How Spectrally Nearby Samples Influence the Inversion of Soil Heavy Metal Copper

Monitoring soil heavy metal contamination in urban land to protect human health requires rapid and low-cost methods. Visible and infrared (vis-NIR) spectroscopy shows strong promise for monitoring metals such as copper (Cu). However, an important question is how “spectrally nearby” samples influence Cu estimation models. This study investigates that issue in depth. We collected 250 soil samples from Shenzhen City, China (the world’s tenth-largest city). During building the model, we selected spectrally nearby samples for each validation sample, varying the number of neighbors from 20 to 200 by adding one sample at a time. Results show that, compared with the traditional method, incorporating nearby samples substantially improved Cu prediction: the coefficient of determination in prediction (Rp2) increased from 0.75 to 0.92, and the root mean square error of prediction (RMSEP) decreased from 8.56 to 4.50 mg·kg−1. The optimal number of nearby samples was 125, representing 62.25% of the dataset. And the performance followed an L-shape curve as the number of neighbors increased—rapid improvement at first, then stabilization. We conclude that using spectrally nearby samples is an effective way to improve vis-NIR Cu estimation models. The optimal number of neighbors should balance model accuracy, robustness, and complexity.