Predicting Soil Organic Carbon Content by Combining Unmanned Aerial Vehicle Multispectral Images and Machine Learning Algorithms

文献类型： 外文期刊

第一作者： Qi, Guanghui

作者： Qi, Guanghui;Lu, Hangyu;Hu, Xiao;Zang, Yulong;Li, Xinju;Zhang, Jiguang

作者机构：

关键词： Soil organic carbon (SOC); Unmanned aerial vehicle (UAV); Red-edge; Modified spectral indices; Machine learning (ML)

期刊名称：JOURNAL OF SOIL SCIENCE AND PLANT NUTRITION （ 影响因子：3.1； 五年影响因子：3.6 ）

ISSN： 0718-9508

年卷期： 2025 年

页码：

收录情况： SCI

摘要： Accurate and rapid acquisition of soil organic carbon (SOC) content is important for field fertilization and management in tobacco fields. Spectral reflectance were extracted from unmanned aerial vehicle (UAV) remote sensing multispectral images and spectral indices were calculated. Red-edge bands were introduced to modify original indices. Modified spectral indices dominated by near-infrared and red-edge bands were formed. Three analysis approaches including gray correlation, dispersion and multicollinearity were used for variables determination. Four machine learning (ML) algorithms, including random forest (RF), support vector machine (SVM), back propagation neural networks (BPNN) and extreme gradient boosting (XGBoost), were employed to construct prediction models. Modified spectral indices have better correlation with SOC content. Models based on combination of three indices as variables have highest accuracy of all. The coefficient of determination (R2) and root mean square error (RMSE) were used to comprehensively evaluate the model accuracy. The best prediction models for tobacco fields a and b were constructed using RF algorithm (with R2 of 0.913 and 0.907, with RMSE of 0.504 and 0.525, respectively). In contrast, the best prediction models for tobacco fields c and d were constructed using XGBoost algorithm (with R2 of 0.923 and 0.915, with RMSE of 0.559 and 0.561, respectively). Through model validation, the optimal models hold the highest prediction accuracy with R2 of 0.923. The optimal models were used to obtain the spatial distribution of SOC. Using modified spectral indices is an effective approach for predicting SOC from UAV images.

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