Aerospace Information Research Institute, Chinese Academy of Sciences, No.20 Datun Road, Chaoyang District, Beijing 100101, China; University of Chinese Academy of Sciences, No.3 Datun Road, Chaoyang District, Beijing 100101, China.
Aerospace Information Research Institute, Chinese Academy of Sciences, No.20 Datun Road, Chaoyang District, Beijing 100101, China.
Sci Total Environ. 2022 Sep 10;838(Pt 2):156129. doi: 10.1016/j.scitotenv.2022.156129. Epub 2022 May 21.
Soil heavy metal distribution maps can provide decision-making information for pollution control and agricultural management. However, the estimation of heavy metals is sensitive to the resolution of the soil spectra due to their sparse content in soils. The purposes of this study were to test the sensitivity of Ni, Zn and Pb prediction results to variations in spectral resolution, then to map their spatial distributions over a large area. In addition, the effectiveness of spectral feature extraction was investigated. In total, 92 soil samples and corresponding field soil spectra were obtained from the Tongwei-Zhuanglang area in Gansu Province, China. Airborne HyMap hyperspectral image of this area was acquired simultaneously. Three satellite image spectra (AHSI, Hyperion, AHSI) were simulated using the field spectra which were measured under real environmental conditions rather than laboratory conditions. The combination of genetic algorithm and partial least squares regression (GA-PLSR) was used as prediction algorithm. The models calibrated by HyMap image full spectral bands had the highest accuracies (R = 0.8558, 0.8002, and 0.8592 for Ni, Zn, and Pb, respectively) because of high consistency. For field spectra and three simulated satellite spectra, models calibrated by simulated AHSI spectra performed best because of appropriate resolution (5 nm in the visible near-infrared [VNIR] and 10 nm in the short-wave infrared [SWIR]). The spectral feature extraction method only improved prediction accuracy of the field spectra, indicating that this method benefited from higher spectral resolution. The mapping of the spatial distribution of soil heavy metals over a large area was realized based on HyMap image. According to the results of the satellite simulation spectra, this study proposes to use GF-5 hyperspectral image to estimate heavy metals content. The outcomes provide a reference for the utilization of aerial and satellite hyperspectral images in prediction of soil heavy metal concentrations.
土壤重金属分布图谱可以为污染控制和农业管理提供决策信息。然而，由于重金属在土壤中的含量稀少，其含量的估算对土壤光谱的分辨率很敏感。本研究的目的是测试 Ni、Zn 和 Pb 预测结果对光谱分辨率变化的敏感性，然后绘制其在大面积上的空间分布。此外，还研究了光谱特征提取的有效性。总共从中国甘肃省通渭-庄浪地区获得了 92 个土壤样本及其相应的野外土壤光谱，同时还获取了该地区的机载 HyMap 高光谱图像。使用在真实环境条件下而不是实验室条件下测量的野外光谱模拟了三个卫星图像光谱（AHSI、Hyperion、AHSI）。使用遗传算法和偏最小二乘回归（GA-PLSR）的组合作为预测算法。由 HyMap 图像全谱带校准的模型具有最高的精度（Ni、Zn 和 Pb 的 R 值分别为 0.8558、0.8002 和 0.8592），因为它们具有很高的一致性。对于野外光谱和三个模拟卫星光谱，由模拟 AHSI 光谱校准的模型表现最佳，因为它们的分辨率适当（可见光近红外区为 5nm，短波红外区为 10nm）。光谱特征提取方法仅提高了野外光谱的预测精度，表明该方法受益于更高的光谱分辨率。基于 HyMap 图像实现了大面积土壤重金属空间分布的制图。根据卫星模拟光谱的结果，本研究提出使用 GF-5 高光谱图像来估算重金属含量。研究结果为航空和卫星高光谱图像在土壤重金属浓度预测中的应用提供了参考。
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