Remote Sensing Inversion of Aboveground Biomass of Grassland in Lanzhou City Based on Machine Learning Algorithm DOI Open Access
Wenjing Dong, Hua Zhang

Academic Journal of Environment & Earth Science, Journal Year: 2023, Volume and Issue: 5(10)

Published: Jan. 1, 2023

Grassland is the important terrestrial ecosystem, aboveground biomass an indicator of productivity grassland monitoring very to assessment current status growth and conservation development resources Lanzhou city. In this study, reflectance was extracted 9 vegetation indexes calculated from Landsat images that combined with field sampling data in city July-August 2021 construct two machine models Random Forest model (RF) eExtreme Gradient Boosting (XGBoost) chose best invert grass City 2000 2023 analyze its spatial temporal dynamics.The results study show that: (1) The other bands except b5-NIR band nine indices were significantly correlated by Pearson correlation analysis, so remaining 14 factors selected as input variables. (2) Compared XGBoost (R2 0.78, RMSE 37.03), RF 0.89, 23.28) has a higher accuracy it more suitable for inversion (3) time, average value showed increasing trend whole; space, decreased firstly southeast northwest then increased City. area high zone increased, low kept transforming zone. This can provide theoretical reference technical support estimation protection ecosystem

Language: Английский

Estimating and mapping the dynamics of soil salinity under different crop types using Sentinel-2 satellite imagery DOI Creative Commons
Xin Cui, Wenting Han, Huihui Zhang

et al.

Geoderma, Journal Year: 2023, Volume and Issue: 440, P. 116738 - 116738

Published: Dec. 1, 2023

Soil salinization is one of the main factors contributing to land degradation, affecting ecological equilibrium, environmental health, and sustainable development agriculture. Due spatial temporal heterogeneity soil properties conditions in a large-scale region, monitoring accuracy can be challenging. This study investigated whether classification diverse crop types on time series improve prediction regional salinity levels. Specifically, we evaluated changes salt content (SSC) under vegetation cover over Hetao Irrigation District (HID) using multi-phase Sentinel-2 imagery ground-truth data collected from June September 2021 2022. Focused sunflower maize fields, this analyzed impact classifying these two examining four distinct SSC estimation. Five indices were selected as characteristic parameters pool 17 (VIs) 13 (SIs) derived satellite images. Moreover, three machine learning algorithms used establish estimation models. The findings underscored efficacy considering different enhancing response sensitivity spectral improving modeling accuracy. Among indices, VIs made more contributions model than SIs, achieving highest coefficient determination (R2) 0.71. artificial neural networks algorithm outperformed other terms stability, yielding an optimal R2 0.72 Root Mean Square Error (RMSE) 0.15%. proposed mapping approach that considers various series, offering valuable insights for accurately assessing salinization, guiding strategies its prevention remediation.

Language: Английский

Citations

14
Monitoring salinity in bare soil based on Sentinel-1/2 image fusion and machine learning DOI
Yujie He, Zhitao Zhang,

Ru Xiang

et al.

Infrared Physics & Technology, Journal Year: 2023, Volume and Issue: 131, P. 104656 - 104656

Published: March 17, 2023

Language: Английский

Citations

13
Dynamics and drivers of soil salinization in arid and semiarid regions from 2002 to 2021: A case study in the Qaidam Basin DOI

Xiaolin She,

Chuanbao Jing, Weihong Liu

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

0
Minimizing vegetation influence on soil salinity mapping with novel bare soil pixels from multi-temporal images DOI Creative Commons
Danyang Wang, Haichao Yang,

Hao Qian

et al.

Geoderma, Journal Year: 2023, Volume and Issue: 439, P. 116697 - 116697

Published: Oct. 24, 2023

Optical remote sensing satellites provide rapid access to regional topsoil salinization mapping. However, mapping based on spectral reflectance is always affected by background material like vegetation cover, straw mulching and soil types. In light of these challenges, this study investigates the potential image fusion, where images original bare pixels were combined, minimize impact cover salinity A case was presented for typical area using synchronized Sentinel-2 MSI (named image) 255 ground-truth data collected in October 2020, aligning with periods salt return. Furthermore, obtain novel pixels, multi-temporal acquired during two distinct intervals: March May September November, spanning years from 2018 2021. The synthetic (SYSI) obtained extracting images. Two (original, SYSI) fused non-negative matrix factorization (NMF) method, named SYSIfused. Then, stacking machine algorithm used under different types, evaluating SYSIfused accuracy prediction. results showed outperformed (the R2 best models increased 0.054–0.242, RMSE MAE decreased 0.049–0.780 0.012–0.546, respectively). Based SYSIfused, order effect types coastal bog solonchaks > alluvial cinnamon coral saline overall samples, their roles improving model 0.141, 0.085, 0.022, 0.012, respectively. Besides, provided prediction performances (R2 = 0.742, 0.377, 0.362). This introduces concept merging SYSI, resulting a significant improvement areas covered vegetation.

