Heavy Metal Concentration Estimation for Different Farmland Soils Based on Projection Pursuit and LightGBM with Hyperspectral Images.

Nan Lin, Xiaofan Shao, Huizhi Wu, Ranzhe Jiang, Menghong Wu

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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5 · Who and what money

Authors and funding

5 authors.

Nan LinCollege of Surveying and Exploration Engineering, Jilin Jianzhu University, Changchun 130118, China.

Xiaofan ShaoCollege of Surveying and Exploration Engineering, Jilin Jianzhu University, Changchun 130118, China.

Huizhi WuHenan Academy of Geology, Zhengzhou 450016, China.

Ranzhe JiangCollege of Biological and Agricultural Engineering, Jilin University, Changchun 130012, China.

Menghong WuCollege of Surveying and Exploration Engineering, Jilin Jianzhu University, Changchun 130118, China.

Funding

the National Natural Science Foundation of China 52178042the Natural Science Foundation of Jilin Province 20230101373JCthe Science and Technology Development Project of Jilin Province 20210203016SF

6 · The paper itself

Abstract

Heavy metal pollution in farmland soil threatens soil environmental quality. It is an important task to quickly grasp the status of heavy metal pollution in farmland soil in a region. Hyperspectral remote sensing technology has been widely used in soil heavy metal concentration monitoring. How to improve the accuracy and reliability of its estimation model is a hot topic. This study analyzed 440 soil samples from Sihe Town and the surrounding agricultural areas in Yushu City, Jilin Province. Considering the differences between different types of soils, a local regression model of heavy metal concentrations (As and Cu) was established based on projection pursuit (PP) and light gradient boosting machine (LightGBM) algorithms. Based on the estimations, a spatial distribution map of soil heavy metals in the region was drawn. The findings of this study showed that considering the differences between different soils to construct a local regression estimation model of soil heavy metal concentration improved the estimation accuracy. Specifically, the relative percent difference (

Indexed as

hyperspectral imageLightGBMlocal regression estimationprojection pursuitsoil heavy metals

Identifiers

PMID38794105

DOI10.3390/s24103251

PMCPMC11125194

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.