Improved Mapping of Soil Heavy Metals Using a Vis-NIR Spectroscopy Index in an Agricultural Area of Eastern China

Abstract

Visible and near-infrared reflectance (Vis-NIR) spectroscopy can provide low-cost and high-density data for mapping various soil properties. However, a weak correlation between the spectra and measurements of soil heavy metals makes spectroscopy difficult to use in predicting incipient risk areas. In this study, we introduce a new spectral index (SI) based on Vis-NIR spectra and use it as a covariate in ordinary cokriging (OCK) to improve the mapping of soil heavy metals. The SI was defined from the highest covariance between spectra and heavy metal content in the partial least squares regression (PLSR) model. The proposed mapping approach was compared with an ordinary kriging (OK) predictor that uses only soil heavy metal data and an OCK predictor that uses soil organic matter (SOM) and Fe as covariates. To this end, a total of 100 topsoil (0-20 cm) samples were collected in an agricultural area near Longkou City, and the contents of As, Pb and Zn in the soil were determined. The results showed that OCK with the SI provided better results in terms of unbiasedness and accuracy compared to other comparative methods. Additionally, we explored the SI through simple strategies based on spectral analysis and correlation statistics and found that the SI synthesized most of the soil properties affected by heavy metals and was not limited to Fe and SOM. In summary, the SI method is cost-effective for improving soil heavy metal mapping and can be applied to other areas. INDEX TERMS Soil heavy metals, digital soil mapping, Vis-NIR spectroscopy, geostatistics.

Source apportionment of potentially toxic elements in soils using APCS/MLR, PMF and geostatistics in a typical industrial and mining city in Eastern China

“…The multitude of emission sources have made this a typical area for verifying source apportionment models [ 32 ]. The area is characterized by mineral resource exploitation, including coal, gold mine and lead zinc mining, and the abundant natural resources have promoted the development of preliminary industrial enterprises, such as iron-making plants, paper mills and electroplating factories [ 33 ]. There is approximately 300 km 2 of agricultural land in this area, mainly wheat and maize planting in the west, apple and grape orchards in the east, and vegetable planting areas in the north ( Fig 2 ).…”

Pemetaan Spasial Kadar Kobal pada Endapan Laterit dengan Metode Ordinary Cokriging dan Inverse Distance Weighting

“…Beberapa metode dapat digunakan dalam interpolasi spasial, antara lain; metode kriging, inverse distance weighting, interpolasi polynomial, splines, dan lain lain (Shahbeik, 2014). Metode ordinary cokriging (OCK) merupakan perluasan dari metode ordinary kriging (OK), dimana variabel sekunder dapat digunakan untuk meningkatkan ketepatan estimasi interpolasi dengan asumsi kedua variabel tersebut berkorelasi (Cao et al, 2020). Secara umum teknik OCK digunakan dalam kasus geostatistik multi variat.…”

“…Secara umum teknik OCK digunakan dalam kasus geostatistik multi variat. Metode OCK telah diaplikasikan pada berbagai bidang antara lain ; Dalam bidang agrikultura diaplikasikan dalam pemetaan sebaran kandungan mineral berat dalam tanah pertanian (Cao et al, 2020); Dalam bidang eksplorasi digunakan dalam estimasi sumberdaya endapan mineral (Farag et al, 2020); Dalam bidang hidrologi diaplikasikan untuk pemetaan distribusi spasial curah hujan (Adhikary et al, 2017), dan di bidang pertambangan untuk optimalisasi estimasi sumberdaya mineral dalam operasi pertambangan (Minnitt et al, 2014). Metode inverse distance weighting (IDW) adalah salah satu metode interpolasi spasial deterministik yang banyak diimplementasikan terutama di bidang geologi, pertambangan dan bidang kebumian lainnya (Lu and Wong, 2008).…”

“…Dalam proses estimasi metode ini menggunakan lebih dari satu variabel yang disebut dengan variabel primer dan variabel sekunder. Metode OCK digunakan untuk meningkatkan estimasi variabel utama dengan menggunakan variabel sekunder dengan asumsi bahwa kedua variabel tersebut berkorelasi (Cao et al, 2020).…”

Pollution-Induced Food Safety Problem in China: Trends and Policies

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