A comparison of machine learning algorithms for mapping soil iron parameters indicative of pedogenic processes by hyperspectral imaging of intact soil profiles

Abstract

AbstractSoil iron (Fe) performs vital functions in the biogeochemical cycles of soil environments. The amount and profile allocation of various Fe parameters can be used as sensitive indicators of soil development and pedogenic processes. This study aimed to evaluate the potential of ground‐based hyperspectral imaging (HSI: 400–1010 nm) spectroscopy to predict and map six Fe parameters indicative of pedogenic processes: total Fe (Fet), dithionite‐citrate‐bicarbonate (DCB)‐extracted Fe (Fed), oxalate‐extracted Fe (Feo), weathering index (FeW), active ratio (FeA) and crystallinity ratio (FeC). In total, 17 intact soil profiles at a depth of 100 ± 5 cm were collected to acquire HSI images. Four non‐linear machine learning techniques, namely, random forest (RF), XGBoost, CatBoost and support vector machine regression (SVMR), were implemented and compared with linear partial least squares (PLS) to identify the models with the best performance for different soil Fe parameters. Our results indicate that the four non‐linear machine learning models outperformed PLS for most soil Fe parameters, with low root mean square error (RMSE) and high Lin's concordance correlation coefficient (LCCC) values. Based on the testing set, SVMR showed better performance over the other tested models for Fet (RMSEP = 2.645 g kg−1, LCCCP = 0.89), Fed (RMSEP = 0.972 g kg−1, LCCCP = 0.97), Feo (RMSEP = 0.273 g kg−1, LCCCP = 0.97), FeW (RMSEP = 0.035, LCCCP = 0.97), FeA (RMSEP = 0.033, LCCCP = 0.97) and FeC (RMSEP = 0.031, LCCCP = 0.97). According to the LCCCP values, soil Fet was predicted to be in substantial agreement by SVMR, and the other soil Fe predictions were considered to be in near perfect agreement. Moreover, SVMR required lower computational costs. Given these results, the combination of HSI spectroscopy and SVMR is recommended due to its more reliable estimation and profile mapping of the selected soil Fe parameters than that of PLS, RF, XGBoost and CatBoost.Highlights Hyperspectral imaging was used to map six soil Fe parameters of intact profiles. Nonlinear machine learning models were compared to select the most suitable model. Nonlinear techniques outperformed the linear PLS model in most cases. SVMR showed higher comprehensive performance than other models. Hyperspectral imaging combined with SVMR is recommended to map soil Fe in profiles.