Soil and Tillage Research, Journal Year: 2024, Volume and Issue: 248, P. 106397 - 106397
Published: Dec. 5, 2024
Language: Английский
Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 231, P. 109970 - 109970
Published: Jan. 18, 2025
Language: Английский
Citations
2
Ecological Indicators, Journal Year: 2025, Volume and Issue: 172, P. 113303 - 113303
Published: March 1, 2025
Language: Английский
Citations
0
CATENA, Journal Year: 2025, Volume and Issue: 254, P. 108958 - 108958
Published: March 19, 2025
Language: Английский
Citations
0
Frontiers in Environmental Science, Journal Year: 2025, Volume and Issue: 13
Published: March 24, 2025
Rapid and accurate soil salinity (SS) analysis is essential for effective management of salinized agricultural lands. However, the potential utilizing periodic remote sensing satellite data to improve accuracy regional SS inversion requires further exploration. This study proposes a novel approach that combines multi-temporal images captured near field sampling period (September 5–10, 2020). Focusing on Wudi County, China, we analyzed three time-series Sentinel-2 obtained determine time window. Images within window were synthesized into four combined-temporal through arithmetic operation strategies one band combination strategy. SS-related spectral variables derived from both single selected using Random Forest (RF), ReliefF, Support Vector Machine Recursive Feature Elimination algorithms (SVM-RFE). Subsequently, models developed compared an Extreme Learning Machine. The optimal model was then applied map distribution. results demonstrate that: (1) consistently outperformed single-temporal models, particularly those employing strategy, showing 0.25–0.53 higher mean Relative Percentage Deviation (RPD); (2) RF variable selection exhibited superior stability efficiency, with RPD 0.02 0.04 than other algorithms; (3) ELM image achieved highest validation precision (Coefficient Determination = 0.72, Root Mean Square Error 0.87 dS/m, 1.93); (4) final revealed spatial gradient increasing in farmland southwestern area toward northeastern coastal region, 46.7% exhibiting yield-affecting levels. These findings provide empirical insights development techniques supporting agricultural-environmental strategies.
Language: Английский
Citations
0
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
Plant and Soil, Journal Year: 2024, Volume and Issue: unknown
Published: Sept. 2, 2024
Language: Английский
Citations
0
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
Citations
0
Soil and Tillage Research, Journal Year: 2024, Volume and Issue: 248, P. 106397 - 106397
Published: Dec. 5, 2024
Language: Английский
Citations
0