Improving land surface model accuracy in soil moisture

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      Improving the accuracy of land surface models (LSMs) in soil moisture simulations is crucial for better hydrological and climate predictions. Traditional LSMs rely on physically based parametric schemes to simulate soil moisture dynamics, but these models often suffer from uncertainties due to simplified parameterization and inadequate representation of complex soil-vegetation-atmosphere interactions. Recent advancements in machine learning (ML) offer a promising approach to enhancing soil moisture simulations by integrating data-driven techniques with conventional parametric schemes. ML algorithms, such as random forests, support vector machines, and deep learning models, can refine parameter estimation, optimize model calibration, and correct biases in LSM outputs. By leveraging remote sensing data, in-situ soil moisture measurements, and meteorological inputs, ML-based hybrid models can significantly improve predictive accuracy and spatial resolution. Integrating ML with parametric schemes can thus enhance the reliability of soil moisture simulations, supporting applications in agriculture, water resource management, and climate modeling.


#SoilMoisture #LandSurfaceModels #MachineLearning #Hydrology #ClimateModeling #RemoteSensing #DataScience #Sustainability #AIinScience #EnvironmentalModeling




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