Estimation of soil free Iron content using spectral reflectance and machine learning algorithms

作者：Wanzhu Ma, Hongkui Zhou, Hao Hu, Zhiqing Zhuo, K. J. Zhu, Guangzhi Zhang · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-09301-7 · 被引用次数：2 · 研究领域：Soil Geostatistics and Mapping、Geochemistry and Geologic Mapping、Remote Sensing in Agriculture

Spectral reflectance technology has emerged as a promising tool for estimating soil properties while offering a rapid, non-destructive, and cost-effective alternative to traditional methods. Free iron is an important soil property, and it reflects the occurrence and evolution of soil. An accurate and efficient determination of soil free iron content is important. To evaluate the feasibility of using spectral reflectance and machine learning methods to estimate soil free iron content, we collected the spectral reflectance of 540 soil samples from 135 locations. We looked at the original spectrum and transforms such as the first derivative (FD), standard normal variate (SNV), and continuum removed (CR). The full spectrum, correlated spectrum, and principal components from principal component analysis (PCA) were considered as model variable selection. We used machine learning algorithms, such as partial least squares (PLS), support vector machine (SVM), random forest (RF), and deep neural network (DNN) algorithms for model construction. We found that FD was a more efficient transform than the original, SNV and CR spectra. The average R 2 , RMSE , and RRMSE when using the FD transform for training were 0.797, 5.550 g/kg, and 25.1%, respectively. In testing models, CR had a higher accuracy than the other transforms and its R 2 , RMSE , and RRMSE were 0.644, 7.140 g/kg, and 32.7%. Variable selection based on PCA projection improved model accuracy compared to using full and correlat...