Massari et al. (2021) A Review of Irrigation Information Retrievals from Space and Their Utility for Users

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

Identification

• Journal: Remote Sensing
• Year: 2021
• Authors: Christian Massari, Sara Modanesi, Jacopo Dari, Alexander Gruber, Gabriëlle De Lannoy, Manuela Girotto, Pere Quintana Seguí, Michel Le Page, Lionel Jarlan, Mehrez Zribi, Nadia Ouaadi, Mariëtte Vreugdenhil, Luca Zappa, Wouter Dorigo, Wolfgang Wagner, Joost Brombacher, H. Pelgrum, Pauline Jaquot, Vahid Freeman, Espen Volden, D.F. Prieto, Angelica Tarpanelli, Silvia Barbetta, Luca Brocca
• DOI: 10.3390/rs13204112

Research Groups

Earth Observation Scientists, Hydrologists, Agricultural Water Management Specialists.

Short Summary

This paper reviews existing Earth observation (EO) datasets, models, and algorithms used for mapping and quantifying irrigation water use, contrasting current monitoring capabilities with user requirements derived from a survey to identify shortcomings and propose guidelines for future satellite missions.

Objective

• Review and synthesize current Earth observation capabilities (datasets, models, algorithms) for irrigation mapping and water quantification across scales, and define future observation strategies based on identified user requirements and monitoring shortcomings.

Study Configuration

• Spatial Scale: Field to global scale.
• Temporal Scale: Not explicitly defined, but covers monitoring capacities relevant for agricultural water resources management (e.g., seasonal water use).

Methodology and Data

• Models used: Various existing models and algorithms used for irrigation water requirements and impact quantification (specific models are reviewed, not run by the authors).
• Data sources: Earth observation datasets (satellite), existing model outputs, and results from a survey conducted among users regarding requirements for satellite-observed irrigation data in agricultural water resources management.

Main Results

• A comprehensive review of existing Earth observation datasets and algorithms for irrigation mapping and water quantification was performed, highlighting the central role of irrigation as a major human intervention in the terrestrial water cycle.
• Current observation capacities were systematically confronted with the results of a survey detailing user requirements for satellite-observed irrigation data.
• Significant shortcomings in current irrigation monitoring capabilities from space were identified, particularly concerning the accurate quantification of water actually used for irrigation.
• Guidelines were formulated for potential future satellite missions and observation strategies to address the identified gaps in global irrigation monitoring.

Contributions

• Provides a systematic synthesis of current Earth observation capabilities for irrigation mapping and water use quantification across scales (field to global).
• Introduces novel insights by confronting technical monitoring capabilities with practical user requirements derived from a dedicated survey.
• Establishes clear, actionable guidelines for future satellite missions and observation strategies aimed at improving the accuracy and utility of irrigation monitoring data.

Funding

Not mentioned in the provided text.

Citation

@article{Massari2021Review,
  author = {Massari, Christian and Modanesi, Sara and Dari, Jacopo and Gruber, Alexander and Lannoy, Gabriëlle De and Girotto, Manuela and Quintana‐Seguí, Pere and Page, Michel Le and Jarlan, Lionel and Zribi, Mehrez and Ouaadi, Nadia and Vreugdenhil, Mariëtte and Zappa, Luca and Dorigo, Wouter and Wagner, Wolfgang and Brombacher, Joost and Pelgrum, H. and Jaquot, Pauline and Freeman, Vahid and Volden, Espen and Prieto, D.F. and Tarpanelli, Angelica and Barbetta, Silvia and Brocca, Luca},
  title = {A Review of Irrigation Information Retrievals from Space and Their Utility for Users},
  journal = {Remote Sensing},
  year = {2021},
  doi = {10.3390/rs13204112},
  url = {https://doi.org/10.3390/rs13204112}
}

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