Monitoring the Soil Copper of Urban Land with Visible and Near-Infrared Spectroscopy: Comparing Spectral, Compositional, and Spatial Similarities

Yi Liu, Tiezhu Shi (), Yiyun Chen, Zeying Lan, Kai Guo, Dachang Zhuang, Chao Yang and Wenyi Zhang
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Yi Liu: School of Public Administration, Guangdong University of Finance & Economics, Guangzhou 510320, China
Tiezhu Shi: State Key Laboratory of Subtropical Building and Urban Science & Guangdong–Hong Kong-Macau Joint Laboratory for Smart Cities & MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen University, Shenzhen 518060, China
Yiyun Chen: School of Resource and Environmental Science & Key Laboratory of Geographic Information System of the Ministry of Education, Wuhan University, Wuhan 430079, China
Zeying Lan: School of Management, Guangdong University of Technology, Guangzhou 510520, China
Kai Guo: School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Dachang Zhuang: School of Public Administration, Guangdong University of Finance & Economics, Guangzhou 510320, China
Chao Yang: State Key Laboratory of Subtropical Building and Urban Science & Guangdong–Hong Kong-Macau Joint Laboratory for Smart Cities & MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen University, Shenzhen 518060, China
Wenyi Zhang: School of Public Administration, Guangdong University of Finance & Economics, Guangzhou 510320, China

Land, 2024, vol. 13, issue 8, 1-19

Abstract: Heavy metal contamination in urban land has become a serious environmental problem in large cities. Visible and near-infrared spectroscopy (vis-NIR) has emerged as a promising method for monitoring copper (Cu), which is one of the heavy metals. When using vis-NIR spectroscopy, it is crucial to consider sample similarity. However, there is limited research on studying sample similarities and determining their relative importance. In this study, we compared three types of similarities: spectral, compositional, and spatial similarities. We collected 250 topsoil samples (0–20 cm) from Shenzhen City in southwest China and analyzed their vis-NIR spectroscopy data (350–2500 nm). For each type of similarity, we divided the samples into five groups and constructed Cu measurement models. The results showed that compositional similarity exhibited the best performance ( R p 2 = 0.92, RPD = 3.57) and significantly outperformed the other two types of similarity. Spatial similarity ( R p 2 = 0.73, RPD = 1.88) performed slightly better than spectral similarity ( R p 2 = 0.71, RPD = 1.85). Therefore, we concluded that the ranking of the Cu measurement model’s performance was as follows: compositional similarity > spatial similarity > spectral similarity. Furthermore, it is challenging to maintain high levels of similarity across all three aspects simultaneously.

Keywords: proximal sensing; soil pollution; soil property prediction; spatial analysis (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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