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Journals / Sigma Mühendislik ve Fen Bilimleri Dergisi / 2020 / Cilt: 38 - Sayı: 4

ESTIMATION PERFORMANCE OF SOIL MOISTURE WITH GPS-IR METHOD

Cemali ALTUNTAŞ
Nursu TUNALIOĞLU
Pages
2217–2230
Publication Date
2021-10-05
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Here, Soil Moisture Content (SMC) is estimated from SNR data with four data analyzing strategies using Lomb Scargle Periodogram to retrieve dominant frequency as; (1) considering it is four times greater than background noise assuming the reflector height is inconstant in each day, (2) considering it is three times greater than background noise assuming the reflector height is inconstant in each day, (3) assuming the reflector height is constant and median values are used for overall estimations in each day, (4) assuming the reflector height is constant and median values are used in each day.

📌 3 Key Takeaways

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Objective & Hypothesis:This study aims to retrieve soil moisture from Global Positioning System (GPS) Signal-to-Noise Ratio (SNR) data with varying analysis to compute the best-fitting analyzing methodology.
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Key Findings & Data:Here, Soil Moisture Content (SMC) is estimated from SNR data with four data analyzing strategies using Lomb Scargle Periodogram to retrieve dominant frequency as; (1) considering it is four times greater than background noise assuming the reflector height is inconstant in each day, (2) considering it is three times greater than background noise assuming the reflector height is inconstant in each day, (3) assuming the reflector height is constant and median values are used for overall estimations in each day, (4) assuming the reflector height is constant and median values are used in each day.
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Conclusion & Impact:Results show that SMC estimated from SNR-derived metrics shows well agreement with in-situ measurements i.e. as the highest correlation of 95%; whilst the second strategy was followed.
🔬Design:Research Article
📍Scope:2015 Verileri
⚙️Variables:Global Positioning System Interferometric Reflectometry (GPS-IR) · Signal-to-Noise Ratio (SNR) · Soil Moisture Content (SMC)

Abstract

This study aims to retrieve soil moisture from Global Positioning System (GPS) Signal-to-Noise Ratio (SNR) data with varying analysis to compute the best-fitting analyzing methodology. Phase, amplitude, and reflector height, which are SNR-derived interferogram metrics are examined and results are proofed with respect to correlation coefficients compared with in-situ measurements. Here, Soil Moisture Content (SMC) is estimated from SNR data with four data analyzing strategies using Lomb Scargle Periodogram to retrieve dominant frequency as; (1) considering it is four times greater than background noise assuming the reflector height is inconstant in each day, (2) considering it is three times greater than background noise assuming the reflector height is inconstant in each day, (3) assuming the reflector height is constant and median values are used for overall estimations in each day, (4) assuming the reflector height is constant and median values are used in each day. To do that, the GPS Interferometric Reflectometry (GPS-IR) method was implemented to the data of OSOR station, installed in Chile (within the scope of the CAP Andes GPS Network Project carried out by UNAVCO), during 213 days from 01 January 2015 to 31 July 2015. Validation of the estimates is done by the recorded soil moistures from the Oromo Calibration Site in the LAB-net network. Results show that SMC estimated from SNR-derived metrics shows well agreement with in-situ measurements i.e. as the highest correlation of 95%; whilst the second strategy was followed.

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Cite This Article

ALTUNTAŞ, C., TUNALIOĞLU, N. (2021). ESTIMATION PERFORMANCE OF SOIL MOISTURE WITH GPS-IR METHOD. Sigma Mühendislik ve Fen Bilimleri Dergisi, Cilt: 38 - Sayı: 4, 2217–2230. https://ajindex.com/articles/419019

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