Monitoring natural and anthropogenic plant stressors by hyperspectral remote sensing: Recommendations and guidelines based on a meta-review

G Lassalle - Science of the Total Environment, 2021 - Elsevier
This review outlines the advances achieved in monitoring natural and anthropogenic plant
stressors by hyperspectral remote sensing over the last 50 years. A broad diversity of …

VIRS based detection in combination with machine learning for map** soil pollution

X Jia, D O'Connor, Z Shi, D Hou - Environmental Pollution, 2021 - Elsevier
Widespread soil contamination threatens living standards and weakens global efforts
towards the Sustainable Development Goals (SDGs). Detailed soil map** is needed to …

Potential driving forces and probabilistic health risks of heavy metal accumulation in the soils from an e-waste area, southeast China

H Chen, L Wang, B Hu, J Xu, X Liu - Chemosphere, 2022 - Elsevier
The integrated analysis of the distribution characteristics, health risks, and source
identification of heavy metals is crucial for formulating prevention and control strategies for …

Map** soil organic carbon stock by hyperspectral and time-series multispectral remote sensing images in low-relief agricultural areas

L Guo, X Sun, P Fu, T Shi, L Dang, Y Chen… - Geoderma, 2021 - Elsevier
High-precision digital soil organic carbon (SOC) stocks map** is very important for
agricultural production management and global carbon cycle. The spatial heterogeneity of …

Plant nanobionic sensors for arsenic detection

TTS Lew, M Park, J Cui, MS Strano - Advanced Materials, 2021 - Wiley Online Library
Arsenic is a highly toxic heavy‐metal pollutant which poses a significant health risk to
humans and other ecosystems. In this work, the natural ability of wild‐type plants to pre …

Regional and global hotspots of arsenic contamination of topsoil identified by deep learning

M Wu, C Qi, S Derrible, Y Choi, A Fourie… - Communications Earth & …, 2024 - nature.com
Topsoil arsenic (As) contamination threatens the ecological environment and human health.
However, traditional methods for As identification rely on on-site sampling and chemical …

SOC content of global Mollisols at a 30 m spatial resolution from 1984 to 2021 generated by the novel ML-CNN prediction model

X Meng, Y Bao, C Luo, X Zhang, H Liu - Remote Sensing of Environment, 2024 - Elsevier
Carbon cycle is influenced by agricultural soils, and accurately map** the soil organic
carbon (SOC) content of global Mollisols at a 30 m spatial resolution can contribute to …

Estimating heavy metal concentrations in suburban soils with reflectance spectroscopy

H Cheng, R Shen, Y Chen, Q Wan, T Shi, J Wang… - Geoderma, 2019 - Elsevier
Soil contamination by heavy metals has become a serious environmental issue worldwide.
Rapidly and reliably obtaining heavy metal concentrations in soil is vital for soil monitoring …

Map** soil arsenic pollution at a brownfield site using satellite hyperspectral imagery and machine learning

X Jia, D Hou - Science of The Total Environment, 2023 - Elsevier
Heavy metal contamination is ubiquitous in brownfields. Traditional site investigation
employs geostatistical interpolation methods (GIMs) to predict the distribution of soil …

The effect of silicon on iron plaque formation and arsenic accumulation in rice genotypes with different radial oxygen loss (ROL)

C Wu, Q Zou, SG Xue, WS Pan, L Huang… - Environmental …, 2016 - Elsevier
Rice is one of the major pathways of arsenic (As) exposure in human food chain, threatening
over half of the global population. Greenhouse pot experiments were conducted to examine …