Ensemble machine learning approaches for estimating soil texture components in loess soils of Golestan Province DOI

Soraya Bandak,

Abdolhossein Boali,

Soraya Yaghobi

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: May 9, 2025

Language: Английский

National-scale mapping topsoil organic carbon of cropland in China using multitemporal Sentinel-2 images DOI Creative Commons
Jie Xue, Xianglin Zhang, Songchao Chen

et al.

Geoderma, Journal Year: 2025, Volume and Issue: 456, P. 117272 - 117272

Published: March 30, 2025

Language: Английский

Citations

0
Mapping Soil Organic Carbon by Integrating Time-Series Sentinel-2 Data, Environmental Covariates and Multiple Ensemble Models DOI Creative Commons

Zhibo Cui,

Songchao Chen, Bifeng Hu

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(7), P. 2184 - 2184

Published: March 30, 2025

Despite extensive use of Sentinel-2 (S-2) data for mapping soil organic carbon (SOC), how to fully mine the potential time-series S-2 still remains unclear. To fill this gap, study introduced an innovative approach mining data. Using 200 top samples as example, we revealed temporal variation patterns in correlation between SOC and subsequently identified optimal monitoring time window SOC. The integration environmental covariates with multiple ensemble models enabled precise arid region southern Xinjiang, China (6109 km2). Our results indicated following: (a) exhibited both interannual monthly variations, while July August is SOC; (b) adding properties texture information could greatly improve accuracy prediction models. Soil contribute 8.85% 61.78% best model, respectively; (c) among different models, stacking model outperformed weight averaging sample terms performance. Therefore, our proved that spectral from window, integrated has a high accurate mapping.

Language: Английский

Citations

0
Spatial Inversion of Soil Organic Carbon Content Based on Hyperspectral Data and Sentinel‐2 Images DOI

Xiaoyu Huang,

Xuemei Wang, Yanping Guo

et al.

Land Degradation and Development, Journal Year: 2025, Volume and Issue: unknown

Published: April 3, 2025

ABSTRACT Given that Sentinel‐2 (S2) multispectral images provide extensive spatial information and ground‐based hyperspectral data capture refined spectral characteristics, their integration can enhance both the comprehensiveness precision of surface acquisition. This study seeks to leverage these sources develop an optimized estimation model for accurately monitoring large‐scale soil organic carbon (SOC) content, thereby addressing current limitations in multi‐source fusion research. In this study, using mathematical transformation discrete wavelet transform process ground delta oasis Weigan Kuqa rivers Xinjiang, China, combination with S2 image, machine learning algorithms were employed construct models SOC content total variables characteristic variables, inversion oases was carried out. We found R ‐DWT‐H9 significantly correlation between ( p < 0.001). The accuracy constructed based on feature selected by SPA IRIV generally higher than variable models. IRIV‐RFR had highest stable capability. values 2 training validation sets 0.66 0.64, respectively. RMSE 1.5 g∙kg −1 , RPD > 1.4. interior oasis, mainly deficient (61.35%) or relatively (8.17%), while periphery it extremely (30.48%). Combine providing a reference evaluating fertility arid regions.

Language: Английский

Citations

0
Enhancing proximal and remote sensing of soil organic carbon: A local modelling approach guided by spectral and spatial similarities DOI Creative Commons
Qi Sun, Pu Shi

Geoderma, Journal Year: 2025, Volume and Issue: 457, P. 117298 - 117298

Published: April 22, 2025

Language: Английский

Citations

0
Ensemble machine learning approaches for estimating soil texture components in loess soils of Golestan Province DOI

Soraya Bandak,

Abdolhossein Boali,

Soraya Yaghobi

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: May 9, 2025

Language: Английский

Citations

0