Monitoring Soil Salinity in Arid Areas of Northern Xinjiang Using Multi-Source Satellite Data: A Trusted Deep Learning Framework

Land, Год журнала: 2025, Номер 14(1), С. 110 - 110

Опубликована: Янв. 8, 2025

Soil salinization affects agricultural productivity and ecosystem health in Xinjiang, especially arid areas. The region’s complex topography limited data emphasize the pressing need for effective, large-scale monitoring technologies. Therefore, 1044 soil samples were collected from farmland northern potential effectiveness of salinity was explored by combining environmental variables with Landsat 8 Sentinel-2. study applied four types feature selection algorithms: Random Forest (RF), Competitive Adaptive Reweighted Sampling (CARS), Uninformative Variable Elimination (UVE), Successive Projections Algorithm (SPA). These are then integrated into various machine learning models—such as Ensemble Tree (ETree), Extreme Gradient Boosting (XGBoost), LightBoost—as well deep models, including Convolutional Neural Networks (CNN), Residual (ResNet), Multilayer Perceptrons (MLP), Kolmogorov–Arnold (KAN), modeling. results suggest that fertilizer use plays a critical role processes. Notably, interpretable model KAN achieved an accuracy 0.75 correctly classifying degree salinity. This highlights integrating multi-source remote sensing technologies, offering pathway to monitoring, thereby providing valuable support management.

Язык: Английский

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Advancements and Perspective in the Quantitative Assessment of Soil Salinity Utilizing Remote Sensing and Machine Learning Algorithms: A Review

Remote Sensing, Год журнала: 2024, Номер 16(24), С. 4812 - 4812

Опубликована: Дек. 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.

Язык: Английский

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Hyperspectral estimation of chlorophyll content in grapevine based on feature selection and GA-BP

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 7, 2025

Abstract Leaf chlorophyll content (LCC) is a key indicator for assessing the growth of grapes. Hyperspectral techniques have been applied to LCC research. However, quantitative prediction grape using this technique remains challenging due baseline drift, spectral peak overlap, and ambiguity in sensitive range. To address these issues, two typical crop leaf hyperspectral data were collected reveal response characteristics standardization by variables (SNV) multiple far scattering correction (MSC) preprocessing variations. The range determined Pearson’s algorithm, features are further extracted within that Extreme Gradient Boosting (XGBoost), Recursive Feature Elimination (RFE), Principal components analysis (PCA). Comparison ability Random Forest Regression (RFR) Support Vector Machine (SVR) model, Genetic Algorithm-Based Neural Network (GA-BP) on based features. A SNV-RFE-GA-BP framework predicting grapes proposed, where $$\:{R}^{2}$$ =0.835 NRMSE = 0.091. results show SNV MSC treatments improve correlation between reflectance LCC, different feature screening methods greater impact model accuracy. It was shown SNV-based processed combined with GA-BP has great potential efficient monitoring grapevine. This method provides new theory constructing analytical grapevine indicators.

Язык: Английский

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Central Asia’s desertification challenge: Recent trends and drives explored with Google Earth Engine

Journal of Cleaner Production, Год журнала: 2024, Номер 460, С. 142595 - 142595

Опубликована: Май 17, 2024

Язык: Английский

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Comparison of global and zonal modeling strategies - A case study of soil organic matter and C:N ratio mapping in Altay, Xinjiang, China

Ecological Informatics, Год журнала: 2024, Номер 84, С. 102882 - 102882

Опубликована: Ноя. 17, 2024

Язык: Английский

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Digital mapping of soil salinity with time-windows features optimization and ensemble learning model

Ecological Informatics, Год журнала: 2024, Номер unknown, С. 102982 - 102982

Опубликована: Дек. 1, 2024

Язык: Английский

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Solar Induced Chlorophyll Fluorescence: Origins and Applications, Relation to Photosynthesis and Retrieval

Elsevier eBooks, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

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Optimal time-window for assessing soil salinity via Sentinel-2 multitemporal synthetic data in the arid agricultural regions of China

Ecological Indicators, Год журнала: 2025, Номер 176, С. 113642 - 113642

Опубликована: Май 27, 2025

Язык: Английский

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Remote sensing and machine learning algorithms to predict soil salinity in southern Kazakhstan

Discover Sustainability, Год журнала: 2024, Номер 5(1)

Опубликована: Окт. 28, 2024

Salinization and land degradation are significant challenges in the southern regions of Kazakhstan. These issues arise due to climate change, unequal water resource distribution, human impact. The primary concern revolves around resources, which influenced by area's trans boundary flow major rivers. low level food security has pushed development new approaches based on remote sensing monitoring geographic information systems (GIS) provide solutions for soil salinity. research aims focus utilizing high-resolution radar images. This data type is effective cloudy weather can be useful continued some areas. Machine learning methods solve problem automatic mapping agricultural salinity Kazakhstan's regions. precise area helps prevent or decrease salinity's impact agriculture. experiment realized that complex models such as LightGBM do not have accuracy performance over simple a small dataset compared with Ridge regression. results allow us recommend an approach further improvement ground-based measurement other deep-learning lands.

Язык: Английский

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Novel spectral indices and transfer learning model in estimat moisture status across winter wheat and summer maize

Computers and Electronics in Agriculture, Год журнала: 2024, Номер 229, С. 109762 - 109762

Опубликована: Дек. 6, 2024

Язык: Английский

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