Comparative Performance Analysis of Heterogeneous Ensemble Learning Models for Multi-Satellite Fusion GNSS-IR Soil Moisture Retrieval

Yao Jiang, Rui Zhang (), Hang Jiang, Bo Zhang, Kangyi Chen, Jichao Lv, Jie Chen and Yunfan Song
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Yao Jiang: Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China
Rui Zhang: Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China
Hang Jiang: Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China
Bo Zhang: Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China
Kangyi Chen: Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China
Jichao Lv: Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China
Jie Chen: Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Yunfan Song: Institute of Plateau Meteorology, China Meteorological Administration, Chengdu 610072, China

Land, 2025, vol. 14, issue 9, 1-20

Abstract: Given the complexity of near-surface soil moisture retrieval, a single machine learning algorithm often struggles to capture the intricate relationships among multiple features, resulting in limited generalization and robustness. To address this issue, this study proposes a multi-satellite fusion GNSS-IR soil moisture retrieval method based on heterogeneous ensemble machine learning models. Specifically, two heterogeneous ensemble learning strategies (Bagging and Stacking) are combined with three base learners, Back Propagation Neural Network (BPNN), Random Forest (RF), and Support Vector Machine (SVM), to construct eight ensemble GNSS-IR soil moisture retrieval models. The models are validated using data from GNSS stations P039, P041, and P043 within the Plate Boundary Observatory (PBO) network. Their retrieval performance is compared against that of individual machine learning models and a deep learning model (Multilayer Perceptron, MLP), enabling an optimized selection of algorithms and model architectures. Results show that the Stacking-based models significantly outperform those based on Bagging in terms of retrieval accuracy. Among them, the Stacking (BPNN-RF-SVM) model achieves the highest performance across all three stations, with R of 0.903, 0.904, and 0.917, respectively. These represent improvements of at least 2.2%, 2.8%, and 2.1% over the best-performing base models. Therefore, the Stacking (BPNN-RF-SVM) model is identified as the optimal retrieval model. This work aims to contribute to the development of high-accuracy, real-time monitoring methods for near-surface soil moisture.

Keywords: heterogeneous ensemble learning; GNSS-IR; Bagging; Stacking; signal-to-noise ratio; soil moisture retrieval (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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