Aiming at the problem of how to effectively improve the stability and accuracy of machine learning models for inversion of soil moisture,the paper proposes a stacking ensemble learning model to improve the accuracy and generalization ability of the model by combining the advantages of multiple machine learning models.Firstly,through correlation analysis,the observation data correlate best with soil moisture when the incidence angle is 42.5°.Secondly,a simulation database is constructed using the water cloud model and the τ-ω model,together with the observation data form the inversion dataset.Lastly,the soil moisture of sparse grassland is inverted using the four types of machine learning and the stacking ensemble learning model.The experimental results show that:the inversion results using active-passive synergistic microwave data have higher accuracy than that of single active or passive microwave data;the coefficient of determination of the inversion results of the optimal stacking ensemble learning method reaches 0.971 4,and the root mean square error and the average absolute error reach 0.013 6 cm3/cm3 and 0.010 2 cm3/cm3,which are better than that of the optimal single machine learning method,which proves the effectiveness of the proposed method.
维普
万方数据