A novel model for mapping soil organic matter: Integrating temporal and spatial characteristics

Abstract

Mapping the spatial distribution of soil organic matter (SOM) content is crucial for land management decisions, yet its accurate mapping faces challenges due to complex soil-environment relationships and temporal feature capture limitations in machine learning models. This study focuses on the typical black soil region in Northeast China, specifically using Youyi Farm as the main research area and Heshan Farm as the transfer research area. A novel approach is proposed that combines the CNN-LSTM model with a Cosine Annealing Warm Restarts learning rate (CNN-LSTM-CAWR) to enhance the accuracy of SOM mapping. In this model, the Convolutional Neural Network (CNN) extracts spatial context features from static variables (e.g., climate and terrain variables), while the Long Short-Term Memory (LSTM) network captures temporal features from dynamic variables (e.g., Sentinel-2 time series from April to October). The incorporation of the CAWR learning rate helps alleviate overfitting issues. Comparing the CNN-LSTM model, CNN model, and traditional RF model, the results show that the CNN-LSTM-CAWR model achieves the highest accuracy within research Area 1 (R2 = 0.64, RMSE = 0.54 %) and maintains strong performance in the transfer research area (R2 = 0.60, RMSE = 0.57 %). CNN-LSTM-CAWR demonstrates faster convergence, thereby improving mapping precision and effectively utilizing temporal information from features to enhance overall model performance. This study underscores the significant potential of the hybrid CNN-LSTM with CAWR model, highlighting the valuable information for SOM mapping contained within Sentinel-2 time series data.