Abstract:

[Objective] To explore the feasibility of using low-altitude unmanned aerial vehicle (UAV) hyperspectral remote sensing to estimate soil copper (Cu) content in contaminated areas and to provide technical support for the rapid assessment and remediation of contaminated sites. [Methods] An abandoned non-ferrous metal processing plant in Yinzhou District, Tieling City, Liaoning Province, was selected as the study area. A quantitative inversion method for soil Cu content was developed by integrating spectral transformation, feature-band selection, and spectral indices. Three modeling strategies were compared: four two-dimensional spectral indices (2D), four three-dimensional vegetation spectral indices (3D SI), and four newly constructed three-dimensional spectral indices (3D BI). The Newton-Raphson optimized eXtreme Gradient Boosting (NRBO-XGBoost) algorithm was used to establish the Cu content inversion model. [Results] Spectral transformation significantly improved the correlations between spectral variables and Cu content. The first-order derivative transformation yielded the strongest correlation, with ABI3 (850, 885, 520) exhibiting an r value of −0.570. Under the same preprocessing conditions, the 3D vegetation spectral indices and newly constructed 3D spectral indices showed better correlations with Cu content and better modeling performance than the 2D spectral indices. The NRBO-XGBoost model combined with the FD-3D SI dataset achieved the best performance (R² = 0.933, σ_RMSE = 75 mg·kg⁻¹), substantially outperforming the conventional XGBoost model (R² = 0.833, σ_RMSE = 116 mg·kg⁻¹). [Conclusion] The UAV hyperspectral estimation model based on NRBO-XGBoost can rapidly estimate soil Cu contamination and generate regional distribution maps. These results demonstrate the potential of UAV hyperspectral remote sensing for monitoring soil heavy metal contamination and provide an effective technical approach for the efficient assessment and remediation of contaminated sites.

Key words: UAV, hyperspectral, spectral index, model, soil heavy metal copper (Cu), inversion

摘要：

【目的】探讨利用低空无人机平台对污染区域土壤重金属铜（Cu）含量进行遥感估算的可行性，为污染场地快速评估与修复提供技术支撑，【方法】以辽宁省铁岭市银州区某废弃有色金属加工厂为研究区，提出一种结合光谱变换、特征波段筛选及光谱指数的土壤Cu含量定量反演方法。比较三类建模策略：4种二维光谱指数（2D）、4种三维植被光谱指数（3D-SI）和4种新三维光谱指数（3D-BI），并采用牛顿−拉夫逊优化极致梯度提升（NRBO-XGBoost）算法建立Cu含量反演模型。【结果】光谱变换显著提升光谱变量与Cu含量的相关性，其中一阶导数变换下ABI3（850，885，520）相关性最佳（−0.570）。同一预处理下，3D植被指数和新三维光谱指数与Cu含量的相关性及模型效果均优于2D光谱指数。NRBO-XGBoost结合FD-3D-SI数据集模型性能最优（R²=0.933，RMSE=75 mg·kg⁻¹），显著优于XGBoost模型（R²=0.833，RMSE=116 mg·kg⁻¹）。【结论】基于NRBO-XGBoost的无人机高光谱估算模型可快速预测研究区重金属污染并实现区域填图，证实了无人机高光谱数据在重金属污染监测中的潜力，可为污染场地的高效评估与修复提供有效技术手段。

关键词: 无人机, 高光谱, 光谱指数, 模型, 土壤重金属铜（Cu）, 反演