Zappa et al. (2024) Benefits and pitfalls of irrigation timing and water amounts derived from satellite soil moisture

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Agricultural Water Management
• Year: 2024
• Authors: Luca Zappa, Jacopo Dari, Sara Modanesi, Raphael Quast, Luca Brocca, Gabriëlle De Lannoy, Christian Massari, Pere Quintana Seguí, Anaïs Barella-Ortiz, Wouter Dorigo
• DOI: 10.1016/j.agwat.2024.108773

Research Groups

The paper focuses on methodological assessment and comparison, implying collaboration between groups specializing in remote sensing, hydrological modeling, and water resource management, likely affiliated with institutions involved in developing and applying the SMDelta and SMInversion algorithms and managing the Ebro basin data. (Specific institutions are not named in the provided text.)

Short Summary

This study comprehensively assesses and inter-compares two satellite soil moisture-based irrigation retrieval methods (SMDelta and SMInversion) using Sentinel-1 data over the Ebro basin, demonstrating their reliability for estimating irrigation timing and water volumes at district and seasonal scales, despite limitations at the pixel level.

Objective

• To perform a comprehensive assessment and inter-comparison of the SMDelta and SMInversion algorithms, which estimate irrigation timing and water amounts using Sentinel-1 surface soil moisture data, focusing on their ability to discriminate irrigated fields and quantify agreement with reference irrigation data in the Ebro basin.

Study Configuration

• Spatial Scale: Ebro basin (83,000 km²), down to 1 km resolution (pixel scale), aggregated to district scale.
• Temporal Scale: 2017–2019 (3 years), aggregated to monthly, seasonal, and 15-day intervals.

Methodology and Data

• Models used: SMDelta approach, SMInversion approach.
• Data sources: Sentinel-1 surface soil moisture retrievals (1 km resolution), reference irrigation data (district-scale).

Main Results

• Both SMDelta and SMInversion methods were found unsuitable for mapping irrigated and rainfed fields, as they erroneously retrieved irrigation over rainfed pixels.
• When auxiliary information on irrigated fields was used, both methods showed satisfactory agreement with district-scale reference irrigation data.
  • SMDelta: Pearson R = 0.67; Bias = -4.99 mm/15 days.
  • SMInversion: Pearson R = 0.71; Bias = -4.75 mm/15 days.
• Aggregated estimates (in space or time) exhibited coherent temporal dynamics and spatial patterns, successfully capturing events like the delayed irrigation in 2018 due to wetter spring conditions.
• Limited consistency was observed between the two methods at the pixel scale, attributed to differing assumptions (e.g., constant vs. pixel-specific soil water capacity).
• The reliability of irrigation estimates increases significantly when shifting from small spatial and short temporal scales (pixel level, sub-weekly) to larger and longer scales (district level, seasonal).

Contributions

• Provides the first comprehensive, head-to-head assessment of the SMDelta and SMInversion irrigation retrieval algorithms using high-resolution Sentinel-1 data.
• Quantifies the scale-dependency of the reliability of satellite-derived irrigation estimates, demonstrating their utility primarily at the district/basin and seasonal scales rather than the pixel scale.
• Explores practical applications of the derived estimates, including attributing water volumes to specific irrigation systems and individual crops, offering valuable information for water resource management and hydrological modeling.

Funding

(Information not provided in the paper text.)

Citation

@article{Zappa2024Benefits,
  author = {Zappa, Luca and Dari, Jacopo and Modanesi, Sara and Quast, Raphael and Brocca, Luca and Lannoy, Gabriëlle De and Massari, Christian and Quintana‐Seguí, Pere and Barella-Ortiz, Anaïs and Dorigo, Wouter},
  title = {Benefits and pitfalls of irrigation timing and water amounts derived from satellite soil moisture},
  journal = {Agricultural Water Management},
  year = {2024},
  doi = {10.1016/j.agwat.2024.108773},
  url = {https://doi.org/10.1016/j.agwat.2024.108773}
}

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