Advanced navigation techniques reveal soil moisture patterns

IGBP global land cover categories from SMAP product (40°S–40°N). Credit: Satellite Navigation
A pioneering method for soil moisture retrieval using satellite navigation systems has been introduced, significantly improving the accuracy and efficiency of global data collection. The study, published in the journal Satellite Navigation, addresses the challenges posed by geographical differences in soil moisture assessment, providing an important advancement in monitoring a key parameter for climate, agricultural and environmental applications.
This study leverages data from the Cyclone Global Navigation Satellite System (CYGNSS) constellation and the Soil Moisture Active Passive (SMAP) product to provide a refined approach that reduces the reliance on ancillary data and enhances soil moisture retrievals at both regional and global scales.
Soil moisture plays a vital role in the Earth’s water cycle, influencing weather patterns, crop growth and ecosystem health. Traditional acquisition methods such as microwave remote sensing are limited in providing high-resolution data due to complex topography and surface conditions.
Recently, Global Navigation Satellite System-Reflectometers (GNSS-R) have emerged as an innovative solution for monitoring soil moisture. However, existing techniques often suffer from geographic variability and over-reliance on additional data, highlighting the need for more sophisticated models to accurately capture global soil moisture dynamics.
The study led by Huang et al. presents an advanced approach to soil moisture retrieval using space-borne GNSS reflectometry. The study addresses the geographical disparity by integrating Cyclone Global Navigation Satellite System (CYGNSS) and Soil Moisture Active Passive (SMAP) data and devising five different models customized for different geographical grids.
This customized method not only improves retrieval accuracy but also minimizes the need for supplementary data, significantly enhancing capabilities over traditional single-model approaches and establishing a new standard for soil moisture assessment.
The study presents a method to improve soil moisture retrieval accuracy by accounting for geographic differences that are often overlooked in traditional models. Using data from CYGNSS and SMAP, the researchers developed five unique models designed for different geographic grids with different surface conditions.
The model was optimized based on key performance metrics such as root mean square error (SRMSE), resulting in an average 9.1% reduction in SRMSE and an average 22.7% improvement in correlation coefficient compared to traditional methods. This innovative approach reduces reliance on redundant ancillary data, effectively adapts to regional variability, and provides accurate and reliable soil moisture estimates worldwide.
“Our work directly addresses the challenge of geographic variability in soil moisture retrievals. By tailoring the model to specific regions, we have developed a method that not only increases accuracy but also reduces reliance on ancillary data, making it a valuable tool for environmental and climate research,” said Dr. Fayed Chen, corresponding author of the study.
“This method is adaptable to a wide range of conditions around the globe, marking a major step forward in soil moisture monitoring and application to real-world scenarios.”
This advanced soil moisture retrieval model has far-reaching implications for environmental monitoring, agriculture, and climate research: by providing more accurate soil moisture data without extensive auxiliary inputs, the model can improve weather forecasts, optimize irrigation strategies, and enhance disaster management activities such as flood and drought response.
Its adaptability to different landscapes and climates makes it a valuable tool for scientists and policy makers seeking to better understand and manage the world’s water resources, ultimately supporting sustainable agricultural practices and climate resilience.
Further information: Liangke Huang et al., “A new global grid model for soil moisture retrieval considering geographic differences of space-borne GNSS-R,” Satellite Navigation (2024). DOI: 10.1186/s43020-024-00150-9
Courtesy of the Chinese Academy of Sciences
Source: Advanced navigation techniques reveal soil moisture patterns (September 16, 2024) Retrieved September 16, 2024 from https://phys.org/news/2024-09-unveiling-soil-moisture-patterns-advanced.html
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