Digital soil mapping (DSM) is an advanced approach that integrates statistical modeling and cutting-edge technologies, including machine learning (ML) methods, to accurately depict soil properties and their spatial distribution. Soil organic carbon (SOC) is a crucial soil attribute providing valuable insights into soil health, nutrient cycling, greenhouse gas emissions, and overall ecosystem productivity. This study highlights the significance of spatial-temporal deep learning (DL) techniques within the DSM framework. A novel architecture is proposed, incorporating spatial information using a base convolutional neural network (CNN) model and spatial attention mechanism, along with climate temporal information using a long short-term memory (LSTM) network, for SOC prediction across Europe. The model utilizes a comprehensive set of environmental features, including Landsat-8 images, topography, remote sensing indices, and climate time series, as input features. Results demonstrate that the proposed framework outperforms conventional ML approaches like random forest commonly used in DSM, yielding lower root mean square error (RMSE). This model is a robust tool for predicting SOC and could be applied to other soil properties, thereby contributing to the advancement of DSM techniques and facilitating land management and decision-making processes based on accurate information.

翻译：数字土壤制图（DSM）是一种先进方法，它整合了统计建模与包括机器学习（ML）方法在内的尖端技术，以精确描绘土壤属性及其空间分布。土壤有机碳（SOC）是一个关键的土壤属性，为土壤健康、养分循环、温室气体排放以及整体生态系统生产力提供了宝贵的见解。本研究强调了时空深度学习（DL）技术在DSM框架内的重要性。我们提出了一种新颖的架构，该架构利用基础卷积神经网络（CNN）模型和空间注意力机制整合空间信息，同时利用长短期记忆（LSTM）网络整合气候时序信息，用于预测整个欧洲的SOC。该模型利用了一套全面的环境特征作为输入，包括Landsat-8图像、地形、遥感指数和气候时间序列。结果表明，所提出的框架优于DSM中常用的传统ML方法（如随机森林），产生了更低的均方根误差（RMSE）。该模型是预测SOC的稳健工具，并可应用于其他土壤属性，从而有助于推动DSM技术的发展，并促进基于准确信息的土地管理和决策过程。