Integrating bi-dynamic strategy and multivariate deep learning algorithms to predict high-accuracy, long-term cropland soil organic matter

Yilin Bao, Xiangtian Meng, Xingnan Liu, Xue Wang, Zhengchao Qiu, Huanjun Liu, Mingchang Wang, Abdul Mounem Mouazen,
Integrating bi-dynamic strategy and multivariate deep learning algorithms to predict high-accuracy, long-term cropland soil organic matter,
International Soil and Water Conservation Research,
2025,
100598,
ISSN 2095-6339,
https://doi.org/10.1016/j.iswcr.2025.11.006.
(https://www.sciencedirect.com/science/article/pii/S2095633925001376)
Abstract: Soil organic matter (SOM) is a key indicator for assessing soil health and carbon neutrality, while environmental heterogeneity and dynamic sensitivity changes between SOM and environmental variables can reduce prediction accuracy. This study developed a deep learning model that accounts for spatial variability and sensitivity changes in the soil environment, using 2284 soil samples and 64,802 Landsat TM/OLI images as inputs. A bi-dynamic strategy is proposed, utilizing a Gaussian mixture model to dynamically partition the study area and account for changes in environmental heterogeneity across periods. A multivariate deep learning algorithm, T-CNN-GNN, which combines Transformer, a convolutional neural network (CNN), and a graph neural network (GNN), was developed to extract advanced spatio-temporal features, thereby enhancing the accuracy of long-term SOM spatial distribution predictions. The results showed that the integration of the bi-dynamic strategy and multivariate deep learning model achieved the highest prediction accuracy, with a root mean square error (RMSE) of 9.49 g/kg and a coefficient of determination (R2) of 0.77. The dynamic partitioning strategy effectively captured spatial variations in environmental heterogeneity across periods. Over the past 40 years, the SOM content in Northeast China decreased from 41.52 ± 0.24 g/kg to 37.94 ± 0.21 g/kg. This study showed that SOM maps generated by the bi-dynamic strategy closely matched measured SOM values, offering a promising method for long-term, high-accuracy soil mapping.
Keywords: Soil organic matter; Gaussian mixed partitioning; Structural equation modelling; Deep learning