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<p>Current approaches for monitoring soil organic matter (SOM) exhibit limitations in long-term predictive accuracy and data efficiency. This study aims to develop a remote sensing framework that integrating Landsat imagery and three modeling algorithms (PLSR, RF, Cubist) to address these challenges, reduce sampling workload, and enable large scale soil fertility assessments. Feature selection via Boruta and recursive feature elimination (RFE) was applied to optimize model performance, with PLSR identified astheoptimal algorithm. The framework utilized long-term Landsat imagery (2007–2021) and an inter-annual migration learning approach to map SOM dynamics. PLSR achieved cross-year SOM prediction (R<sup>2</sup> = 0.51, RMSE = 3.97&#xa0;g/kg), enabling accurate mapping of non-sample years with minimal field data and long-term imagery. Analysis of SOM trends revealed a decade-long decline in the study area, strongly correlated with land-use intensity. The proposed inter-annual migration learning method demonstrates that SOM dynamics can be efficiently tracked using sparse sampling and time-series remote sensing, offering a scalable tool for soil fertility management and precision agriculture.</p>

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Monitoring the interannual dynamic changes of soil organic matter using long-term Landsat images

  • 期刊论文

  • 发表日期：

    2025年5月16日

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  • Chang Liu,
  • Qian Sun,
  • Chi Zhang,
  • Wentao Chen,
  • Xuzhou Qu,
  • Boyi Tang,
  • Kai Ma,
  • Xiaohe Gu

摘要

Current approaches for monitoring soil organic matter (SOM) exhibit limitations in long-term predictive accuracy and data efficiency. This study aims to develop a remote sensing framework that integrating Landsat imagery and three modeling algorithms (PLSR, RF, Cubist) to address these challenges, reduce sampling workload, and enable large scale soil fertility assessments. Feature selection via Boruta and recursive feature elimination (RFE) was applied to optimize model performance, with PLSR identified astheoptimal algorithm. The framework utilized long-term Landsat imagery (2007–2021) and an inter-annual migration learning approach to map SOM dynamics. PLSR achieved cross-year SOM prediction (R2 = 0.51, RMSE = 3.97 g/kg), enabling accurate mapping of non-sample years with minimal field data and long-term imagery. Analysis of SOM trends revealed a decade-long decline in the study area, strongly correlated with land-use intensity. The proposed inter-annual migration learning method demonstrates that SOM dynamics can be efficiently tracked using sparse sampling and time-series remote sensing, offering a scalable tool for soil fertility management and precision agriculture.