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Remote Sensing
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Irrigation Mapping Using Satellite Remote Sensing II
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Irrigation Mapping Using Satellite Remote Sensing II

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 3105

Special Issue Editors

Dr. Kiril Manevski

E-Mail Website
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
Prof. Mathias N. Andersen

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

E-Mail Website
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
Prof. Dr. Juan Antonio Rodríguez Díaz

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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

Related Special Issue

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 303
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 II)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Study region and validation sampling distribution. Two tiles (training and validation) were selected for each National Pole of Irrigated Agriculture. Black points represent non-rice samples, while green points represent rice samples.</p> Full article ">Figure 2
<p>Rice spectral–temporal profile for each study region in Tocantins (<b>a</b>), Santa Catarina (<b>b</b>), and Rio Grande do Sul (<b>c</b>). The spectral indices are NDVI (green), NDMI (orange), and NDWI (blue).</p> Full article ">Figure 3
<p>An example of spectral–temporal metrics (STM) for Rio Grande do Sul (RS). The red line is the contour of the rice mapping by ANA-CONAB (2019/2020). The polar metrics Q1 (<b>a</b>), Q2 (<b>b</b>), Q3 (<b>c</b>), Q4 (<b>d</b>), and Eccentricity (<b>e</b>) provide less contrast. On the other hand, Gyration Radius (<b>f</b>) and the basic metrics, Max (<b>g</b>), Min (<b>h</b>), Mean (<b>i</b>), AMD (<b>j</b>), Standard Deviation (<b>k</b>), First Quartile (<b>l</b>), Second Quartile (<b>m</b>), Third Quartile (<b>n</b>), and Interquartile Range (<b>o</b>) provide more contrast.</p> Full article ">Figure 4
<p>Example of areas of training classified by the STM-based approach for the study regions compared to the official mapping for irrigated rice in Brazil. In black, the areas that are not rice are highlighted; in blue, areas of the ANA-CONAB mapping that were not classified by our approach are highlighted; in orange, areas classified as irrigated rice and not included in the official mapping are highlighted; in dark green, irrigated rice areas mapped by ANA-CONAB and our approach are highlighted. For Tocantins, the STM basic (<b>a</b>), STM Polar (<b>b</b>), STM Basic+Polar (<b>c</b>) and STM Global (<b>d</b>) classifications are shown. Similarly, Santa Catarina has STM basic (<b>e</b>), STM Polar (<b>f</b>), STM Basic+Polar (<b>g</b>) and STM Global (<b>h</b>) classifications. Finally, Rio Grande do Sul has classifications by STM basic (<b>i</b>), STM Polar (<b>j</b>), STM Basic+Polar (<b>k</b>) and STM Global (<b>l</b>) classifications.</p> Full article ">Figure 5
<p>Estimated rice-planted area for training region in Santa Catarina (<b>a</b>), Rio Grande do Sul (<b>b</b>) and Tocantins (<b>c</b>), and comparison between regional and global classifications and ANA-CONAB mapping.</p> Full article ">Figure 6
<p>Average confusion matrices for STM basic+polar classification in (<b>a</b>) Tocantins, (<b>b</b>) Santa Catarina, (<b>c</b>) Rio Grande do Sul, and (<b>d</b>) for global classification.</p> Full article ">Figure 7
<p>The most frequent important variables over the 100 iterations of the Monte Carlo simulation for the classification of basic+polar STMs classified by MeanDecreaseGini (<b>a</b>) and MeanDecreaseAccuracy (<b>b</b>).</p> Full article ">Figure 8
<p>Example of areas of validation classified by the STM-based approach for the study regions compared to the official mapping for irrigated rice in Brazil. In black, areas that are not rice are represented; in blue, areas of the ANA-CONAB mapping that were not classified by our approach are represented; in orange, areas classified as irrigated rice and not included in the official mapping are represented; in dark green, irrigated rice areas mapped by ANA-CONAB and our approach are represented. For Tocantins, the STM basic (<b>a</b>), STM Polar (<b>b</b>), STM Basic+Polar (<b>c</b>) and STM Global (<b>d</b>) classifications are shown. Similarly, Santa Catarina has STM basic (<b>e</b>), STM Polar (<b>f</b>), STM Basic+Polar (<b>g</b>) and STM Global (<b>h</b>) classifications. Finally, Rio Grande do Sul has classifications by STM basic (<b>i</b>), STM Polar (<b>j</b>), STM Basic+Polar (<b>k</b>) and STM Global (<b>l</b>) classifications.</p> Full article ">Figure 9
<p>Estimated rice-planted area for validation region in Santa Catarina (<b>a</b>), Rio Grande do Sul (<b>b</b>) and Tocantins (<b>c</b>), and comparison between regional and global classifications and ANA-CONAB mapping.</p> Full article ">Figure A1
<p>Validation pixel protocol for rice classes in Tocantins (validation samples #56 and #108). NDVI is light green, NDMI is dark green and NDWI is dark blue.</p> Full article ">Figure A2
<p>Validation pixel protocol for rice classes in Santa Catarina (validation samples #86 and #129). NDVI is light green, NDMI is dark green and NDWI is dark blue.</p> Full article ">Figure A3
<p>Validation pixel protocol for rice classes in Rio Grande do Sul (validation samples #56 and #120). NDVI is light green, NDMI is dark green and NDWI is dark blue.</p> Full article ">
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 866
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 II)
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Figure 1

