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）是一种先进的集成统计建模与机器学习（ML）等前沿技术的方法，旨在精确描述土壤属性及其空间分布。土壤有机碳（SOC）作为关键的土壤属性，为土壤健康、养分循环、温室气体排放及整体生态系统生产力提供了重要见解。本研究强调了时空深度学习（DL）技术在DSM框架中的重要性。提出了一种新颖架构，该架构利用基于卷积神经网络（CNN）基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，用于预测欧洲范围的SOC。模型采用一组综合环境特征作为输入，包括Landsat-8影像、地形、遥感指数及气候时间序列。结果表明，所提出的框架优于DSM中常用的随机森林等传统机器学习方法，具有更低的均方根误差（RMSE）。该模型作为预测SOC的稳健工具，可用于其他土壤属性，从而推动DSM技术的发展，并基于准确信息促进土地管理与决策进程。