GNSS-IR Soil Moisture Retrieval Using Multi-Satellite Data Fusion Based on Random Forest

IF 4.2 2区地球科学 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Pub Date : 2024-09-15 DOI:10.3390/rs16183428

Yao Jiang, Rui Zhang, Bo Sun, Tianyu Wang, Bo Zhang, Jinsheng Tu, Shihai Nie, Hang Jiang, Kangyi Chen

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引用次数: 0

Abstract

The accuracy and reliability of soil moisture retrieval based on Global Positioning System (GPS) single-star Signal-to-Noise Ratio (SNR) data is low due to the influence of spatial and temporal differences of different satellites. Therefore, this paper proposes a Random Forest (RF)-based multi-satellite data fusion Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) soil moisture retrieval method, which utilizes the RF Model’s Mean Decrease Impurity (MDI) algorithm to adaptively assign arc weights to fuse all available satellite data to obtain accurate retrieval results. Subsequently, the effectiveness of the proposed method was validated using GPS data from the Plate Boundary Observatory (PBO) network sites P041 and P037, as well as data collected in Lamasquere, France. A Support Vector Machine model (SVM), Radial Basis Function (RBF) neural network model, and Convolutional Neural Network model (CNN) are introduced for the comparison of accuracy. The results indicated that the proposed method had the best retrieval performance, with Root Mean Square Error (RMSE) values of 0.032, 0.028, and 0.003 cm3/cm3, Mean Absolute Error (MAE) values of 0.025, 0.022, and 0.002 cm3/cm3, and correlation coefficients (R) of 0.94, 0.95, and 0.98, respectively, at the three sites. Therefore, the proposed soil moisture retrieval model demonstrates strong robustness and generalization capabilities, providing a reference for achieving high-precision, real-time monitoring of soil moisture.

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利用基于随机森林的多卫星数据融合进行 GNSS-IR 土壤水分检索

受不同卫星时空差异的影响，基于全球定位系统（GPS）单星信噪比（SNR）数据的土壤水分检索精度和可靠性较低。因此，本文提出了一种基于随机森林（RF）的多卫星数据融合全球导航卫星系统干涉反射测量（GNSS-IR）土壤水分检索方法，该方法利用 RF 模型的平均减小杂质（MDI）算法自适应地分配弧形权重，以融合所有可用的卫星数据，从而获得精确的检索结果。随后，利用来自板块边界观测站（PBO）网络站点 P041 和 P037 的 GPS 数据以及在法国拉马斯奎尔收集的数据验证了所提方法的有效性。为比较精度，引入了支持向量机模型（SVM）、径向基函数（RBF）神经网络模型和卷积神经网络模型（CNN）。结果表明，建议的方法具有最佳的检索性能，在三个地点的均方根误差（RMSE）值分别为 0.032、0.028 和 0.003 cm3/cm3，平均绝对误差（MAE）值分别为 0.025、0.022 和 0.002 cm3/cm3，相关系数（R）分别为 0.94、0.95 和 0.98。因此，所提出的土壤水分检索模型具有很强的鲁棒性和泛化能力，为实现土壤水分的高精度实时监测提供了参考。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Remote Sensing REMOTE SENSING-

CiteScore

8.30

自引率

24.00%

发文量

5435

审稿时长

20.66 days

期刊介绍： Remote Sensing (ISSN 2072-4292) publishes regular research papers, reviews, letters and communications covering all aspects of the remote sensing process, from instrument design and signal processing to the retrieval of geophysical parameters and their application in geosciences. Our aim is to encourage scientists to publish experimental, theoretical and computational results in as much detail as possible so that results can be easily reproduced. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.