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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


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Guest Editor
Department of Agroecology, Aarhus University, 8830 Tjele, Denmark
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

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Guest Editor
Department of Agroecology, Aarhus University, 8830 Tjele, Denmark
Interests: irrigation; plant physiology; GMO; water

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Guest Editor
Department of Crop Production Ecology, Swedish University of Agricultural Sciences, 90183 Umeå, Sweden
Interests: drought monitoring using remote sensing data; precision agriculture; water and nitrogen management in agriculture; remote sensing and digital images analysis

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Guest Editor
Department of Agronomy, University of Córdoba, Campus de Rabanales, 14071 Córdoba, Spain
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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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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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Published Papers (3 papers)

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Research

23 pages, 19658 KiB  
Article
Mapping Irrigated Rice in Brazil Using Sentinel-2 Spectral–Temporal Metrics and Random Forest Algorithm
by Alexandre S. Fernandes Filho, Leila M. G. Fonseca and Hugo do N. Bendini
Remote Sens. 2024, 16(16), 2900; https://doi.org/10.3390/rs16162900 - 8 Aug 2024
Viewed by 1019
Abstract
Brazil, a leading rice producer globally, faces challenges in systematically mapping its diverse rice fields due to varying cropping systems, climates, and planting calendars. Existing rice mapping methods often rely on complex techniques like deep learning or microwave imagery, posing limitations for large-scale [...] Read more.
Brazil, a leading rice producer globally, faces challenges in systematically mapping its diverse rice fields due to varying cropping systems, climates, and planting calendars. Existing rice mapping methods often rely on complex techniques like deep learning or microwave imagery, posing limitations for large-scale mapping. This study proposes a novel approach utilizing Sentinel-2 spectral–temporal metrics (STMs) in conjunction with a random forest classifier for rice paddy mapping. By extracting diverse STMs and training both regional and global classifiers, we validated the method across independent areas. While regional models tended to overestimate rice areas, the global model effectively reduced discrepancies between our data and the reference maps, achieving an overall classifier accuracy exceeding 80%. Despite the need for further refinement to address confusion with other crops, STM exhibits promise for national-scale rice paddy mapping in Brazil. Full article
(This article belongs to the Special Issue Irrigation Mapping Using Satellite Remote Sensing: 2nd Edition)
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28 pages, 6586 KiB  
Article
Comparative Analysis of Earth Observation Methodologies for Irrigation Water Accounting in the Bekaa Valley of Lebanon
by Gabriel Moujabber, Marie Therese Abi Saab, Salim Roukoz, Daniela D’Agostino, Oscar Rosario Belfiore and Guido D’Urso
Remote Sens. 2024, 16(9), 1598; https://doi.org/10.3390/rs16091598 - 30 Apr 2024
Viewed by 1087
Abstract
This study extensively examines the estimation of irrigation water requirements using different methodologies based on Earth Observation data. Specifically, two distinct methods inspired by recent remote sensing and satellite technology developments are examined and compared. The first methodology, as outlined by Maselli et [...] Read more.
This study extensively examines the estimation of irrigation water requirements using different methodologies based on Earth Observation data. Specifically, two distinct methods inspired by recent remote sensing and satellite technology developments are examined and compared. The first methodology, as outlined by Maselli et al. (2020), focuses on using Sentinel-2 MSI data and a water stress scalar to estimate the levels of actual evapotranspiration and net irrigation water (NIW). The second methodology derives from the work of D’Urso et al. (2021), which includes the application of the Penman–Monteith equation in conjunction with Sentinel-2 data for estimating key parameters, such as crop evapotranspiration and NIW. In the context of the Bekaa Valley in Lebanon, this study explores the suitability of both methodologies for irrigated potato crops (nine potato fields for the early season and eight for the late season). The obtained NIW value was compared with measured field data, and the root mean square errors were calculated. The results of the comparison showed that the effectiveness of these methods varies depending on the growing season. Notably, the Maselli method exhibited better performance during the late season, while the D’Urso method proved more accurate during the early season. This comparative assessment provided valuable insights for effective agricultural water management in the Bekaa Valley when estimating NIW in potato cultivation. Full article
(This article belongs to the Special Issue Irrigation Mapping Using Satellite Remote Sensing: 2nd Edition)
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16 pages, 2686 KiB  
Article
PrISM at Operational Scale: Monitoring Irrigation District Water Use during Droughts
by Giovanni Paolini, Maria Jose Escorihuela, Joaquim Bellvert, Olivier Merlin and Thierry Pellarin
Remote Sens. 2024, 16(7), 1116; https://doi.org/10.3390/rs16071116 - 22 Mar 2024
Cited by 1 | Viewed by 1951
Abstract
Efficient water management strategies are of utmost importance in drought-prone regions, given the fundamental role irrigation plays in avoiding yield losses and food shortages. Traditional methodologies for estimating irrigation amounts face limitations in terms of overall precision and operational scalability. This study proposes [...] Read more.
Efficient water management strategies are of utmost importance in drought-prone regions, given the fundamental role irrigation plays in avoiding yield losses and food shortages. Traditional methodologies for estimating irrigation amounts face limitations in terms of overall precision and operational scalability. This study proposes to estimate irrigation amounts from soil moisture (SM) data by adapting the PrISM (Precipitation Inferred from Soil Moisture) methodology. The PrISM assimilates SM into a simple Antecedent Precipitation Index (API) model using a particle filter approach, which allows the creation and estimation of irrigation events. The methodology is applied in a semi-arid region in the Ebro basin, located in the north-east of Spain (Catalonia), from 2016 to 2023. Multi-year drought, which started in 2020, particularly affected the region starting from the spring of 2023, which led to significant reductions in irrigation district water allocations in some of the areas of the region. This study demonstrates that the PrISM approach can correctly identify areas where water restrictions were adopted in 2023, and monitor the water usage with good performances and reliable results. When compared with in situ data for 8 consecutive years, PrISM showed a significant person’s correlation between 0.58 and 0.76 and a cumulative weekly root mean squared error (rmse) between 7 and 11 mm. Additionally, PrISM was applied to three irrigation districts with different levels of modernization, due to the different predominant irrigation systems: flood, sprinkler, and drip. This analysis underlined the strengths and limitations of PrISM depending on the irrigation techniques monitored. PrISM has good performances in areas irrigated by sprinkler and flood systems, while difficulties are present over drip irrigated areas, where the very localized and limited irrigation amounts could not be detected from SM observations. Full article
(This article belongs to the Special Issue Irrigation Mapping Using Satellite Remote Sensing: 2nd Edition)
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