Top-soil organic carbon (SOC) prediction is fundamental to agricultural sustainability, land use policy and fertilization planning. Existing approaches face two limitations: they pair hand-crafted covariates with classical ML or single-modal deep models that miss rich spectral and temporal information, and grid-based architectures ignore the irregular spatial structure of field measurements. We introduce SpTGNN, a multi-modal spatio-temporal graph neural network addressing both. SpTGNN represents soil measurements as nodes in a heterogeneous graph with three edge types (spatial proximity, spectral similarity, elevation), and applies relational graph attention to learn separate patterns per relation. A fine-tuned TerraMind encoder extracts node features from Sentinel-2, Sentinel-1 and DEM signals, combined with per-sample environmental covariates and learned positional and temporal embeddings. A sparse Mixture-of-Experts module fuses the four streams via top-$k$ routing. Uncertainty is captured by pairing heteroscedastic regression (aleatoric) with deep ensembles (epistemic), and a Moran's $I$ penalty regularizes spatial autocorrelation. We evaluate on a global SOC corpus split into three regional instances ($\sim$49k samples globally, Africa $\sim$26k, Europe $\sim$14k). Our 5-member deep ensemble reports $R^2=0.762$, RMSE $=3.51\pm0.48$ g/kg and MAPE $=22.9\%$ on the Africa test split, improving over a tabular XGBoost baseline; the best single checkpoint reaches validation $R^2=0.864$. Ablations confirm the heterogeneous graph, MoE fusion and fine-tuned backbone each contribute substantively, and the ensemble UQ stack achieves post-calibration ECE of $0.031$ (hybrid) and $0.026$ ($β$-NLL). To our knowledge, this is the first framework to unify foundation-model feature extraction, heterogeneous graph attention and decomposed uncertainty quantification for SOC estimation.

翻译：表层土壤有机碳预测是农业可持续性、土地利用政策及施肥规划的基础。现有方法存在两大局限性：一方面将人工设计的协变量与经典机器学习或单模态深度模型结合，忽略了丰富的光谱与时序信息；另一方面基于网格的架构无法适应田间测量的不规则空间结构。我们提出SpTGNN——一种多模态时空图神经网络以解决上述问题。SpTGNN将土壤测量值表示为异构图中的节点，包含三类边（空间邻近性、光谱相似性、高程），并通过关系图注意力学习每种关系对应的独立模式。经微调的TerraMind编码器从Sentinel-2、Sentinel-1和DEM信号中提取节点特征，结合逐样本环境协变量以及学习得到的位置嵌入与时序嵌入。稀疏专家混合模块通过top-$k$路由融合四条数据流。通过配对异方差回归（偶然不确定性）与深度集成（认知不确定性）捕捉不确定性，并采用Moran's $I$惩罚项正则化空间自相关性。我们在划分为三个区域实例的全球SOC语料库上评估模型（全球约4.9万样本，非洲约2.6万，欧洲约1.4万）。基于5成员深度集成的模型在非洲测试集上报告$R^2=0.762$、RMSE=$3.51\pm0.48$ g/kg及MAPE=$22.9\%$，优于表格数据XGBoost基线；最佳单一检查点达到验证$R^2=0.864$。消融实验证实异构图、MoE融合及微调骨干网络均贡献显著，集成不确定性量化栈在校准后达到ECE为$0.031$（混合）和$0.026$（$\beta$-NLL）。据我们所知，这是首个统一基础模型特征提取、异构图注意力与分解不确定性量化进行SOC估计的框架。