Escorihuela et al. (2016) Comparison of remote sensing and simulated soil moisture datasets in Mediterranean landscapes

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Identification

• Journal: Remote Sensing of Environment
• Year: 2016
• Authors: Maria‐José Escorihuela, Pere Quintana Seguí
• DOI: 10.1016/j.rse.2016.02.046

Research Groups

Not explicitly mentioned in the text.

Short Summary

This study compares three global satellite soil moisture products (ASCAT, AMSR, SMOS) against a Land Surface Model (LSM) over Mediterranean landscapes in the Northeast Iberian Peninsula, finding that product performance is highly dependent on the normalization method and land cover, with SMOS uniquely capable of detecting irrigation signals.

Objective

• To compare the temporal performance of three global soil moisture products (ASCAT, AMSR, and SMOS) against a Land Surface Model (LSM) across diverse Mediterranean land cover classes (dryland/irrigated crops, forests, natural vegetation) and topography.
• To assess the impact of different normalization methods (ZV35 and ZV) and temporal smoothing windows (1, 10, and 30 days) on the correlation results for regional agricultural and water management applications.

Study Configuration

• Spatial Scale: Regional and local scale; Northeast of the Iberian Peninsula, encompassing representative Mediterranean landscapes (dryland/irrigated crops, forests, grass-shrubs).
• Temporal Scale: Analysis of temporal series and seasonal effects, utilizing moving average windows of 1 day, 10 days, and 30 days.

Methodology and Data

• Models used: Land Surface Model (LSM) (Specific model name not provided).
• Data sources: Remote sensing satellite soil moisture products: ASCAT, AMSR-E LPRM, and SMOS (L2 Operational Algorithm V5.51). Comparison utilized two normalization methods (ZV35 and ZV) and three smoothing windows (1, 10, 30 days).

Main Results

• The comparison results are highly sensitive to the normalization method applied. The ZV35 method tends to eliminate monthly scale soil moisture memory, making it less sensitive to superficial soil moisture evolution.
• Without temporal smoothing, ASCAT exhibits the best correlation with the LSM across all land cover classes, regardless of the normalization method used.
• When a smoothing window is applied, AMSR-E tends to outperform other products when using the ZV normalization method.
• SMOS is the only remote sensing product capable of identifying and mapping heavily irrigated areas and does not show clear vegetation or roughness effects.
• SMOS shows limitations in areas close to the sea and regions with steep relief, and the V5.51 algorithm provides few values in forested areas.
• ASCAT shows an anomalous increase in soil moisture from June to October over agricultural and natural vegetation, hypothesized to be due to increased penetration depth under dry soil conditions.

Contributions

• Provided a detailed assessment of the sensitivity of global soil moisture product comparisons to different normalization techniques (ZV35 vs. ZV) and temporal smoothing windows.
• Delivered a comprehensive performance evaluation of ASCAT, AMSR, and SMOS specifically tailored to the complex, heterogeneous Mediterranean environment, considering land cover, topography, and proximity to the sea.
• Demonstrated the unique capability of SMOS among the tested products to detect and map irrigation signals, highlighting its potential for high-resolution water management applications.

Funding

Not explicitly mentioned in the text.

Citation

@article{Escorihuela2016Comparison,
  author = {Escorihuela, Maria‐José and Quintana‐Seguí, Pere},
  title = {Comparison of remote sensing and simulated soil moisture datasets in Mediterranean landscapes},
  journal = {Remote Sensing of Environment},
  year = {2016},
  doi = {10.1016/j.rse.2016.02.046},
  url = {https://doi.org/10.1016/j.rse.2016.02.046}
}

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