Estimation of Soil Salinity by Combining Spectral and Texture Information from UAV Multispectral Images in the Tarim River Basin, China DOI Creative Commons

Jiaxiang Zhai,

Nan Wang, Bifeng Hu

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(19), P. 3671 - 3671

Published: Oct. 1, 2024

Texture features have been consistently overlooked in digital soil mapping, especially salinization mapping. This study aims to clarify how leverage texture information for monitoring through remote sensing techniques. We propose a novel method estimating salinity content (SSC) that combines spectral and from unmanned aerial vehicle (UAV) images. Reflectance, index, one-dimensional (OD) were extracted UAV Building on the features, we constructed two-dimensional (TD) three-dimensional (THD) indices. The technique of Recursive Feature Elimination (RFE) was used feature selection. Models estimation built using three distinct methodologies: Random Forest (RF), Partial Least Squares Regression (PLSR), Convolutional Neural Network (CNN). Spatial distribution maps then generated each model. effectiveness proposed is confirmed utilization 240 surface samples gathered an arid region northwest China, specifically Xinjiang, characterized by sparse vegetation. Among all indices, TDTeI1 has highest correlation with SSC (|r| = 0.86). After adding multidimensional information, R2 RF model increased 0.76 0.90, improvement 18%. models, outperforms PLSR CNN. model, which (SOTT), achieves RMSE 5.13 g kg−1, RPD 3.12. contributes 44.8% prediction, contributions TD THD indices 19.3% 20.2%, respectively. confirms great potential introducing semi-arid regions.

Language: Английский

Fine-resolution mapping of cropland topsoil pH of Southern China and its environmental application DOI Creative Commons
Bifeng Hu, Modian Xie, Zhou Shi

et al.

Geoderma, Journal Year: 2024, Volume and Issue: 442, P. 116798 - 116798

Published: Feb. 1, 2024

Soil pH is one of the critical indicators soil quality. A fine resolution map urgently required to address practical issues agricultural production, environmental protection, and ecosystem functioning, which often fall short meeting demands for local applications. To fill this gap, we used data from an extensive survey 13,424 surface samples (0–0.2 m) across cropland Jiangxi Province in Southern China. Using digital mapping techniques with 46 covariates, produced a 30 m topsoil We integrate different variable selection algorithms machine learning methods. Our results indicate Random Forest covariates selected by recursive feature had best performance r 0.583 RMSE 0.41. The prediction interval coverage probability our was 0.92, indicating low estimated uncertainty. Climate identified as most predicting contribution 37.42 %, followed properties (29.09 %), management (21.86 parent material (6.22 biota (5.39 %) factors. mean 5.21, great pressure acidification region. high values were mainly distributed Northern, Western, Eastern parts region while majorly located central part. Compared past surveys 1980 s, there no significant change surveyed can provide important implications guidance decisions on heavy metal pollution remediation, precision agriculture, prevention acidification.

Language: Английский

Citations

11
High-Accuracy Mapping of Soil Organic Carbon by Mining Sentinel-1/2 Radar and Optical Time-Series Data with Super Ensemble Model DOI Creative Commons

Zhibo Cui,

Songchao Chen, Bifeng Hu

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(4), P. 678 - 678

Published: Feb. 17, 2025

Accurate digital soil organic carbon mapping is of great significance for regulating the global cycle and addressing climate change. With advent remote sensing big data era, multi-source multi-temporal techniques have been extensively applied in Earth observation. However, how to fully mine time-series high-accuracy SOC remains a key challenge. To address this challenge, study introduced new idea mining data. We used 413 topsoil samples from southern Xinjiang, China, as an example. By (Sentinel-1/2) 2017 2023, we revealed temporal variation pattern correlation between Sentinel-1/2 SOC, thereby identifying optimal time window monitoring using integrating environmental covariates super ensemble model, achieved Southern China. The results showed following aspects: (1) windows were July–September July–August, respectively; (2) modeling accuracy sensor integrated with was superior single-source alone. In model based on data, cumulative contribution rate Sentinel-2 51.71% higher than that Sentinel-1 data; (3) stacking model’s predictive performance outperformed weight average simple models. Therefore, covariates, driven represents strategy mapping.

Language: Английский

Citations

1
Effects of straw return on soil carbon sequestration, soil nutrients and rice yield of in acidic farmland soil of Southern China DOI
Hongyi Li, Modian Xie, Bifeng Hu

et al.

Environment Development and Sustainability, Journal Year: 2024, Volume and Issue: unknown

Published: April 27, 2024

Language: Английский

Citations

4
A detailed mapping of soil organic matter content in arable land based on the multitemporal soil line coefficients and neural network filtering of big remote sensing data DOI Creative Commons
Д. И. Рухович, П. В. Королева, Alexey Rukhovich

et al.

Geoderma, Journal Year: 2024, Volume and Issue: 447, P. 116941 - 116941

Published: June 12, 2024

Language: Английский

Citations

4
Assessing the potential of multi-source remote sensing data for cropland soil organic matter mapping in hilly and mountainous areas DOI
Peng Li, Xiaobo Wu,

C Feng

et al.

