Fig 4. Study site location, sensor placement, and SM dynamics at Tokkerup, Denmark. (a) Overview map of Denmark indicating the location of the Tokkerup field site (red marker), alongside a satellite image of the field.

Short-Term SAR Change Detection for Soil Moisture Retrieval: A Case Study Over Danish Test Sites

BLUF (Bottom Line Up Front)

Researchers at Denmark's Technical University have validated a novel synthetic aperture radar (SAR) technique that retrieves high-resolution soil moisture data at field scale using Sentinel-1 satellite imagery, achieving correlation coefficients of 0.72 and root mean square errors below 4.32% across Danish agricultural test sites—a significant advancement for precision agriculture and water resource management.

New method leverages temporal changes in radar backscatter to overcome resolution limitations of existing soil moisture products

A breakthrough in agricultural remote sensing published in IEEE Transactions on Geoscience and Remote Sensing demonstrates that synthetic aperture radar satellites can accurately measure soil moisture variations at individual field scales—a capability that has eluded previous space-based systems limited to resolutions of several kilometers.

The research, led by Miquel Negre Dou and John Peter Merryman Boncori at the Technical University of Denmark's National Space Institute, validates a "short-term change detection" (STCD) methodology that analyzes how radar signals reflected from agricultural fields change between consecutive satellite passes. By assuming that soil moisture fluctuates more rapidly than vegetation and surface roughness, the technique extracts moisture information from the ratio of backscatter measurements taken days apart.

"The key innovation is recognizing that while a single radar measurement is influenced by multiple factors—soil moisture, vegetation cover, and surface roughness—the changes between closely-spaced observations are dominated by moisture variations," explains the study published in the February 2026 issue.

Bridging the Resolution Gap

Current operational soil moisture products from missions like NASA's Soil Moisture Active Passive (SMAP) and ESA's Soil Moisture and Ocean Salinity (SMOS) provide global coverage but at spatial resolutions of 25-40 kilometers—far too coarse for individual farm management decisions. The European Union's Copernicus Sentinel-1 constellation offers much finer spatial detail (10 meters) with six-day repeat coverage, but extracting quantitative soil moisture from these C-band radar observations has proven challenging due to confounding effects from vegetation and terrain.

The Danish team's approach constrains the STCD inversion using ancillary data including coarse-resolution soil moisture products from the Global Land Data Assimilation System (GLDAS), soil texture maps at 10-meter resolution from Denmark's DIGIJORD project, and field capacity estimates from OpenLandMap. The method processes 50-image temporal windows (approximately 300 days) to establish seasonal moisture patterns while responding to short-term weather-driven fluctuations.

Validation Against Ground Truth

The researchers validated their technique against two independent datasets spanning 2017-2020: an experimental field in Tokkerup with time-domain reflectometry sensors at 30, 60, and 90 cm depths, and 18 stations from Denmark's Hydrological Observatory (HOBE) network measuring surface moisture at 0-5 cm depth.

At the HOBE agricultural sites, the STCD method achieved an overall Pearson correlation of R=0.72 with ground measurements, substantially outperforming the GLDAS baseline product (R=0.63). Root mean square errors averaged 4.32%, with unbiased RMSE of 4.31%—meeting accuracy targets established for satellite soil moisture validation by the Committee on Earth Observation Satellites.

Performance varied by crop type and field conditions. Stations featuring permanent grass-clover mixtures and winter cereal rotations showed the strongest correlations (R≥0.77), while fields with root and tuber crops required additional seasonal adjustments to account for subsurface biomass effects on radar returns. The researchers developed an asymmetric sinusoidal correction model to handle these challenging crop types, improving correlations from R=0.38 to R=0.70 for potato-cereal rotations.

"For crops like potatoes that concentrate significant biomass near or below the soil surface, the standard backscatter ratios can be contaminated by volume scattering and surface roughness changes from root development," the authors note. "Our seasonal adjustment approach helps separate these vegetation-related signals from actual moisture dynamics."

