A bibliometric analysis of research on remote sensing-based monitoring of soil organic matter conducted between 2003 and 2023

Abstract

Soil organic matter (SOM) is a key metric for assessing soil quality and crop yield potential. It plays a vital role in maintaining the ecological balance environment and promoting sustainable farming practices. This review examines the evolving trends in remote sensing ( RS )-based SOM monitoring by analyzing 739 scholarly publications from the Web of Science database from 2003 to 2023 using a bibliometric approach. The study reveals that research on RS-based SOM monitoring has entered a rapid growth phase since 2018, with China and the United States as the main contributors and an extensive international cooperation network. In model construction, high frequency covariates such as soil pH, precipitation, temperature, and topography significantly improved the prediction accuracy. Data preprocessing methods such as Standard Normal Variables (SNV), Principal Component Analysis (PCA), and Multiple Scattering Correction (MSC) enhanced data consistency. Traditional statistical models are gradually being replaced by nonlinear machine learning and deep learning methods (CNN, XGBoost, andStacking), which are particularly good at handling complex high-dimensional data. Regional spectral libraries (OzSoil and AfSIS) excel in local accuracy, while global spectral libraries (ISRIC and LUCAS) are more suitable for cross-region modeling, and the migration learning technique effectively improves the model generalization ability in low data regions. Integrated models (CNN-LSTM and GAN) have significant advantages in capturing the spatial and temporal dynamics of SOMs, and uncertainty quantification methods (Bayesian inference, Monte Carlo simulation) enhance the reliability of the models in multi-source data and data-scarce scenarios. Future research should focus on further optimization of multi-source data fusion and uncertainty quantification to promote the development and applicability of RS-based SOM monitoring techniques for precision soil management and sustainable agriculture. • Rapid growth in RS-based SOM monitoring since 2018, led by China and the US with strong international collaboration. • High-frequency covariates and preprocessing techniques (SNV, PCA, MSC) significantly improve prediction accuracy. • Machine learning models (CNN, XGBoost, Stacking) and integrated models (CNN-LSTM, GAN) replace traditional methods, with uncertainty quantification enhancing robustness. • Spectral libraries (OzSoil, ISRIC, AfSIS, LUCAS) improve SOM monitoring accuracy at both local and global scales. • Future research should focus on data fusion, hybrid methods, transfer learning, and uncertainty quantification.

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