Jinhua Cao
Biographic Data
| ID | 8202642 |
|---|---|
| NAME | Jinhua Cao |
| GIVEN NAMES | Jinhua |
| FAMILY NAME | Cao |
| SIGNATURE | CAO J |
| AFFILIATIONS | College of Geographical and Remote Sciences Xinjiang University Urumqi China |
| ORCID | 0009-0001-8532-4332 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Application of the Process‐Oriented and Machine Learning Combined Model in Mapping of Soil Organic Carbon of Non‐Waterlogged Mineral Soils at National Scale
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
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…
Unveiling the Dynamic Patterns and Driving Forces of Soil Organic Carbon in Chinese Croplands From 1980 to 2020
Soil organic carbon (SOC) in cropland is a critical component of the global carbon cycle, representing the most dynamic segment of the carbon pool, and is vital to addressing both “dual‐carbon” goals and food security challenges. However, the current research on SOC in China's croplands has limitations in timeliness, continuity, and accuracy. This study constructed a machine learning model to assess the spatial–temporal distribution and changes o…
Soil Organic Carbon Sequestration Potential, Storage, and Influencing Mechanisms in China
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…
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Unveiling the Dynamic Patterns and Driving Forces of Soil Organic Carbon in Chinese Croplands From 1980 to 2020
Soil organic carbon (SOC) in cropland is a critical component of the global carbon cycle, representing the most dynamic segment of the carbon pool, and is vital to addressing both “dual‐carbon” goals and food security challenges. However, the current research on SOC in China's croplands has limitations in timeliness, continuity, and accuracy. This study constructed a machine learning model to assess the spatial–temporal distribution and changes o…
Soil Organic Carbon Sequestration Potential, Storage, and Influencing Mechanisms in China
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
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
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 (4 works) · Carbon fibers (3 works) · Chemistry (3 works) · Earth science (3 works) · Environmental Chemistry (3 works) · Environmental Science (3 works) · Geology (3 works) · Soil carbon (3 works) · Soil Science (3 works) · Total organic carbon (3 works)