Technical Innovations and Constraints

The methodology employs VV polarization (vertical transmit and receive) from Sentinel-1's dual-polarization mode, which provides greater sensitivity to soil moisture than cross-polarized (VH) measurements. The inversion scheme converts backscatter ratios to dielectric constant estimates using the Hallikainen empirical model relating permittivity to moisture content, sand fraction, and clay fraction.

A critical algorithmic choice involves constraining the otherwise underdetermined mathematical system. While the original STCD formulation used a bounded least-squares approach tied to the minimum observed moisture, the Danish team found this overly sensitive to biases in the external reference data. Instead, they implemented a linear interpolation constraint that relates the final image in each temporal window to the external product's moisture range, allowing retrievals to extend beyond the coarse product's dynamic range while maintaining physical consistency.

Field capacity—the maximum water soil can retain after drainage—serves as an essential upper bound to prevent unphysical estimates. The study found this constraint critical for robust performance, though it also limits the method's ability to capture saturation conditions following intense precipitation events.

Implications for Agricultural Water Management

The validation demonstrates that SAR-based field-scale soil moisture monitoring is technically feasible for operational agricultural applications in temperate climates with the six-day Sentinel-1 revisit cycle. Such information could support precision irrigation scheduling, yield forecasting, and drought monitoring at spatial scales relevant for farm management decisions.

The technique's global applicability depends on availability of appropriate ancillary datasets. While high-resolution soil texture maps like DIGIJORD exist only for select regions, global products such as SoilGrids250m provide worldwide coverage at 250-meter resolution with acceptable performance for the moisture retrieval algorithm. Similarly, the GLDAS model provides global soil moisture estimates at 0.25-degree resolution that can serve as inversion constraints.

Future Directions and Limitations

The authors acknowledge several constraints requiring further research. The fundamental assumption that vegetation and roughness change more slowly than soil moisture breaks down during rapid crop development phases, harvest operations, and tillage events. Winter freeze-thaw cycles introduce additional complications that were not systematically addressed in the current validation.

The C-band radar (5.4 GHz) penetrates only the top few centimeters of soil, while many agricultural applications require root-zone moisture estimates extending to 30-100 cm depth. The Danish validation at Tokkerup demonstrated that surface observations can track deeper moisture dynamics when combined with appropriate seasonal models, but this relationship is site-specific and depends on soil hydraulic properties.

Computational efficiency presents another consideration for operational implementation. Processing a single field through the 50-image temporal window involves matrix inversions across hundreds of acquisition dates from multiple satellite tracks. However, the increasing availability of cloud computing resources and pre-processed analysis-ready Sentinel-1 data makes continental-scale applications increasingly feasible.

The upcoming Copernicus expansion includes additional Sentinel-1 satellites (Sentinel-1C launched December 2024; Sentinel-1D planned) that will improve temporal sampling to better distinguish moisture fluctuations from vegetation dynamics. The European Space Agency is also developing the ROSE-L mission, a longer-wavelength (L-band) SAR with deeper soil penetration capability for launch in the 2030s.

Broader Context in Agricultural Remote Sensing

This work contributes to a growing body of research exploring synergies between active radar and passive microwave soil moisture observations. Recent studies have demonstrated downscaling approaches combining SMAP's accurate but coarse L-band radiometry with Sentinel-1's fine-resolution backscatter, achieving sub-kilometer moisture estimates with improved accuracy over radar-only methods.

The integration of optical vegetation indices from Sentinel-2 offers another avenue for refinement. Normalized Difference Vegetation Index (NDVI) time series can identify periods of rapid canopy development when radar backscatter becomes unreliable for moisture retrieval, enabling adaptive quality flagging. Several research groups are developing multi-sensor fusion frameworks that optimally weight SAR, radiometer, and optical inputs based on local conditions.

Machine learning approaches represent an alternative paradigm to the physics-based STCD methodology. Convolutional neural networks trained on historical SAR imagery, meteorological data, and ground observations have shown promise for direct soil moisture prediction without explicit electromagnetic modeling. However, these data-driven methods require extensive training datasets and may not generalize well to conditions outside their training envelope.

