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Remote Sensing
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Irrigation Estimates and Management from EO Data
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Irrigation Estimates and Management from EO Data

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 10458

Special Issue Editors

Dr. Jacopo Dari

E-Mail Website
Guest Editor
1. Department of Civil and Environmental Engineering, University of Perugia, via G. Duranti 93, 06125 Perugia, Italy
2. National Research Council, Research Institute for Geo-Hydrological Protection, via Madonna Alta 126, 06128 Perugia, Italy
Interests: remote sensing; soil moisture; irrigation; hydrological and land surface modeling; evapotranspiration; water resource management
Special Issues, Collections and Topics in MDPI journals
Dr. Sara Modanesi

E-Mail Website
Guest Editor
National Research Council, Research Institute for Geo-Hydrological Protection, via Madonna Alta 126, 06128 Perugia, Italy
Interests: remote sensing; irrigation; land surface modeling; hydrological modeling; data assimilation; water resources management; drought monitoring
Dr. Christian Massari

E-Mail Website
Guest Editor
National Research Council, Research Institute for Geo-Hydrological Protection, via Madonna Alta 126, 06128 Perugia, Italy
Interests: hydrological and land surface water balance modelling; development of land data assimilation systems to ingest remote sensing retrievals; droughts and floods; ecohydrology
Dr. Luca Brocca

grade E-Mail Website
Guest Editor
Research Institute for Geo-Hydrological Protection, National Research Council, Via della Madonna Alta, 126, 06128 Perugia, Italy
Interests: soil moisture; flood; drought; irrigation; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Julian Koch

E-Mail Website
Guest Editor
Department of Hydrology, Geological Survey of Denmark and Greenland, 1350 Copenhagen, Denmark
Interests: hydrological modelling; remote sensing; machine learning; evapotranspiration
Special Issues, Collections and Topics in MDPI journals
Dr. Manuela Girotto

E-Mail Website
Guest Editor
Department of Environmental Science, Policy & Management of Berkeley University of California, Berkeley, CA, USA
Interests: hydrologic response; natural and human driven processes; remote sensing; sea level change; snow hydrology

Special Issue Information

Dear Colleagues,

Irrigation is currently the largest freshwater consumer over anthropized basins and one of the most important factors of disturbance in the natural hydrological cycle. Remote sensing has proven to be an essential tool for monitoring irrigation dynamics, as well as reliable support for management strategies. Hence, the development of remote-sensing-based algorithms and innovative techniques aimed at detecting and estimating irrigation is needed to face the further stress on the water resource foreseen in the upcoming years due to population growth, the rising living standards, and the global warming scenarios.

This Special Issue welcomes novel studies aimed at monitoring irrigation dynamics at different spatial scales through Earth Observation (EO) data, as well as works proposing managing strategies based on remote sensing observations. Review papers are also welcome. Topics of interest include, but are not limited to:

  • Irrigation detection (timing and/or mapping) through remotely sensed data;
  • irrigation estimates from satellite products;
  • monitoring the irrigation dynamics at different spatial scales;
  • innovative data assimilation systems to merge EO and land surface models to improve irrigation quantification/detection and the estimation of essential climatic variables;
  • coupling remote sensing observations with hydrological modeling for irrigation management purposes;
  • assessing irrigation efficiency through remotely sensed estimates of hydrological variables;
  • assessing the impacts of irrigation practices on the water cycle over anthropized basins;
  • prediction of irrigation requirements in future climate scenarios.

Dr. Jacopo Dari
Dr. Sara Modanesi
Dr. Christian Massari
Dr. Luca Brocca
Dr. Julian Koch
Dr. Manuela Girotto
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.

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Published Papers (3 papers)

