A Semi-Empirical SNR Model for Soil Moisture Retrieval Using GNSS SNR Data
A Semi-Empirical SNR Model for Soil Moisture Retrieval Using GNSS SNR Data
复制标题
使用 GNSS SNR 数据反演土壤湿度的半经验 SNR 模型
DOI：
10.3390/rs10020280
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发表时间：
2018-02-01
期刊：
影响因子：
5
通讯作者：
Song, Shuhui
中科院分区：
文献类型：
--
作者：
Han, Mutian;Zhu, Yunlong;Song, Shuhui
研究了全球导航卫星系统-干涉和反射测量(GNSS-IR)技术在土壤水分遥感中的应用。针对GNSS接收机常规采集的信噪比数据，提出了一种半经验信噪比(SNR)曲线拟合模型。该模型的目的是从SNR数据中重建直接反射信号，同时提取受土壤水分影响的频率和相位信息，这是K.M.Larson等人提出的。这是基于成熟的SNR模型，通过分别用二阶和四阶多项式来逼近直接信号和反射信号来经验地实现的。与其他模型(K.M.Larson等，T.Yang等)相比，该模型可以在不需要多少先验知识的情况下提高拟合质量(QOF)，并且可以从重建的信号中估计土壤的介电常数。在建立该模型的过程中，我们通过仿真展示了噪声对接收机信噪比估计的影响，从而说明了在裸土假设下模型的性能。结果表明，在5度~15度范围内重建的信号对土壤水分的反演效果较好。QOF提高了约45%，从而更好地估计了频率和相位信息。然而，我们发现对相位估计的改进可以忽略不计。在法国拉马斯基尔收集的实验数据也被用来验证所提出的模型。将计算结果与模拟结果和前人的工作进行了比较。结果表明，即使在信噪比变化不规律的情况下，该模型也能保证良好的拟合质量。此外，根据重建信号计算的土壤湿度与地面真实测量结果接近约15%。在这一阶段，对拉森模型和所提出的模型有了更深入的了解，这形成了对这一事实的可能解释。此外，还研究了利用该模型提取的频率和相位信息对土壤水分变化的监测能力。最后，对检索歧义、错误敏感等现象进行了描述和讨论。
The Global Navigation Satellite System-Interferometry and Reflectometry (GNSS-IR) technique on soil moisture remote sensing was studied. A semi-empirical Signal-to-Noise Ratio (SNR) model was proposed as a curve-fitting model for SNR data routinely collected by a GNSS receiver. This model aims at reconstructing the direct and reflected signal from SNR data and at the same time extracting frequency and phase information that is affected by soil moisture as proposed by K. M. Larson et al. This is achieved empirically through approximating the direct and reflected signal by a second-order and fourth-order polynomial, respectively, based on the well-established SNR model. Compared with other models (K. M. Larson et al., T. Yang et al.), this model can improve the Quality of Fit (QoF) with little prior knowledge needed and can allow soil permittivity to be estimated from the reconstructed signals. In developing this model, we showed how noise affects the receiver SNR estimation and thus the model performance through simulations under the bare soil assumption. Results showed that the reconstructed signals with a grazing angle of 5 degrees-15 degrees were better for soil moisture retrieval. The QoF was improved by around 45%, which resulted in better estimation of the frequency and phase information. However, we found that the improvement on phase estimation could be neglected. Experimental data collected at Lamasquere, France, were also used to validate the proposed model. The results were compared with the simulation and previous works. It was found that the model could ensure good fitting quality even in the case of irregular SNR variation. Additionally, the soil moisture calculated from the reconstructed signals was about 15% closer in relation to the ground truth measurements. A deeper insight into the Larson model and the proposed model was given at this stage, which formed a possible explanation of this fact. Furthermore, frequency and phase information extracted using this model were also studied for their capability to monitor soil moisture variation. Finally, phenomena such as retrieval ambiguity and error sensitivity were stated and discussed.