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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40104 - Soil science

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

ECOLOGICAL INDICATORS

ISSN

1470-160X

e-ISSN

1470-160X

Svazek periodika

158

Číslo periodika v rámci svazku

JAN 2024

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

13

Strana od-do

—

Kód UT WoS článku

001143465900001

EID výsledku v databázi Scopus

2-s2.0-85180974259