Prediction of Vanadium Contamination Distribution Pattern Through Remote Sensing Image Fusion and Machine Learning

Soil vanadium contamination poses a significant threat to ecosystems. Hyperspectral remote sensing plays a critical role in extracting spectral features of heavy metal contamination, mapping its spatial distribution, and monitoring its trends over time. This study targets a vanadium-contaminated area in Panzhihua City, Sichuan Province. Soil sampling and spectral measurements occurred in the laboratory. Hyperspectral (Gaofen-5, GF-5) and multispectral (Gaofen-2, GF-2; Sentinel-2) images were acquired and preprocessed, and feature bands were extracted by combining laboratory spectral data. A dual-branch convolutional neural network (DB-CNN) fused hyperspectral and multispectral images and confirmed the fusion’s effectiveness. Six prevalent machine learning models were adopted, and a unified learning framework leveraged a Random Forest (RF) as a second-layer model to enhance the predictive performance of these base models. Both the base models and the ensemble learning model were evaluated based on predictive accuracy. The fusion process enhanced the predictive performance of the base models, improving R2 values for vanadium (V) and pentavalent vanadium (V5+) from 0.54 and 0.3 to 0.58 and 0.39, respectively, at a 4 m resolution. Further optimization using RF as a second-layer model to refine Extreme Trees (ETs) significantly increased R2 values to 0.83 and 0.75 for V and V5+, respectively, at this scale. The 934 nm and 464 nm wavelengths were identified as the most critical spectral bands for predicting soil vanadium contamination. This integrated approach robustly delineates the spatial distribution characteristics of V and V5+ in soils, facilitating precise monitoring and ecological risk assessments of vanadium contamination through a comparative analysis of predictive accuracy across diverse models.