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

Land, Journal Year: 2025, Volume and Issue: 14(1), P. 110 - 110

Published: Jan. 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.

Language: Английский

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

Ecological Informatics, Journal Year: 2024, Volume and Issue: unknown, P. 102982 - 102982

Published: Dec. 1, 2024

Language: Английский

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Exploring Rangeland Dynamics in Punjab, Pakistan: Integrating LULC, LST, and Remote Sensing for Ecosystem Analysis (2000–2020)

Rangeland Ecology & Management, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 1, 2024

Language: Английский

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Retrievaling Soil Salinity Based on Optimal Temporal Remote Sensing Derived from Effects of Salt-Alkalia Soil on Crop Stress

Published: Jan. 1, 2024

Language: Английский

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Effects of salt content and particle size on spectral reflectance and model accuracy: Estimating soil salt content in arid, saline-alkali lands

Microchemical Journal, Journal Year: 2024, Volume and Issue: unknown, P. 111666 - 111666

Published: Sept. 1, 2024

Language: Английский

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Remote Sensing-Based Earth Climate Detection in Geoscience Model with Artificial Intelligence Application

Remote Sensing in Earth Systems Sciences, Journal Year: 2024, Volume and Issue: 7(4), P. 569 - 581

Published: Oct. 8, 2024

Language: Английский

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Digital mapping of soil properties using geomatics: integration of GIS, GPS, and remote sensing applications

Arabian Journal of Geosciences, Journal Year: 2024, Volume and Issue: 17(12)

Published: Dec. 1, 2024

Language: Английский

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Aplicaciones de la inteligencia artificial en el monitoreo y conservación ambiental: una revisión exploratoria

REVISTA AMBIENTAL AGUA AIRE Y SUELO, Journal Year: 2024, Volume and Issue: 15(2), P. 48 - 68

Published: Sept. 27, 2024

Este artículo explora el uso de la inteligencia artificial en vigilancia y preservación del agua, aire suelo. El análisis examinó estudios revisador por pares publicados entre 2020 2024, con un enfoque específico contribución a mejora las técnicas gestión ambiental. procedimiento selección se limitó treinta tres investigaciones pertinentes, que clasificaron dominios principales, calidad suelo, contaminación monitoreo ambiental, aplicaciones IA. Las artificial, incluido aprendizaje automático profundo, muestran gran potencial para mejorar precisión predicciones optimizar asignación recursos varios campos ambientales. Los usos principales esta tecnología son evaluar predecir los niveles gestionar hídricos. La integración IA métodos convencionales eficacia Sin embargo, existen dificultades continuas garantizar confiabilidad datos, capacidad modelos aplicarse diferentes escenarios exitosa estos diversas situaciones. ha demostrado su generar cambios significativos conservación medio ambiente. posteriores deberían dar prioridad ampliación conjuntos incorporación tecnologías desarrollo resolución consecuencias socioeconómicas, fin aprovechar al máximo abordar cuestiones ambientales complejas.

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