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Liangyi Li

Biographic Data

ID8843211
NAMELiangyi Li
GIVEN NAMESLiangyi
FAMILY NAMELi
SIGNATURELI L
AFFILIATIONSCollege of Geographical and Remote Sciences Xinjiang University Urumqi China
ORCID0009-0000-7764-3694
VERIFIEDYes
TOTAL WORKS3
TOTAL CITATIONS0
WORKS AS AUTHOR3
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2025
LATEST PUBLICATION YEAR2026
H-INDEX0
  • Application of the Process‐Oriented and Machine Learning Combined Model in Mapping of Soil Organic Carbon of Non‐Waterlogged Mineral Soils at National Scale

    Open Access•Keqiang Wang, Zipeng Zhang et al.•ARTICLE•Land Degradation and Development•2026

    Integrating process‐oriented (PO) and machine learning (ML) models is effective for obtaining dynamic spatial information on soil organic carbon (SOC) stocks. However, PO‐ML integration, particularly at large scales, has received insufficient attention. This gap limits our understanding of and predictive capabilities regarding SOC dynamics. To explore the adaptability and effectiveness of PO‐ML integration on a large scale, we constructed a natio…

  • Fractional Order Differentiation Preprocessing Based Fusion of Vis– NIR and pXRF

    Open Access•Liangyi Li, Zipeng Zhang et al.•ARTICLE•Land Degradation and Development•2026

    Soil contamination by heavy metals has become a significant issue threatening the ecological security of global agriculture, particularly in arid regions, where accurate monitoring of low‐concentration heavy metals remains a technical challenge. This study proposes a proximal sensing method based on the fusion of visible–near infrared (Vis–NIR) spectroscopy and portable X‐ray fluorescence (pXRF) sensors, aiming to address the limitations of tradi…

  • Soil Organic Carbon Sequestration Potential, Storage, and Influencing Mechanisms in China

    Open Access•Jinhua Cao, Zipeng Zhang et al.•ARTICLE•Land Degradation and Development•2025

    The soil organic carbon sequestration potential (SOC sp ) has important implications for the global carbon cycle and responses to climate change. However, there is a dearth of spatial information specifically for China within this field, and our knowledge regarding the factors influencing SOC sp remains somewhat limited. To solve this problem, this study utilized legacy soil data collected in the 1980s (1979–1984s), combined with climatic landsca…

No prominent works on this page.

  • Soil Organic Carbon Sequestration Potential, Storage, and Influencing Mechanisms in China

    Open Access•Jinhua Cao, Zipeng Zhang et al.•ARTICLE•Land Degradation and Development•2025

    The soil organic carbon sequestration potential (SOC sp ) has important implications for the global carbon cycle and responses to climate change. However, there is a dearth of spatial information specifically for China within this field, and our knowledge regarding the factors influencing SOC sp remains somewhat limited. To solve this problem, this study utilized legacy soil data collected in the 1980s (1979–1984s), combined with climatic landsca…

  • Application of the Process‐Oriented and Machine Learning Combined Model in Mapping of Soil Organic Carbon of Non‐Waterlogged Mineral Soils at National Scale

    Open Access•Keqiang Wang, Zipeng Zhang et al.•ARTICLE•Land Degradation and Development•2026

    Integrating process‐oriented (PO) and machine learning (ML) models is effective for obtaining dynamic spatial information on soil organic carbon (SOC) stocks. However, PO‐ML integration, particularly at large scales, has received insufficient attention. This gap limits our understanding of and predictive capabilities regarding SOC dynamics. To explore the adaptability and effectiveness of PO‐ML integration on a large scale, we constructed a natio…

  • Fractional Order Differentiation Preprocessing Based Fusion of Vis– NIR and pXRF

    Open Access•Liangyi Li, Zipeng Zhang et al.•ARTICLE•Land Degradation and Development•2026

    Soil contamination by heavy metals has become a significant issue threatening the ecological security of global agriculture, particularly in arid regions, where accurate monitoring of low‐concentration heavy metals remains a technical challenge. This study proposes a proximal sensing method based on the fusion of visible–near infrared (Vis–NIR) spectroscopy and portable X‐ray fluorescence (pXRF) sensors, aiming to address the limitations of tradi…

Soil water (3 works) · Carbon fibers (2 works) · Chemistry (2 works) · Earth science (2 works) · Environmental Chemistry (2 works) · Environmental Science (2 works) · Geochemistry and Geologic Mapping (2 works) · Geography (2 works) · Geology (2 works) · Materials Science (2 works)

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