Mapping vertical distribution of SOC and TN in reclaimed mine soils using point and imaging spectroscopy

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41210%2F24%3A101031" target="_blank" >RIV/60460709:41210/24:101031 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.ecolind.2023.111437" target="_blank" >https://doi.org/10.1016/j.ecolind.2023.111437</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ecolind.2023.111437" target="_blank" >10.1016/j.ecolind.2023.111437</a>

Result language

angličtina

Original language name

Mapping vertical distribution of SOC and TN in reclaimed mine soils using point and imaging spectroscopy

Original language description

Soil organic carbon (SOC) and total nitrogen (TN) contents in different soil horizons are essential for vegetation growth and crucial indicators to evaluate soil quality in reclaimed mining areas. Compared with conventional wet chemistry methods, soil spectroscopy, including imaging spectroscopy, can be used as a cost and timeefficient soil analysis technique. However, there is a great challenge in combining laboratory point spectra and laboratory hyperspectral imagery for mapping vertical distribution of SOC and TN (0-100 cm) in reclaimed soils. This is primarily because mixing of spectral data from different sources and technologies to improve soil models is still in its infancy. The main objective of this study is to provide a generic workflow to efficiently evaluate and map reclaimed mine soils in different horizons using imaging spectroscopy and machine learning approaches. A total of 65 soil samples (0-100 cm) were collected from three reclaimed mining lands and one natural site in northern China. Both point soil spectral information and hyperspectral images (350-2500 nm) were obtained under laboratory condition. In order to enhance the relationship between soil quality indicators and spectral features, the stacked feature selection algorithms and three-bands spectral indices were proposed for further modelling. Three machine learning methods (partial least squares regression; PLSR, random forest; RF, and radial basis function model; RBF) based on the point spectra were applied to calibrate and map continuous vertical distribution of SOC and TN. According to the results, thirty spectral bands were identified as important spectral features for SOC and eighteen bands for TN. With feature spectral bands and optimized three-bands spectral indices, the RF model yielded the best predictions for both SOC (R2 = 0.97, RMSE = 7.5 g kg-1) and TN (R2 = 0.78, RMSE = 0.33 g kg-1). It was concluded that imaging spectroscopy can be used to quantify and map soil quality indicators for better monitoring ecological restoration process in reclaimed soil of mining site.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

40104 - Soil science

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

ECOLOGICAL INDICATORS

ISSN

1470-160X

e-ISSN

1470-160X

Volume of the periodical

158

Issue of the periodical within the volume

JAN 2024

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

13

Pages from-to

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UT code for WoS article

001143465900001

EID of the result in the Scopus database

2-s2.0-85180974259