Figure 1
<p>Map of Lebanon, showing the geographical position of the Upper Litani and the distribution of the fields grown with potatoes in the early and late seasons of 2020.</p> Full article ">Figure 2
<p>Workflow of NIW<sub>i</sub> estimation according to method (A), based on Maselli et al. [<a href="#B18-remotesensing-16-01598" class="html-bibr">18</a>].</p> Full article ">Figure 3
<p>(<b>a</b>) NDVI-STR space for the suggested substrate and leaf stomatal resistance regulation in the OPTRAM approach and (<b>b</b>) variation of the leaf resistance r<sub>leaf</sub> with the water index W, as taken into consideration in this study (literature reference in the text).</p> Full article ">Figure 4
<p>FVC trends for early- (<b>a</b>) and late-season (<b>b</b>) potato validation fields.</p> Full article ">Figure 5
<p>NDVI trends for early- (<b>a</b>) and late-season (<b>b</b>) potato validation fields.</p> Full article ">Figure 6
<p>Albedo trends for early- (<b>a</b>) and late-season (<b>b</b>) potato validation fields.</p> Full article ">Figure 7
<p>LAI trends for early- (<b>a</b>) and late-season (<b>b</b>) potato validation.</p> Full article ">Figure 8
<p>STR trends for early- (<b>a</b>) and late-season (<b>b</b>) potato validation fields.</p> Full article ">Figure 9
<p>Examples of Sentinel-2 FVC images at the beginning ((<b>a</b>)—20 April) and peak period ((<b>b</b>)—9 June) of the early season, and maps of NIW as predicted by the described procedure at the beginning ((<b>c</b>)—20 April) and peak period ((<b>d</b>)—9 June) of the early season of 2020.</p> Full article ">Figure 10
<p>Examples of Sentinel-2 FVC images at the beginning ((<b>a</b>)—3 August) and peak period ((<b>b</b>)—7 October) of the early season, and maps of NIW as predicted by the described procedure at the beginning ((<b>c</b>)—3 August) and peak period ((<b>d</b>)—7 October) of the early season of 2020.</p> Full article ">Figure 11
<p>FVC, ET<sub>a</sub>, and NIW trends for each early-season potato validation field.</p> Full article ">Figure 12
<p>Trends for the FVC, ET<sub>a</sub>, and NIW of each late-season potato validation field.</p> Full article ">Figure 13
<p>NDVI-STR domain for the Upper Litani watershed, extracted from the time series of 2020 Sentinel-2 images (<a href="#app2-remotesensing-16-01598" class="html-app">Appendix A</a>). Wet and dry edges are shown with dashed lines. The parameters of Equations (12) and (13) are indicated in the right corner.</p> Full article ">Figure 14
<p>Temporal series of Water index W for early- (<b>a</b>) and late-season (<b>b</b>) potato validation fields.</p> Full article ">Figure 15
<p>Temporal series of leaf resistance r<sub>leaf</sub> in the Peman–Monteith Sentinel-2 models modulated by using the OPTRAM approach satellite for early- (<b>a</b>) and late-season (<b>b</b>) potato validation fields.</p> Full article ">Figure 16
<p>NIW comparison between field data and method A (Maselli) and method B (D’Urso) for early-season validation fields.</p> Full article ">Figure 17
<p>NIW comparison between field data, method A (Maselli) and method B (D’Urso) for late-season validation fields.</p> Full article ">
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 1332
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 II)
►▼ Show Figures

