High-Resolution Soil Moisture Mapping: Reconstruction, Fusion and Applications

Soil moisture is a key variable regulating exchanges of water, energy, and carbon between the land surface and the atmosphere. Accurate, high-resolution soil moisture information is essential for drought and flood monitoring, agricultural water management, climate assessment, ecosystem modeling, and hydrological forecasting. However, existing soil moisture observations are often constrained by coarse spatial resolution, discontinuous temporal coverage, shallow sensing depth, retrieval uncertainty, and limited ground-based measurements. These limitations restrict their application in heterogeneous landscapes and at field, watershed, and regional scales, and they are especially acute for root-zone soil moisture, which satellite retrievals cannot observe directly and which remains one of the least resolved variables in land–atmosphere science.The goal of this Research Topic is to advance theories, methods, datasets, and applications that deliver spatially and temporally continuous, high-resolution soil moisture products — surface and root-zone alike. Data assimilation, statistical methods, machine learning, and multi-source data fusion are treated here as complementary tools toward that end, alongside land surface and hydrological modeling, rather than as the subject in their own right. Despite rapid methodological progress, major challenges remain in integrating heterogeneous observations, resolving scale mismatches, reconstructing missing information, representing root-zone conditions, ensuring physical consistency, and evaluating products in regions with sparse ground measurements. This Research Topic seeks to bring together studies using satellite observations, in situ measurements, land surface and hydrological models, data assimilation, statistical methods, artificial intelligence, and multi-source data fusion to generate accurate and continuous soil moisture information.Contributions addressing spatial downscaling, temporal interpolation, gap filling, long-term dataset reconstruction, and root-zone soil moisture estimation are particularly encouraged, as are contributions establishing cross-scale validation protocols, benchmark datasets, and error-propagation and uncertainty-quantification frameworks for evaluating reconstructed products . Studies that evaluate how reconstructed products support irrigation management, crop assessment, ecosystem analysis, hydrological forecasting, and climate research are also welcomed, alongside applications in drought and flood monitoring as one set of downstream use cases among several. By integrating advances in remote sensing, hydrology, soil science, meteorology, agricultural science, and artificial intelligence, this Research Topic aims to promote robust and interpretable reconstruction frameworks and facilitate the development of reliable, well-validated soil moisture datasets for scientific and operational applications.This Research Topic welcomes high-quality original research articles, review articles, mini-reviews, perspectives, methods papers, data reports, and case studies that advance high-resolution soil moisture reconstruction. Topics of interest include, but are not limited to: - Satellite soil moisture downscaling- Spatiotemporal data fusion- Reconstruction of missing or discontinuous observations- Long-term and near-real-time dataset development- Surface and root-zone soil moisture estimation- Integration of optical, thermal, microwave, meteorological, topographic, vegetation, and model-based information- Data assimilation and hybrid modeling frameworks- Machine learning, deep learning, and physically informed artificial intelligence methods applied specifically to reconstruction- Cross-scale validation, benchmark datasets, error propagation, uncertainty quantification, model interpretability, regional transferability, and computational efficiencyApplications in precision agriculture, irrigation scheduling, hydrological forecasting, land–atmosphere interaction, ecosystem assessment, and climate analysis are also within scope, alongside drought and flood monitoring. Interdisciplinary studies offering methodological innovation, open datasets or tools, rigorous validation, or practical decision-support applications are particularly welcome. Topic editors: Haoxuan Yang, Mengyuan Xu Source: https://www.frontiersin.org/research-topics/85488/high-resolution-soil-moisture-mapping-reconstruction-fusion-and-applications