Evidence map›Paper›PMID 41565678›Full record

ArticleScientific data2026

A spatially rich, temporally coherent soil spectral dataset for soil organic carbon estimation.

Jeehwan Bae, Inhye Seo, Junge Hyun, Yelim Park, Minseop Jeong, Jaewoo Kim, Seoyeon Kim, Sunyoung Joo, Youngseo Shin, Yonghui Jung and 9 more

Abstract read
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In one paragraph

Article in Scientific data, 2026. 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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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

19 authors.

Jeehwan BaeDepartment of Environmental Science and Engineering, Kyung Hee University, Yongin, South Korea.
Inhye SeoDepartment of Applied Environmental Science, Kyung Hee University, Yongin, South Korea.
Junge HyunDepartment of Environmental Science and Engineering, Kyung Hee University, Yongin, South Korea.
Yelim ParkDepartment of Applied Environmental Science, Kyung Hee University, Yongin, South Korea.
Minseop JeongDepartment of Applied Environmental Science, Kyung Hee University, Yongin, South Korea.
Jaewoo KimDepartment of Applied Environmental Science, Kyung Hee University, Yongin, South Korea.
Seoyeon KimDepartment of Applied Environmental Science, Kyung Hee University, Yongin, South Korea.
Sunyoung JooDepartment of Applied Environmental Science, Kyung Hee University, Yongin, South Korea.
Youngseo ShinDepartment of Applied Environmental Science, Kyung Hee University, Yongin, South Korea.
Yonghui JungDepartment of Applied Environmental Science, Kyung Hee University, Yongin, South Korea.
Seunghee SeoDepartment of Applied Environmental Science, Kyung Hee University, Yongin, South Korea.
Heesoo KimDepartment of Applied Environmental Science, Kyung Hee University, Yongin, South Korea.
Chaehee AhnDepartment of Applied Environmental Science, Kyung Hee University, Yongin, South Korea.
Juneyoung PyungCollective Carbon Capture Inc., Gwangmyeong, South Korea.
Minjoon ChaCollective Carbon Capture Inc., Gwangmyeong, South Korea.
Byeonggil ChoiGyeonggi Forestry Environment Research Center, Osan, South Korea.
Wheemoon KimClimate & Environment Data Center, Gyeonggi Research Institute, Suwon, South Korea.
Hansu KimClimate & Environment Data Center, Gyeonggi Research Institute, Suwon, South Korea.
Gayoung YooDepartment of Environmental Science and Engineering, Kyung Hee University, Yongin, South Korea. gayoo@khu.ac.kr.

Funding

Ministry of Knowledge Economy | Korea Institute of Energy Technology Evaluation and Planning (KETEP) 20224000000260Ministry of Trade, Industry and Energy (Ministry of Trade, Industry and Energy, Korea) 20224000000260
6 · The paper itself

Abstract

Accurate estimation of soil organic carbon (SOC) is crucial for climate mitigation and sustainable land management. Near-infrared (NIR) spectroscopy provides a rapid, cost-effective approach for SOC assessment, but its predictive performance depends on calibration datasets with adequate spatiotemporal coverage. Here, we present the Gyeonggi Soil Spectral Library (G-SSL), comprising NIR spectra (1,400-2,500 nm) from 1,500 topsoil samples (0-15 cm) collected systematically across Gyeonggi Province, South Korea, in 2024. Sampling spans 11 representative land cover types, including deciduous, coniferous, and mixed forests; paddy and upland fields; orchards; greenhouses; urban parks; artificial grasslands; riparian zones; and bare lands. To develop an accurate NIR-based SOC prediction model, SOC measurements from 712 samples were used to calibrate partial least squares regression (PLSR) models, which showed robust performance in a 70:30 train-test split (R

Identifiers

PMID41565678
PMCPMC12901301

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LicenceCC BY-NC-ND
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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.