###### Abstract  
The accurate prediction of soil organic matter (SOM) content is essential for promoting sustainable soil management and addressing global climate change. Due to multiple factors such as topography and climate, especially in mountainous areas, SOM spatial prediction faces significant challenges. The main novelty of this study lies in proposing a geographic positional encoding mechanism that embeds geographic location information into the feature representation of a Transformer model. The encoder structure is further modified to enhance spatial awareness, resulting in the development of the Geo-Positional Transformer (GPTransformer). Furthermore, this model is integrated with a 1D-CNN to form a dual-branch neural network called the Geo-Positional Transformer-CNN (GPTransCNN). This study collected 1490 topsoil samples (0-20 cm) from cultivated land in Longshan County to develop a predictive model for mapping the spatial distribution of SOM across the entire cultivated area. Different models were comprehensively evaluated through ten-fold cross-validation, ablation experiments, and uncertainty analysis. The results show that GPTransCNN has the best performance, with an R\({}^{2}\) improvement of approximately 43% over the Transformer, 19% over the GPTransformer, and 15% over the 1D-CNN. This study demonstrates that by incorporating geographic positional information, GPTransCNN effectively combines the global modeling capabilities of the GPTransformer with the local feature extraction strengths of the 1D-CNN, which can improve the accuracy of SOM mapping in mountainous areas. This approach provides data support for sustainable soil management and decision-making in response to global climate change. digital soil mapping; soil organic matter; cultivated land; deep learning; environmental modeling; uncertainty +
Footnote †: journal: Applied sciences  
0000-0001-6000-0000]X.X. can improve the soil carbon sink capacity, contributing to the mitigation of greenhouse gas emissions and alleviating the adverse impacts of climate change [5; 6]. Exploring the spatial distribution of SOM and achieving accurate SOM prediction are crucial for promoting sustainable land management, tackling climate change, and improving agricultural productivity [7]. Traditional soil mapping relies on soil experts conducting field surveys to mentally construct a soil-landscape model, followed by manual delineation based on topographic maps, aerial photographs, or satellite imagery [8]. This approach is not only time-consuming and labor-intensive but also limited in accuracy. With the advancement of geographic information technologies and statistical modeling, digital soil mapping (DSM) has emerged as a more efficient, data-driven mapping method [9; 10]. The introduction of the scorpion-SSPFe framework (soil spatial prediction function with spatially autocorrelated errors) has provided a theoretical foundation for the development of DSM and laid the groundwork for the spatial modeling of soil properties [11]. In recent years, the rapid development of remote sensing technologies has enabled DSM to integrate multi-source environmental information (e.g., topography, vegetation, hydrology), thereby improving the spatial accuracy of soil maps [12; 13; 14; 15]. Particularly for SOM prediction, recent studies have employed machine learning methods such as Random Forest [16] and XGBoost [17] in combination with multi-source remote sensing data to enhance prediction accuracy. As a prominent branch of machine learning, deep learning offers superior capabilities in nonlinear modeling and spatial feature extraction. It has demonstrated improved predictive performance over traditional approaches in the estimation of SOM [18; 19; 20]. The spatial variation in soil properties exhibits scale-dependent patterns [21], with SOM distribution being driven by multi-scale spatial factors [22]. These include large-scale geological and climatic conditions as well as small-scale topographic features and biological processes [23].