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

文献类型： 外文期刊

作者： Liu, Chang 1 ; Sun, Qian 3 ; Zhang, Chi 2 ; Chen, Wentao 2 ; Qu, Xuzhou 2 ; Tang, Boyi 2 ; Ma, Kai 2 ; Gu, Xiaohe 2 ;

作者机构： 1.Shenyang Agr Univ, Coll Informat & Elect Engn, Shenyang 110866, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

3.Yangzhou Univ, Agr Coll, Res Inst Smart Agr, Jiangsu Coinnovat Ctr Modern Prod Technol Grain Cr, Yangzhou 225009, Peoples R China

关键词： Soil organic matter; Remote sensing; Machine learning; Transfer learning; Spatial-temporal change

期刊名称：PRECISION AGRICULTURE （ 影响因子：6.6； 五年影响因子：7.4 ）

ISSN： 1385-2256

年卷期： 2025 年 26 卷 3 期

页码：

收录情况： SCI

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.

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