CATENA, Journal Year: 2024, Volume and Issue: 245, P. 108312 - 108312

Published: Aug. 12, 2024

Language: Английский

Citations

4
Improving SOC estimation in low-relief farmlands using time-series crop spectral variables and harmonic component variables based on minimum sample size DOI Creative Commons

Chenjie Lin,

Ling Zhang, Nan Zhong

et al.

International Soil and Water Conservation Research, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

0
Spatial and Temporal Variations in Soil Organic Carbon in Northwestern China via Comparisons of Different Methods DOI Creative Commons
Jinlin Li, Ning Hu, Yuxin Qi

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(3), P. 420 - 420

Published: Jan. 26, 2025

Soil organic carbon (SOC) is a crucial component for investigating cycling and global climate change. Accurate data exhibiting the temporal spatial distributions of SOC are very important determining soil sequestration potential formulating strategies. An scheme mapping to establish link between environmental factors via different methods. The Shiyang River Basin third largest inland river basin in Hexi Corridor, which has closed geographical conditions relatively independent cycle system, making it an ideal area research arid areas. In this study, 65 samples were collected 21 assessed from 2011 2021 Basin. linear regression (LR) method two machine learning methods, i.e., support vector (SVR) random forest (RF), applied estimate distribution SOC. RF slightly better than SVR because its advantages comparison classification. When latitude, slope, normalized vegetation index (NDVI) used as predictor variables, best performance shown. Compared with Harmonized World Database (HWSD), optimal improved accuracy significantly. Finally, tended increase, total increase 135.94 g/kg across whole basin. northwestern part middle decreased by 2.82% industrial activities. Minqin County increased approximately 62.77% 2021. Thus, variability increased. This study provides theoretical basis basins. addition, can also provide effective scientific suggestions projects, offer key understanding cycle, change adaptation mitigation

Language: Английский

Citations

0
High-Resolution Mapping of Cropland Soil Organic Carbon in Northern China DOI Creative Commons

Rui Wang,

Wenbo Du, Ping Li

et al.

Agronomy, Journal Year: 2025, Volume and Issue: 15(2), P. 359 - 359

Published: Jan. 30, 2025

Mapping the high-precision spatiotemporal dynamics of soil organic carbon (SOC) in croplands is crucial for enhancing fertility and sequestration ensuring food security. We conducted field surveys collected 1121 samples from cropland Changzhi, northern China, 2010 2020. Random Forest (RF) models combined with 19 environmental covariates were used to map topsoil (0–20 cm) SOC 2020, uncertainty maps calculate dynamic changes between Finally, RF Structural Equation Modeling (SEM) employed explore effects climate, vegetation, topography, properties, agricultural management on variation croplands. Compared prediction model using only natural variables (RF_C), incorporating (RF_A) significantly improved simulation accuracy SOC. The coefficient determination (R2) increased 0.77 0.85, while Root Mean Square Error (RMSE) decreased 1.74 1.53 g kg−1, Absolute (MAE) was reduced 1.10 0.94 kg−1. our predictions low, an average value 0.39–0.66 From Changzhi exhibited overall increasing trend, increase 1.57 Climate change, management, properties strongly influence variation. annual precipitation (MAP), drainage condition (DC), net primary productivity (NPP) drivers variability. Our findings highlight effectiveness predicting Overall, study confirms that has great potential stocks, which may contribute sustainable development.

Language: Английский

Citations

0
Improved estimation of soil organic carbon stock in subtropical cropland of Southern China based on digital soil mapping and multi-sources data DOI Creative Commons
Bifeng Hu, Qian Zhu, Modian Xie

et al.

Geo-spatial Information Science, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 24

Published: March 12, 2025

Language: Английский

Citations

0
Comparison of Machine Learning and Geostatistical Methods on Mapping Soil Organic Carbon Density in Regional Croplands and Visualizing Its Location‐Specific Dominators via Interpretable Model DOI Open Access
Bifeng Hu,

Yibo Geng,

Yi Lin

et al.

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

Published: March 17, 2025

ABSTRACT High‐precision soil organic carbon density (SOCD) map is significant for understanding ecosystem cycles and estimating storage. However, the current mapping methods are difficult to balance accuracy interpretability, which brings great challenges of SOCD. In present research, a total 6223 samples were collected, along with data pertaining 30 environmental covariates, from agricultural land located in Poyang Lake Plain Jiangxi Province, southern China. Furthermore, ordinary kriging (OK), geographically weighted regression (GWR), random forest (RF), empirical Bayesian (EBK), three hybrid models (RF‐OK, RF‐EBK, RF‐GWR), constructed. These used SOCD (soil density) study region high resolution m. After that, shapley additive explanations (SHAP) quantify global contribution spatially identify dominant factors that influence variation. The outcomes suggested compared single geostatistics model model, RF method emerged as most effective predictive showcasing superior performance (coefficient determination ( R 2 ) = 0.44, root mean squared error (RMSE) 0.61 kg m −2 , Lin's concordance coefficient (LCCC) 0.58). Using SHAP, we found properties contributed prediction (81.67%). At pixel level, nitrogen dominated 50.33% farmland, followed by parent material (8.11%), available silicon (8.00%), annual precipitation (5.71%), remaining variables accounted less than 5.50%. summary, our offered valuable enlightenment toward achieving between interpretability digital mapping, deepened spatial variation farmland

Language: Английский

Citations

0