Figure 1

Figure 1
<p>Map of the study area and the three irrigation districts considered in this study: Canals d’Urgell, Algerri-Balaguer, and Segria Sud. For each irrigation district, the distribution of the different irrigation types is shown in a pie chart. Data from SIGPAC.</p> Full article ">Figure 2
<p>Representation of the in situ data used for this study. (<b>A</b>) Spatial distribution of the water meters installed in Algerri-Balaguer and Canals d’Urgell (yellow crosses), together with the footprint of the canal systems and the meteo stations (red dots). (<b>B</b>–<b>D</b>) Weekly evolution of the irrigation amounts registered by the water meters in the districts for the years from 2016 to 2023 (2023 is depicted in green to underline the differences in water supply, while the other years from 2016 to 2022 are in grey).</p> Full article ">Figure 3
<p>Flowchart of the PrISM methodology representing the main steps. Numbers in each box correspond to the main operation required to estimate irrigation, as listed and explained in the text.</p> Full article ">Figure 4
<p>Visualization of the main step of PrISM (corresponding to numbers 3 to 6 of the methodology flowchart) needed to create a first guess of irrigation and a final estimation of the two scenarios. The visualization assumes daily irrigation, typical of modern irrigation systems such as sprinklers and drips.</p> Full article ">Figure 5
<p>Comparison of irrigation amounts produced by PrISM against in situ data from 2016 to 2023 for the area of the Canals d’Urgell district fed by the main canal. (<b>A</b>) shows time series of in situ and retrieved irrigation amounts expressed in mm/week, (<b>B</b>) shows the scatter plot and the linear correlation between these 2 time series, (<b>C</b>) shows the cumulative amounts in mm for each year, and (<b>D</b>) shows the area considered for this analysis.</p> Full article ">Figure 6
<p>Comparison of irrigation amounts produced by PrISM against in situ data from 2016 to 2023 for the area of the Canals d’Urgell district fed by the auxiliary canal. Plots are displayed as in <a href="#remotesensing-16-01116-f005" class="html-fig">Figure 5</a>.</p> Full article ">Figure 7
<p>Comparison of irrigation amounts produced by PrISM against in situ data from 2016 to 2023 for the district of Algerri-Balaguer. Plots are displayed as in <a href="#remotesensing-16-01116-f005" class="html-fig">Figure 5</a>.</p> Full article ">Figure 8
<p>Analysis for a single pixel located in Segria Sud. (<b>A</b>) shows the map of Segria Sud, depicting the percentage of irrigated area in each 1 km pixel. The pixel with the highest irrigation percentage is indicated by the arrow and selected to visualize the time series in panel (<b>B</b>), which shows the time series of observed SM (orange diamonds), PrISM SM (green line), in situ precipitation (blue line), and PrISM irrigation amounts (pink line) for the selected pixel.</p> Full article ">Figure 9
<p>Irrigation amounts produced by PrISM from 2016 to 2023 for the district of Segria-Sud, the upper panel shows the time series of irrigation amounts, the lower panel shows the cumulative annual irrigation amounts.</p> Full article ">
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