Advancements in Soil Moisture Estimation: Integrating Dual-Polarization GNSS-R and SAR Technologies for Precision Agriculture

Accurate soil moisture estimation is critical for precision agriculture, sustainable water resource management, and climate modeling. Despite significant advances in remote sensing technologies, achieving high spatial and temporal resolution measurements of soil moisture across heterogeneous agricultural landscapes remains a substantial challenge. This Research Topic aims to address these limitations by exploring the integration of dual-polarization global navigation satellite system reflectometry (GNSS-R) with synthetic aperture radar (SAR) data to enhance the accuracy and reliability of soil moisture retrieval.The transition from single-polarization to dual-polarization GNSS-R represents a significant technological advancement, offering greater sensitivity for characterizing soil properties and surface features. When combined with the high-resolution capabilities of SAR, this integration holds promise for overcoming current limitations in soil moisture monitoring. Key knowledge gaps that this Research Topic seeks to address include: (1) optimal fusion algorithms for multi-sensor data integration; (2) calibration and validation methodologies for dual-polarization GNSS-R in agricultural contexts; (3) scalability of the integrated approaches across different crop types and soil conditions; and (4) real-time implementation strategies for precision agriculture applications.Soil moisture is a fundamental variable in the Earth’s water cycle and plays a crucial role in agricultural productivity, drought monitoring, and flood forecasting. Traditional in-situ measurements, while accurate, are spatially limited and labor-intensive. Remote sensing techniques, particularly microwave-based methods, offer the potential for continuous, large-scale monitoring. GNSS-R has emerged as a cost-effective technique that utilizes reflected GNSS signals to estimate soil moisture, while SAR provides high-resolution active sensing capabilities. Dual-polarization GNSS-R demonstrates enhanced retrieval accuracy by capturing additional information about surface scattering mechanisms. When synergistically combined with SAR systems (e.g., Sentinel-1, NISAR, RADARSAT-2), the complementary nature of GNSS-R’s temporal resolution and SAR’s spatial detail creates opportunities for robust, multi-scale soil moisture products. This Research Topic seeks to advance understanding of these integrated approaches and their practical implementation in precision agriculture and water resource management.This Research Topic welcomes contributions including, but not limited to, the following themes: • Dual-polarization GNSS-R methodologies: Novel algorithms, calibration techniques, and validation strategies for dual-polarization soil moisture retrieval.• SAR and GNSS-R data fusion: Integration frameworks, machine learning approaches (including neural networks), and synergistic retrieval methods.• Precision agriculture applications: Decision support systems, irrigation scheduling, detection of irrigation events, mapping of irrigated areas, yield prediction, and crop health monitoring using integrated soil moisture products.• Multi-scale analysis: Techniques for bridging spatial resolution gaps between GNSS-R and SAR observations.• Validation and uncertainty quantification: Ground truth verification activities, error characterization, and quality assessment of integrated products.• Operational systems: Real-time processing pipelines, data assimilation schemes, and practical implementation challenges.• Regional studies: Case studies demonstrating the application of integrated approaches in different agricultural regions and climate zones. Topic editors: Fang Huang, Srinivas Nagaraja Source: https://www.frontiersin.org/research-topics/83183/advancements-in-soil-moisture-estimation-integrating-dual-polarization-gnss-r-and-sar-technologies-for-precision-agriculture