Irrigation Mapping Using Satellite Remote Sensing: 2nd Edition
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".
Deadline for manuscript submissions: 31 January 2025 | Viewed by 4747
Special Issue Editors
Interests: crop modeling; nitrogen and evapotranspiration in agriculture; scale issues and global scales; water and nitrogen management in agriculture; spectral analyses of land cover; water conservation and soil amendments
Interests: irrigation; plant physiology; GMO; water
Interests: drought monitoring using remote sensing data; precision agriculture; water and nitrogen management in agriculture; remote sensing and digital images analysis
Interests: irrigation engineering; water-food-energy nexus; water distribution systems; agricultural water management
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Special Issue Information
Dear Colleagues,
We are pleased to invite you for the Special Issue “Irrigation Mapping Using Satellite Remote Sensing II” – continuation of the earlier successful effort.
At the time of writing this summary (April 2023), vast lands across the world encounter severe drought, with some regions in Spain and the USA entering a critical water shortage stage following multi-annual drought events, prompting regional governments to reduce water allocations to 10–20% and completely restrict irrigation. If we assume a fairly constant but unevenly distributed amount of water at a global scale amid increasing water use by population and cultivation, irrigation becomes pivotal in all resource nexuses. Remote sensing at field and unmanned aerial systems (UASs) scales provide prospects for local/regional solutions to assist irrigation models, and evaluate and account for spatiotemporal biases and hence save water (e.g., deficit irrigation). However, satellite data ultimately have large/global outreach, often with open access, and can be fully automated. Remote sensing data and numerical crop modeling are also suggested to be used conjunctively to assess crop water status and groundwater return flows more efficiently and support regional water and irrigation systems management. Machine learning algorithms—neural networks, change detection, random forest—synergistically used with high-resolution remote sensing data also offer new approaches to estimate irrigation variables more accurately.
This Special Issue aims to contribute to studies using terrestrial remote sensing data and advanced methods as complementary tools to evaluate the hydrological cycle and improve or innovate irrigation and optimize water use. Our aim is to tackle, among others, the integration of multi-sensor and multi-scale data, especially satellite imagery, for irrigation variables, irrigation-relevant data noise assessment and reduction (mixed pixels, edge effects), detection of evaporative losses in agriculture at a satellite scale, advantages and risks of machine-learning-based decision support systems, ensembled quantification of irrigation demands, calculating regional and global water use efficiency, and especially constraints other than socioeconomic: coarser resolutions of satellite data, potential of deep-penetrating radar to assist satellite studies, diurnal crop water dynamics masking actual irrigation needs, etc. Novel research using very-high-resolution and -accuracy instruments, for instance nuclear magnetic resonance, to support the satellite-scale mapping of crop water and/or soil moisture are also welcomed in this Special Issue.
We therefore welcome original research articles and reviews from scientific and industry experts, including PhD students, for an excellent opportunity to publish original and novel findings on the topic. Research areas may include, but are not limited to, the following:
- Mapping of irrigated areas and evapotranspiration using optical, radar and other sensors at multiple spatiotemporal and spectral scales;
- Assimilation of satellite data and novel sensor data flows in irrigation/hydrologic models to monitor crop water consumption and use;
- Remote-sensing-assisted irrigation engineering and hydraulic management at local and regional scales;
- Computation methods and algorithms, including machine learning, to estimate crop drought/heat stress and irrigation need, and beta-version decision support systems in irrigation management;
- Drought perceptions and irrigation water allocation from a sociopolitical perspective based on existing and planned satellite monitoring programs.
We look forward to receiving your contributions.
Dr. Kiril Manevski
Prof. Mathias N. Andersen
Dr. Junxiang Peng
Prof. Dr. Juan Antonio Rodríguez Díaz
Guest Editors
Manuscript Submission Information
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Keywords
- irrigation scheduling
- deficit irrigation
- scale issue
- biophysical modeling
- artificial intelligence approach
- drought and heat stress
- transpiration
- evaporative loss in agriculture
- water allocation for irrigation
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Related Special Issue
- Irrigation Mapping Using Satellite Remote Sensing in Remote Sensing (9 articles)