魏浩翰, 张强, 沈飞. 基于熵值法轨迹聚类多特征参数融合的GNSS-IR土壤湿度反演方法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20230419
引用本文: 魏浩翰, 张强, 沈飞. 基于熵值法轨迹聚类多特征参数融合的GNSS-IR土壤湿度反演方法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20230419
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WEI Haohan, ZHANG Qiang, SHEN Fei. GNSS-IR Soil Moisture Estimation Based on Track Clustering and Multi Characteristic Parameter Fusion Using Entropy Method[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230419
Citation: WEI Haohan, ZHANG Qiang, SHEN Fei. GNSS-IR Soil Moisture Estimation Based on Track Clustering and Multi Characteristic Parameter Fusion Using Entropy Method[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230419
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基于熵值法轨迹聚类多特征参数融合的GNSS-IR土壤湿度反演方法

GNSS-IR Soil Moisture Estimation Based on Track Clustering and Multi Characteristic Parameter Fusion Using Entropy Method

    摘要
  • 摘要: 全球导航卫星系统干涉反射(global navigation satellite systeminterferometric reflectometry,GNSS-IR)技术作为一种近地遥感的新兴手段，在土壤湿度监测方面凭借其低成本、高精度等优点成为近些年的研究热点。为了提高GNSS-IR技术反演土壤湿度的精度，选取位于南京市溧水区的自建GNSS测站原始观测数据作为数据源，提取GPS、BDS、GLONASS、Galileo等系统信噪比(signal-to-noise ratio,SNR）观测数据的多路径干涉相位、振幅和频率等特征参数，分析不同频段、不同轨迹的特征参数随土壤湿度的变化规律，提出一种顾及卫星轨迹差异的多特征数据融合的GNSS-IR土壤湿度反演方法。首先按照不同卫星轨道、不同频段进行轨迹聚类融合，然后采用熵值法进行土壤湿度反演，并将多系统轨迹融合反演结果与传统均权融合方法、多元线性回归方法进行比较。结果表明，SNR的相位、振幅、频率三种特征参数组合进行土壤湿度融合反演比单一相位特征参数及相位、振幅两种特征参数组合的反演结果更优；多系统轨迹融合较单系统轨迹融合反演性能普遍提升，平均相关系数相比单系统提高了4.0%，均方根误差降低了22.8%—39.9%；基于熵值法的多系统轨迹聚类融合土壤湿度反演方法较传统均权融合方法、多元线性回归方法以及赋权融合法反演RMSE分别降低34.0%、25.6%和29.5%。本文提出的方法能够提供长期、准确的土壤湿度反演结果。

     

    Abstract: Objectives: GNSS-IR technology, as an emerging tool for near-Earth remote sensing, has become a research hotspot in recent years in the area of soil moisture monitoring, with its low cost and high precision. Methods: In order to improve the accuracy of GNSS-IR technology in retrieving soil moisture, a self-built GNSS station located in Lishui District, Nanjing City was selected as the research data source, Firstly, feature parameters such as multipath coherent phase, amplitude, and frequency of Signal-to-Noise Ratio (SNR) observation data from GPS, BDS, GLONASS, Galileo, and other systems are extracted. Based on the analysis of the characteristics of GNSS observation SNR changes with soil moisture in different systems, different frequency bands and different orbits, a multi GNSS system feature data fusion inversion method considering satellite trajectory differences was proposed. For the observation data of multiple GNSS systems, trajectory clustering fusion is carried out according to different orbits and frequency bands. After confirming the weight using entropy method, soil moisture inversion is carried out. The results of multi-system trajectory fusion inversion are compared with traditional average weight fusion methods and multiple linear regression methods. Results: The combination of SNR's phase, amplitude, and frequency feature parameters for soil moisture fusion inversion is better than the inversion results of a single phase feature parameter and a combination of two feature parameters (phase, amplitude); Multi-system trajectory fusion generally improves the inversion performance compared to single system trajectory fusion, with an average correlation coefficient increasing by 4.0% and RMSE decreasing by 22.8% to 39.9%; The multi-system trajectory clustering fusion soil moisture inversion method based on entropy method reduces RMSE by 34.0%, 25.6% and 29.5% respectively compared to traditional average weight fusion method, multiple linear regression method and weighted fusion method. Conclusions: The proposed method can provide long-term and accurate soil moisture inversion results.

     

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