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Comparison and analysis of dielectric models towards soil moisture and salinity estimations

The soil dielectric constant, the basis of the microwave remote sensing for soil moisture and salinity estimations, is one of the main parameters of microwave remote sensing research. It is very important to select the high precision soil water and salt dielectric model to improve the precision of s...

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Bibliographic Details
Main Authors: Long Wei, Weizhen Wang, Chunfeng Ma, Yueru Wu
Format: Conference Proceeding
Language:English
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Summary:The soil dielectric constant, the basis of the microwave remote sensing for soil moisture and salinity estimations, is one of the main parameters of microwave remote sensing research. It is very important to select the high precision soil water and salt dielectric model to improve the precision of soil moisture and salinity inversion. This paper presents a comparison and analysis of permittivity models base on the simulation values of Dobson model considering the salt effect on the free water (Dobson-S model) and WYR model and the measured values under the same condition at L-, C- and X-bands. The complex permittivity of soil samples with different texture, water and salinity contents by microwave vector network analyzer (VNA). Research result indicates that Dobson-S model and WYR model can well simulate the real part of the dielectric constant of saline soil. However, associating to the imaginary part of the saline soil dielectric constant, the results show: 1) Dobson-S model better than WYR model under the condition of soil volumetric water content (MV) equaling to 0.1(cm 3 /cm 3 ). 2) WYR model better than Dobson-S model when MV equals to 0.2 and 0.3(cm 3 /cm 3 ). 3) WYR model better than Dobson-S model at L- and C-bands, while two models performs unsatisfactorily at X-band under the condition of MV equaling to 0.4 (cm 3 /cm 3 ). The most significant contribution of this analysis is providing an insight for the future joint inversion of soil moisture and salinity based on microwave remote sensing.
ISSN:2153-7003
DOI:10.1109/IGARSS.2017.8127746