Implications for Climate and Hydrological Research

Beyond agricultural applications, high-resolution soil moisture data supports improved hydrological modeling, drought monitoring, and climate change studies. Soil moisture controls the partitioning of precipitation between runoff and infiltration, influencing flood risk and groundwater recharge. It also regulates land-atmosphere energy exchange through its effects on evapotranspiration, creating feedbacks that affect regional temperature and precipitation patterns.

The Danish validation sites, located in a temperate maritime climate with sandy loam soils, represent conditions common across Northern Europe's agricultural regions. The technique's performance in other climate zones—particularly semi-arid regions where moisture variability is greater and vegetation sparser—requires additional validation. Such environments may actually favor radar retrievals due to reduced vegetation attenuation, though more frequent moisture fluctuations could violate the STCD temporal stability assumptions.

The integration of field-scale satellite moisture observations into operational numerical weather prediction and seasonal forecasting systems represents a frontier research area. Current data assimilation schemes primarily ingest coarse-resolution products, but techniques for incorporating high-resolution heterogeneous observations are under development.

Verified Sources and Citations

Negre Dou, M., & Merryman Boncori, J. P. (2026). Short-Term SAR Change Detection for Soil Moisture Retrieval: A Case Study Over Danish Test Sites. IEEE Transactions on Geoscience and Remote Sensing, 64, 4403015. https://doi.org/10.1109/TGRS.2026.3658136
Balenzano, A., et al. (2021). Sentinel-1 soil moisture at 1 km resolution: A validation study. Remote Sensing of Environment, 263, 112554. https://doi.org/10.1016/j.rse.2021.112554
Beaudoing, H., & Rodell, M. (2020). GLDAS Noah Land Surface Model L4 3 hourly 0.25 × 0.25 degree V2.1. NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). https://doi.org/10.5067/E7TYRXPJKWOQ
Bircher, S., et al. (2012). A soil moisture and temperature network for SMOS validation in Western Denmark. Hydrology and Earth System Sciences, 16(5), 1445-1463. https://doi.org/10.5194/hess-16-1445-2012
Dorigo, W. A., et al. (2011). The International Soil Moisture Network: A data hosting facility for global in situ soil moisture measurements. Hydrology and Earth System Sciences, 15(5), 1675-1698. https://doi.org/10.5194/hess-15-1675-2011
Fan, D., et al. (2025). A Sentinel-1 SAR-based global 1-km resolution soil moisture data product: Algorithm and preliminary assessment. Remote Sensing of Environment, 318, 114579. https://doi.org/10.1016/j.rse.2024.114579
Hengl, T., et al. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE, 12(2), e0169748. https://doi.org/10.1371/journal.pone.0169748
Jensen, K. H., & Refsgaard, J. C. (2018). HOBE: The Danish hydrological observatory. Vadose Zone Journal, 17(1), 1-24. https://doi.org/10.2136/vzj2018.03.0059
Kerr, Y. H., et al. (2010). The SMOS mission: New tool for monitoring key elements of the global water cycle. Proceedings of the IEEE, 98(5), 666-687. https://doi.org/10.1109/JPROC.2010.2043032
Mengen, D., et al. (2023). High spatial and temporal soil moisture retrieval in agricultural areas using multi-orbit and vegetation adapted Sentinel-1 SAR time series. Remote Sensing, 15(9), 2282. https://doi.org/10.3390/rs15092282
Møller, A. B., et al. (2025). High-resolution 3D soil texture mapping in Denmark using satellite time series and bare soil composites. Advances in Agronomy, 194, 133-186. https://doi.org/10.1016/bs.agron.2024.11.002
Motarjemi, S. K., et al. (2023). Effects of different drainage conditions on nitrogen losses of an agricultural sandy loam soil. Journal of Environmental Management, 325, 116267. https://doi.org/10.1016/j.jenvman.2022.116267
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Note: All citations follow standard academic format with DOIs provided where available. URLs verified as of February 2026.

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Monday, February 16, 2026

Satellite Radar Technique Delivers Field-Scale Soil Moisture Maps for Precision Agriculture