Integrating fractional-order derivatives of soil and leaf hyperspectral reflectance for improved estimation of mangrove soil organic carbon
Mangrove ecosystems are vital for carbon sequestration and coastal protection, yet accurate estimation of soil organic carbon (SOC) using remote sensing remains challenging due to spectral interference caused by dynamic vegetation cover. This study presents a novel framework integrating fractional-o...
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Elsevier
2025-06-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0016706125001624 |
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| author | Yibo Luo Chunlin Li Jinhong Huang Chengcheng Dong Junjie Wang |
| author_facet | Yibo Luo Chunlin Li Jinhong Huang Chengcheng Dong Junjie Wang |
| author_sort | Yibo Luo |
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| description | Mangrove ecosystems are vital for carbon sequestration and coastal protection, yet accurate estimation of soil organic carbon (SOC) using remote sensing remains challenging due to spectral interference caused by dynamic vegetation cover. This study presents a novel framework integrating fractional-order derivative (FOD) techniques with machine learning algorithms for SOC estimation in mangrove wetlands. A total of 201 soil samples were collected from five mangrove wetlands in southern China. FOD was applied to both soil and leaf hyperspectral reflectance to amplify subtle spectral variations typically overlooked by conventional approaches. SOC-sensitive wavelengths were identified using the SHAP-XGBoost (Shapley Additive Explanations-Extreme Gradient Boosting) method. A total of 363 modeling strategies were constructed using Random Forest, XGBoost, and CatBoost (Categorical Boosting) algorithms across 11 vegetation cover levels (0–100 %) and 11 fractional orders (0–2 at 0.2 intervals). Results indicate that fractional orders between 0.8 and 1.4 consistently yielded superior performance. The CatBoost model under 10 % vegetation cover and a fractional order of 1.2 achieved the highest accuracy (R2 = 0.730, RMSE = 0.858 %). Incorporating key soil and terrain variables (e.g. soil iron, clay content, pH, salinity, redox potential, and elevation) into the spectra-based SOC estimation model significantly enhanced prediction accuracy, highlighting the complementary roles of spectral signals, soil characteristics, and topographic features in SOC modeling. This framework holds the potential for advancing blue carbon accounting and supporting sustainable mangrove conservation and management under changing environmental conditions. |
| format | Article |
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| institution | OA Journals |
| issn | 1872-6259 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
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| spelling | doaj-art-9ae9b2ca9eac46e6bc1f2d9b068c2a732025-08-20T02:26:27ZengElsevierGeoderma1872-62592025-06-0145811732410.1016/j.geoderma.2025.117324Integrating fractional-order derivatives of soil and leaf hyperspectral reflectance for improved estimation of mangrove soil organic carbonYibo Luo0Chunlin Li1Jinhong Huang2Chengcheng Dong3Junjie Wang4College of Life Sciences and Oceanography, Shenzhen University, 518060 Shenzhen, ChinaCollege of Life Sciences and Oceanography, Shenzhen University, 518060 Shenzhen, ChinaCollege of Life Sciences and Oceanography, Shenzhen University, 518060 Shenzhen, ChinaSchool of Urban Planning and Design, Shenzhen Graduate School, Peking University, 518071 Shenzhen, ChinaCollege of Life Sciences and Oceanography, Shenzhen University, 518060 Shenzhen, China; MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics, Shenzhen University, 518060 Shenzhen, China; Corresponding author at: College of Life Sciences and Oceanography, Shenzhen University, 518060 Shenzhen, China.Mangrove ecosystems are vital for carbon sequestration and coastal protection, yet accurate estimation of soil organic carbon (SOC) using remote sensing remains challenging due to spectral interference caused by dynamic vegetation cover. This study presents a novel framework integrating fractional-order derivative (FOD) techniques with machine learning algorithms for SOC estimation in mangrove wetlands. A total of 201 soil samples were collected from five mangrove wetlands in southern China. FOD was applied to both soil and leaf hyperspectral reflectance to amplify subtle spectral variations typically overlooked by conventional approaches. SOC-sensitive wavelengths were identified using the SHAP-XGBoost (Shapley Additive Explanations-Extreme Gradient Boosting) method. A total of 363 modeling strategies were constructed using Random Forest, XGBoost, and CatBoost (Categorical Boosting) algorithms across 11 vegetation cover levels (0–100 %) and 11 fractional orders (0–2 at 0.2 intervals). Results indicate that fractional orders between 0.8 and 1.4 consistently yielded superior performance. The CatBoost model under 10 % vegetation cover and a fractional order of 1.2 achieved the highest accuracy (R2 = 0.730, RMSE = 0.858 %). Incorporating key soil and terrain variables (e.g. soil iron, clay content, pH, salinity, redox potential, and elevation) into the spectra-based SOC estimation model significantly enhanced prediction accuracy, highlighting the complementary roles of spectral signals, soil characteristics, and topographic features in SOC modeling. This framework holds the potential for advancing blue carbon accounting and supporting sustainable mangrove conservation and management under changing environmental conditions.http://www.sciencedirect.com/science/article/pii/S0016706125001624Hyperspectral reflectanceFractional-order derivativeMangrove ecosystemSoil organic carbonMachine learning |
| spellingShingle | Yibo Luo Chunlin Li Jinhong Huang Chengcheng Dong Junjie Wang Integrating fractional-order derivatives of soil and leaf hyperspectral reflectance for improved estimation of mangrove soil organic carbon Geoderma Hyperspectral reflectance Fractional-order derivative Mangrove ecosystem Soil organic carbon Machine learning |
| title | Integrating fractional-order derivatives of soil and leaf hyperspectral reflectance for improved estimation of mangrove soil organic carbon |
| title_full | Integrating fractional-order derivatives of soil and leaf hyperspectral reflectance for improved estimation of mangrove soil organic carbon |
| title_fullStr | Integrating fractional-order derivatives of soil and leaf hyperspectral reflectance for improved estimation of mangrove soil organic carbon |
| title_full_unstemmed | Integrating fractional-order derivatives of soil and leaf hyperspectral reflectance for improved estimation of mangrove soil organic carbon |
| title_short | Integrating fractional-order derivatives of soil and leaf hyperspectral reflectance for improved estimation of mangrove soil organic carbon |
| title_sort | integrating fractional order derivatives of soil and leaf hyperspectral reflectance for improved estimation of mangrove soil organic carbon |
| topic | Hyperspectral reflectance Fractional-order derivative Mangrove ecosystem Soil organic carbon Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S0016706125001624 |
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