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Research

25 pages, 4062 KiB  
Article
Irrigation Timing Retrieval at the Plot Scale Using Surface Soil Moisture Derived from Sentinel Time Series in Europe
by Michel Le Page, Thang Nguyen, Mehrez Zribi, Aaron Boone, Jacopo Dari, Sara Modanesi, Luca Zappa, Nadia Ouaadi and Lionel Jarlan
Remote Sens. 2023, 15(5), 1449; https://doi.org/10.3390/rs15051449 - 4 Mar 2023
Cited by 8 | Viewed by 2813
Abstract
The difficulty of calculating the daily water budget of irrigated fields is often due to the uncertainty surrounding irrigation amounts and timing. The automated detection of irrigation events has the potential to greatly simplify this process, and the combination of high-resolution SAR (Sentinel-1) [...] Read more.
The difficulty of calculating the daily water budget of irrigated fields is often due to the uncertainty surrounding irrigation amounts and timing. The automated detection of irrigation events has the potential to greatly simplify this process, and the combination of high-resolution SAR (Sentinel-1) and optical satellite observations (Sentinel-2) makes the detection of irrigation events feasible through the use of a surface soil moisture (SSM) product. The motivation behind this study is to utilize a large irrigation dataset (collected during the ESA Irrigation + project over five sites in three countries over three years) to analyze the performance of an established algorithm and to test potential improvements. The study’s main findings are (1) the scores decrease with SSM observation frequency; (2) scores decrease as irrigation frequency increases, which was supported by better scores in France (more sprinkler irrigation) than in Germany (more localized irrigation); (3) replacing the original SSM model with the force-restore model resulted in an improvement of about 6% in the F-score and narrowed the error on cumulative seasonal irrigation; (4) the Sentinel-1 configuration (incidence angle, trajectory) did not show a significant impact on the retrieval of irrigation, which supposes that the SSM is not affected by these changes. Other aspects did not allow a definitive conclusion on the irrigation retrieval algorithm: (1) the lower scores obtained with small NDVI compared to large NDVI were counter-intuitive but may have been due to the larger number of irrigation events during high vegetation periods; (2) merging different runs and interpolating all SSM data for one run produced comparable F-scores, but the estimated cumulative sum of irrigation was around −20% lower compared to the reference dataset in the best cases. Full article
(This article belongs to the Special Issue Irrigation Estimates and Management from EO Data)
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21 pages, 7485 KiB  
Article
Estimation of Water Use in Center Pivot Irrigation Using Evapotranspiration Time Series Derived by Landsat: A Study Case in a Southeastern Region of the Brazilian Savanna
by Marionei Fomaca de Sousa Junior, Leila Maria Garcia Fonseca and Hugo do Nascimento Bendini
Remote Sens. 2022, 14(23), 5929; https://doi.org/10.3390/rs14235929 - 23 Nov 2022
Cited by 1 | Viewed by 2420
Abstract
In Brazil, irrigated agriculture is responsible for 46% of withdrawals of water bodies and 67% of use concerning the total water abstracted volume, representing the most significant consumptive use in the country. Understanding how different crops use water over time is essential for [...] Read more.
In Brazil, irrigated agriculture is responsible for 46% of withdrawals of water bodies and 67% of use concerning the total water abstracted volume, representing the most significant consumptive use in the country. Understanding how different crops use water over time is essential for planning and managing water allocation, water rights, and farming production. In this work, we propose a methodology to estimate water used in agriculture irrigated by center pivots in the municipality of Itobi, São Paulo, in the Brazilian Savanna (known as Cerrado), which has strong potential for agricultural and livestock production. The methodology proposed for the water use estimate is based on mapping crops irrigated by center pivots for the 2015/2016 crop year and actual evapotranspiration (ETa). ETa is derived from the Operational Simplified Surface Energy Balance model (SSEBop) and parameterized for edaphoclimatic conditions in Brazil (SSEBop-Br). Three meteorological data sources (INMET, GLDAS, CFSv2) were tested for estimating ETa. The water use was estimated for each meteorological data source, relating the average irrigation balance and the total area for each crop identified in the map. We evaluated the models for each crop present in the center pivots through global accuracy and f1-score metrics, and f1-score was more significant than 0.9 for all crops. The potato was the crop that consumed the most water in irrigation, followed by soy crops, beans, carrots, and onions, considering the three meteorological data sources. The total water volume consumed by center pivots in the municipality of Itobi in the 2015/2016 agricultural year for each meteorological data source was 3.2 million m3 (INMET), 2.5 million m3; (GLDAS), and 1.8 million m3 (CFSv2). Full article
(This article belongs to the Special Issue Irrigation Estimates and Management from EO Data)
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22 pages, 6575 KiB  
Article
AgSAT: A Smart Irrigation Application for Field-Scale Daily Crop ET and Water Requirements Using Satellite Imagery
by Hadi Jaafar, Roya Mourad, Rim Hazimeh and Lara Sujud
Remote Sens. 2022, 14(20), 5090; https://doi.org/10.3390/rs14205090 - 12 Oct 2022
Cited by 6 | Viewed by 3622
Abstract
With the foreseen increase in population and the reliance on water as a key input for agricultural production, greater demand will be placed on freshwater supplies. The objective of this work was to present the newly developed Android smartphone application to calculate crop [...] Read more.
With the foreseen increase in population and the reliance on water as a key input for agricultural production, greater demand will be placed on freshwater supplies. The objective of this work was to present the newly developed Android smartphone application to calculate crop evapotranspiration in real-time to support field-scale irrigation management. As part of the answer to water shortage, we embraced technology by developing AgSAT, a Google Earth Engine-based application that optimizes water use for food production. AgSAT uses meteorological data to calculate daily water requirements using the ASCE-Penman–Monteith method (ETref) and vegetation indices from satellite imagery to derive the basal crop growth coefficient, Kcb. The performance of AgSAT to estimate ETref was assessed using climatic data from 18 meteorological stations distributed over several climatic zones worldwide. ETref estimation through the app showed acceptable results with values of 1.27, 0.9, 0.79, 0.95, and 0.5 for root mean square error (RMSE), correlation coefficient (r), modeling efficiency (NSE), concordance index (d), and percentage bias (Pbias), respectively. AgSAT guides gross irrigation requirements for crops and rationalizes water quantities used in agricultural production. AgSAT has been released, is currently in use by research scientists, agricultural producers, and irrigation managers, and is freely accessible from the Google Play and IOS Store and also at agsat.app. Our work is geared towards the development of remote sensing-based technologies that transfer significant benefits to farmers and water-saving efforts. Full article
(This article belongs to the Special Issue Irrigation Estimates and Management from EO Data)
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