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Ground-Based GPS for Soil Moisture Monitoring

机译:地面GPS用于土壤水分监测

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This Soil moisture is an important part of the surface water cycle. Effective monitoring of soil moisture is of great significance for weather forecasting, flood forecasting and crop growth. Existing soil moisture monitoring methods (such as drying weighing, remote sensing observation, hygrometer measurement, etc.) have high cost, low spatial and temporal resolution, damage to observation objects, time-consuming and laborious, and long repeated observation periods. With the rapid development of GNSS remote sensing, the GPS signal based on microwave L-band is used for soil moisture monitoring with the advantages of low cost, high time resolution, strong real-time and high automation, which has attracted the attention of many scholars. This paper intends to study the soil moisture inversion algorithm based on GPS signal-to-noise ratio. Firstly, based on the basic principle, the inversion process is given. The fitting phase is zero-processed and the fusion is performed according to the correlation coefficient. Finally, a linear model of humidity is established. The inversion of the PBO plan station data was carried out, and the soil moisture data provided by PBO was compared. The results confirmed that the accuracy and stability of soil moisture inversion using GPS signal-to-noise ratio were improved by zero processing and weighted fusion.
机译:土壤水分是地表水循环的重要组成部分。有效监测土壤水分对于天气预报,洪水预报和作物生长具有重要意义。现有的土壤水分监测方法(如干燥称重,遥感观测,湿度计测量等)具有成本高,时空分辨率低,对观测对象的损坏,费时费力且观察周期长的问题。随着GNSS遥感技术的飞速发展,基于微波L波段的GPS信号以其低成本,高时间分辨率,实时性强,自动化程度高等优点被用于土壤水分监测,引起了众多关注的关注。学者。本文旨在研究基于GPS信噪比的土壤水分反演算法。首先,根据基本原理,给出了反演过程。拟合阶段进行零处理,并根据相关系数进行融合。最后,建立湿度的线性模型。进行了PBO计划站数据的反演,并比较了PBO提供的土壤水分数据。结果证实,通过零处理和加权融合提高了利用GPS信噪比反演土壤水分的准确性和稳定性。

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