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

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 (R 2 ) 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.