z., Yue, S. et al. Transforming noisy archives into high-fidelity soil physical and chemical property maps: a structure-aware weakly supervised framework.
npj Artif. Intell. (2026). https://doi.org/10.1038/s44387-026-00125-y
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DOI: https://doi.org/10.1038/s44387-026-00125-y
Source: Ecology - nature.com
