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Ground-Based GPS for Soil Moisture Monitoring

机译:土壤水分监测地基GPS

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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:此土壤水分是地表水周期的重要组成部分。土壤水分的有效监测是对气象预报,洪水预报和作物生长具有重要意义。现有土壤湿度监测方法(如干燥称重,遥感观察,湿度计测量等)有高成本,低空间和时间分辨率,对观测物体的损伤,费时和费力的,并且长期反复观察周期。随着全球导航卫星系统的快速发展遥感,GPS信号的微波L波段用于土壤水分具有成本低,高时间分辨率,实时性强,自动化程度高,吸引了众多的目光的优点监控学者。本文打算研究基于GPS信噪比土壤水分反演算法。首先,根据基本原理,反演过程中给出。所述嵌合相位是零处理,并根据该相关系数进行融合。最后,湿度的线性模型。 PBO的计划站数据的反相进行,并且通过PBO提供的土壤湿度数据进行比较。结果证实,使用GPS信噪比土壤湿度反演的精度和稳定性,通过零处理和加权融合改善。

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