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<p>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<sup>2</sup> = 0.95, RMSE = 0.39%, RPD = 4.54). The G-SSL provides a spatially robust, high-resolution resource for digital SOC mapping and establishes a methodological benchmark for developing region-specific spectral libraries in other heterogeneous landscapes.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月21日

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  • Jeehwan Bae,
  • Inhye Seo,
  • Junge Hyun,
  • Yelim Park,
  • Minseop Jeong,
  • Jaewoo Kim,
  • Seoyeon Kim,
  • Sunyoung Joo,
  • Youngseo Shin,
  • Yonghui Jung,
  • Seunghee Seo,
  • Heesoo Kim,
  • Chaehee Ahn,
  • Juneyoung Pyung,
  • Minjoon Cha,
  • Byeonggil Choi,
  • Wheemoon Kim,
  • Hansu Kim,
  • Gayoung Yoo

摘要

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 (R2 = 0.95, RMSE = 0.39%, RPD = 4.54). The G-SSL provides a spatially robust, high-resolution resource for digital SOC mapping and establishes a methodological benchmark for developing region-specific spectral libraries in other heterogeneous landscapes.