Synergistic Earth Observation for Advanced Soil Moisture Datasets
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation Data".
Deadline for manuscript submissions: 31 January 2027 | Viewed by 31
Special Issue Editors
Interests: microwave remote sensing; soil moisture datasets; downscaling; validation; ground networks; freeze–thaw
Interests: passive microwave remote sensing; soil moisture; vegetation optical depth; machine learning; data assimilation; climate records
Interests: multi-sensor remote sensing; soil moisture downscaling; evapotranspiration; irrigation; agricultural hydrology
Interests: microwave remote sensing; soil moisture; freeze-thaw; vegetation optical depth; active-passive microwave modelling; water cycle
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Special Issue Information
Dear Colleagues,
Satellite remote sensing has turned soil moisture from a sparsely observed variable into a routinely mapped component of the terrestrial water cycle. These data are now widely used in hydrology, agriculture, drought assessment, ecosystem studies, and land-atmosphere research. The central challenge, however, has shifted. The field is no longer concerned only with whether soil moisture can be retrieved from individual sensors, but with whether the resulting datasets are consistent, traceable, and useful across scales and applications. Current products still differ in spatial support, temporal coverage, sensing depth, long-term stability, and uncertainty information. Such differences often become most evident over heterogeneous landscapes, where vegetation, soil texture, surface roughness, topography, and freeze–thaw processes complicate both retrieval and validation. No single observing system can resolve these limitations alone.
This Special Issue focuses on advanced soil moisture datasets developed through synergistic Earth observation. We welcome studies that use complementary satellite observations, ground measurements, land surface models, data assimilation, and artificial intelligence to address known weaknesses in existing soil moisture products. The emphasis is not on retrieval algorithms in isolation, but on datasets whose generation, evaluation, documentation, uncertainty estimates, and reuse are clearly demonstrated. This focus fits the Earth Observation Data section of Remote Sensing, where the value of a dataset depends not only on its accuracy, but also on its transparency, reproducibility, and relevance to clearly defined scientific and practical needs.
We invite original research articles, reviews, and methodological or application-oriented studies on multi-source soil moisture dataset development; active/passive microwave and optical/thermal synergy; downscaling, merging, and assimilation; ground validation and representativeness errors; benchmark and uncertainty-aware datasets; machine-learning-ready datasets with quality flags and uncertainty information; long-term harmonized records; and applications in drought monitoring, agricultural water management, hydrological prediction, ecosystem assessment, hydroclimate variability, and land–atmosphere coupling.
Dr. Jingyao Zheng
Dr. Nemesio Rodriguez-Fernandez
Dr. Olivier Merlin
Dr. Tianjie Zhao
Guest Editors
Manuscript Submission Information
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Keywords
- soil moisture datasets
- synergistic Earth observation
- multi-source data integration
- active and passive microwave remote sensing
- downscaling and data fusion
- soil moisture validation
- representativeness error
- uncertainty characterization
- long-term harmonized records
- AI-ready earth observation data
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