Mapping surface soil organic carbon density of cultivated land using machine learning in Zhengzhou DOI Creative Commons
Hengliang Guo,

Jinyang Wang,

Dujuan Zhang

et al.

Environmental Geochemistry and Health, Journal Year: 2024, Volume and Issue: 47(1)

Published: Nov. 28, 2024

Research on soil organic carbon (SOC) is crucial for improving sinks and achieving the "double-carbon" goal. This study introduces ten auxiliary variables based data from a 2021 land quality survey in Zhengzhou multi-objective regional geochemical survey. It uses geostatistical ordinary kriging (OK) interpolation, as well classical machine learning (ML) models, including random forest (RF) support vector (SVM), to map density (SOCD) topsoil layer (0 − 20 cm) of cultivated land. partitions sampling assess generalization capability with Zhongmu County designated an independent test set (dataset2) remaining training (dataset1). The three models are trained using dataset1, directly applied dataset2 evaluate compare their performance. distribution SOCD SOCS soils various types textures analyzed optimal interpolation method. results indicated that: (1) average SOC densities predicted by OK RF, SVM 3.70, 3.74, 3.63 kg/m2, precisions (R2) 0.34, 0.60, 0.81, respectively. (2) ML achieves significantly higher predictive precision than traditional interpolation. RF model's 0.21 model more precise estimating stock. (3) When dataset2, exhibited superior capabilities (R2 = 0.52, MSE 0.32) over 0.32, 0.45). (4) spatial surface area exhibits decreasing gradient west east south north. total stock estimated at approximately 10.76 × 106t. (5) integration attribute variables, climatic remote sensing data, techniques holds significant promise high-precision high-quality mapping agricultural soils.

Language: Английский

Time-lag effects of NEP and NPP to meteorological factors in the source regions of the Yangtze and Yellow Rivers DOI Creative Commons
Hengshuo Zhang, Xizhi Lv, Yongxin Ni

et al.

Frontiers in Plant Science, Journal Year: 2025, Volume and Issue: 15

Published: Jan. 10, 2025

Vegetation productivity and ecosystem carbon sink capacity are significantly influenced by seasonal weather patterns. The time lags between changes in these patterns (including vegetation) responses is a critical aspect vegetation-climate ecosystem-climate interactions. These can vary considerably due to the spatial heterogeneity of vegetation ecosystems. In this study focused on source regions Yangtze Yellow Rivers (SCRYR), we utilized long-term datasets Net Primary Productivity (NPP) model-estimated Ecosystem (NEP) from2015 2020, combined with reconstructed 8-day scale climate sequences, conduct partial correlation regression analysis (isolating influence individual meteorological factors lag effects). found that length effects varies depending regional topography, types, sensitivity their ecological environments factors. region River (SCR), times for NPP NEP response temperature (Tem) longer, compared (SYR), where generally less than 10 days. long precipitation (Pre), ranging from 50 60 days, were primarily concentrated northwestern part SCR, while precipitation, 34 48 covered broad western area. exhibits least solar radiation (SR), exceeding 54 days 99.30% region. contrast, showed varying respect SR: short (ranging 0 15 days) observed areas, 55 64 evident areas. highest SVL, followed C3A, PW, BDS, C3 descending order. This examined spatiotemporal impacts climatic drivers both perspectives. findings crucial enhancing sequestration at important water sources China.

Language: Английский

Citations

0
How is carbon storage in plateau–plain transition zone influenced? Evidence from Minjiang River Basin, China DOI
Menglin Qin,

Xinyu Wu,

Yijia Zhou

et al.

Journal of Cleaner Production, Journal Year: 2025, Volume and Issue: unknown, P. 144766 - 144766

Published: Jan. 1, 2025

Language: Английский

Citations

0
Quantifying ecosystem disturbances in nature reserves using satellite observational data and revealing their constraints on carbon sequestration potential DOI

Aike Kan,

Qing Xiang, Guoqing Li

et al.

Human and Ecological Risk Assessment An International Journal, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 17

Published: April 28, 2025

Language: Английский

Citations

0
Soil carbon metabolizing microorganisms affect the storage and stability of carbon pool in degraded alpine meadows DOI Creative Commons
Qian Liu,

Wenquan Yang,

Jiancun Kou

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 173, P. 113413 - 113413

Published: April 1, 2025

Language: Английский

Citations

0
Exploring soil nitrogen and sulfur dynamics: implications for greenhouse gas emissions on the Qinghai–Tibet Plateau DOI
Siyao Feng, Jie Luo,

Mingpo Li

et al.

Environmental Geochemistry and Health, Journal Year: 2024, Volume and Issue: 46(10)

Published: Aug. 30, 2024

Language: Английский

Citations

2
Mapping surface soil organic carbon density of cultivated land using machine learning in Zhengzhou DOI Creative Commons
Hengliang Guo,

Jinyang Wang,

Dujuan Zhang

et al.

Environmental Geochemistry and Health, Journal Year: 2024, Volume and Issue: 47(1)

Published: Nov. 28, 2024

Research on soil organic carbon (SOC) is crucial for improving sinks and achieving the "double-carbon" goal. This study introduces ten auxiliary variables based data from a 2021 land quality survey in Zhengzhou multi-objective regional geochemical survey. It uses geostatistical ordinary kriging (OK) interpolation, as well classical machine learning (ML) models, including random forest (RF) support vector (SVM), to map density (SOCD) topsoil layer (0 − 20 cm) of cultivated land. partitions sampling assess generalization capability with Zhongmu County designated an independent test set (dataset2) remaining training (dataset1). The three models are trained using dataset1, directly applied dataset2 evaluate compare their performance. distribution SOCD SOCS soils various types textures analyzed optimal interpolation method. results indicated that: (1) average SOC densities predicted by OK RF, SVM 3.70, 3.74, 3.63 kg/m2, precisions (R2) 0.34, 0.60, 0.81, respectively. (2) ML achieves significantly higher predictive precision than traditional interpolation. RF model's 0.21 model more precise estimating stock. (3) When dataset2, exhibited superior capabilities (R2 = 0.52, MSE 0.32) over 0.32, 0.45). (4) spatial surface area exhibits decreasing gradient west east south north. total stock estimated at approximately 10.76 × 106t. (5) integration attribute variables, climatic remote sensing data, techniques holds significant promise high-precision high-quality mapping agricultural soils.

Language: Английский

Citations

0