Soil Moisture Estimation by GNSS-IR from Active Stations: Case Study – RBMC/IBGE, UFPR Station

The Earth is a dynamic planet subject to numerous natural phenomena and processes that human activities have intensified. Monitoring variables associated with these phenomena is essential. Soil moisture, for example, plays a crucial role in climate systems, agriculture, and the hydrological cycle. The Global Navigation Satellite Systems (GNSS) is one of the Geodesy tools used for monitoring the Earth, through which the GNSS Interferometric Reflectometry (GNSS-IR) technique can be employed to estimate soil moisture. In this study, the UFPR station, part of the Brazilian Continuous Monitoring Network of GNSS Systems (RBMC) was selected for investigation. A Python script was developed to automate the preparation of GNSS data from any RBMC station. Different processing configurations of a reflectometric algorithm were evaluated, resulting in a set of time series of soil moisture for 2022. Results indicate that configurations adapted to the station’s local conditions contribute to the enhancement of the results. The best signal among the 24 evaluated was theprecise signal from the L2 frequency of GLONASS (RS2P). Peaks in precipitation were aligned with peaks in soil moisture, with soil moisture ranging from 0 to 0.35 m³/m³. The results support the development of a methodology for monitoring soil moisture in the vicinity of GNSS-RBMC stations that meet GNSS-IR requirements, expanding the potential applications of this network.

Authors

Institutions

Publication Details

Journal
Anuário do Instituto de Geociências
Published
2025-04-17
DOI
https://doi.org/10.11137/1982-3908_2025_48_65911
Primary Topic
Soil Moisture and Remote Sensing
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Soil Moisture Estimation by GNSS-IR from Active Stations: Case Study – RBMC/IBGE, UFPR Station

Luís Augusto Koenig Veiga, Cláudia Pereira Krueger, Wagner Carrupt Machado, Felipe Geremia‐Nievinski et al.
Anuário do Instituto de Geociências
Soil Moisture and Remote Sensing
article

Soil Moisture Estimation by GNSS-IR from Active Stations: Case Study – RBMC/IBGE, UFPR Station

Luís Augusto Koenig Veiga, Cláudia Pereira Krueger, Wagner Carrupt Machado, Felipe Geremia‐Nievinski, Jorge Felipe Euriques
article en

Abstract

The Earth is a dynamic planet subject to numerous natural phenomena and processes that human activities have intensified. Monitoring variables associated with these phenomena is essential. Soil moisture, for example, plays a crucial role in climate systems, agriculture, and the hydrological cycle. The Global Navigation Satellite Systems (GNSS) is one of the Geodesy tools used for monitoring the Earth, through which the GNSS Interferometric Reflectometry (GNSS-IR) technique can be employed to estimate soil moisture. In this study, the UFPR station, part of the Brazilian Continuous Monitoring Network of GNSS Systems (RBMC) was selected for investigation. A Python script was developed to automate the preparation of GNSS data from any RBMC station. Different processing configurations of a reflectometric algorithm were evaluated, resulting in a set of time series of soil moisture for 2022. Results indicate that configurations adapted to the station’s local conditions contribute to the enhancement of the results. The best signal among the 24 evaluated was theprecise signal from the L2 frequency of GLONASS (RS2P). Peaks in precipitation were aligned with peaks in soil moisture, with soil moisture ranging from 0 to 0.35 m³/m³. The results support the development of a methodology for monitoring soil moisture in the vicinity of GNSS-RBMC stations that meet GNSS-IR requirements, expanding the potential applications of this network.

Anuário do Instituto de GeociênciasVol. 48
Universidade Federal do Rio Grande do Sul (BR), Universidade Federal Rural do Rio de Janeiro (BR), Universidade Federal do Paraná (BR), Universidade Federal de Uberlândia (BR), University of Rio Grande and Rio Grande Community College (US)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Universidade Federal do Paraná
Openalex Percentile: Top 93%
Soil Moisture and Remote Sensing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.