Language: Английский

Citations

8
Rapid and precise calibration of soil microparameters for high-fidelity discrete element models in vehicle mobility simulation DOI
Hua Chen,

Runxin Niu,

Xinkai Kuang

et al.

Journal of Terramechanics, Journal Year: 2024, Volume and Issue: 115, P. 100985 - 100985

Published: May 11, 2024

Language: Английский

Citations

2
Advancements and Perspective in the Quantitative Assessment of Soil Salinity Utilizing Remote Sensing and Machine Learning Algorithms: A Review DOI Creative Commons
Fei Wang, Lili Han, Lulu Liu

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(24), P. 4812 - 4812

Published: Dec. 23, 2024

Soil salinization is a significant global ecological issue that leads to soil degradation and recognized as one of the primary factors hindering sustainable development irrigated farmlands deserts. The integration remote sensing (RS) machine learning algorithms increasingly employed deliver cost-effective, time-efficient, spatially resolved, accurately mapped, uncertainty-quantified salinity information. We reviewed articles published between January 2016 December 2023 on sensing-based prediction synthesized latest research advancements in terms innovation points, data, methodologies, variable importance, trends, current challenges, potential future directions. Our observations indicate innovations this field focus detection depth, iterations data conversion methods, application newly developed sensors. Statistical analysis reveals Landsat most frequently utilized sensor these studies. Furthermore, deep remains underexplored. ranking accuracy across various study areas follows: lake wetland (R2 = 0.81) > oasis 0.76) coastal zone 0.74) farmland 0.71). also examined relationship metadata accuracy: (1) Validation accuracy, sample size, number variables, mean exhibited some correlation with modeling while sampling type, time, maximum did not influence accuracy. (2) Across broad range scales, large sizes may lead error accumulation, which associated geographic diversity area. (3) inclusion additional environmental variables does necessarily enhance (4) Modeling improves when area exceeds 30 dS/m. Topography, vegetation, temperature are relatively covariates. Over past years, affected by has been increasing. To further we provide several suggestions for challenges directions research. While sole solution, it provides unique advantages salinity-related studies at both regional scales.

Language: Английский

Citations

2
A remote sensing-based strategy for mapping potentially toxic elements of soils: Temporal-spatial-spectral covariates combined with random forest DOI
Xibo Xu,

Zeqiang Wang,

Xiaoning Song

et al.

Environmental Research, Journal Year: 2023, Volume and Issue: 240, P. 117570 - 117570

Published: Nov. 6, 2023

Language: Английский

Citations

6
Preliminary assessment of the knowledge gaps to reduce land degradation in Europe DOI Creative Commons
Melpomeni Zoka, Salvador Lladó, Nikolaos Stathopoulos

et al.

Soils for Europe., Journal Year: 2024, Volume and Issue: 1

Published: May 30, 2024

Language: Английский

Citations

0
Dynamics and Drivers of Soil Salinization in Arid and Semiarid Regions from 2002 to 2021: A Case Study in the Qaidam Basin DOI

Xiaolin She,

Chuanbao Jing, Weihong Liu

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

0
Estimating the Canopy Nitrogen Content in Maize by Using the Transform-Based Dynamic Spectral Indices and Random Forest DOI Open Access
Shuting Yang,

J. Li,

Li Ji

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(18), P. 8011 - 8011

Published: Sept. 13, 2024

The monitoring of maize health status is crucial for achieving sustainable agricultural development. Canopy nitrogen content (CNC) essential the synthesis proteins and chlorophyll in leaves and, thus, significantly influences growth yield. In this study, we developed a CNC spectral estimation model based on transform-based dynamic indices (TDSI) random forest (RF) algorithm, enabling rapid canopy leaves. A total 60 leaf samples corresponding field spectra were collected. Subsequently, data transformed using centralization transformation (CT), first derivative (D1), second (D2), detrend (DT), min-max normalization (MMN) methods. Three types band combination methods (band difference, ratio, normalized difference) used to construct TDSIs. Finally, optimal TDSI was selected as independent variable, measured dependent variable build RF algorithm. Results indicated that (1) TDSIs can more accurately characterize maize, with correlation coefficient approximately 102% higher than those raw bands. (2) included TDSI1247,1249CT-RI, TDSI625,641CT-NDI, TDSI540,703D1-RI, TDSI514,540D1-RI, TDSI514,530D1-DI, TDSI540,697D1-NDI, TDSI970,1357D2-DI, TDSI523,1031D2-NDI, TDSI617,620DT-RI, TDSI2109,2127MMN-NDI. (3) TDSIs, algorithm achieved accuracy R2 RPIQ 0.92 4.99, respectively, representing maximum improvement 67.27% over traditional (based value). This study provides an approach accurate contributing development agriculture.

Language: Английский

Citations

0