SoilNet: An Attention-based Spatio-temporal Deep Learning Framework for
  Soil Organic Carbon Prediction with Digital Soil Mapping in Europe
Nafiseh Kakhani Moien Rangzan Ali Jamali Sara Attarchi Seyed Kazem Alavipanah Thomas Scholten
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.