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Remote Sensing for Irrigation and Water Management in Agriculture

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: closed (31 July 2023) | Viewed by 2234

Special Issue Editor


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Guest Editor
Institute of Crop Science and Resource Conservation (INRES), Universität Bonn, Bonn, Germany
Interests: agrohydrological modeling with focus on plant-water relations; agricultural water management; assimilation of remote sensing product into ecosystem models for agricultural digitalization; remote sensing as a tool for increasing crop and water producitivty in agriculure

Special Issue Information

Dear Colleagues,

Accurate information on water consumption by irrigating farms has long been a critical need in water resource management. With increasing heat and drought stresses in changing climate, adapting agriculture to these extreme events requires the accurate quantification of irrigation water consumption. The global population increases at the same time, and as a result, water resources come under more and more competition among multiple users and sectors, e.g., farm and city expansion. This urgently calls for the swift monitoring of key environmental parameters and the accurate prediction of their dynamics under multiple individual or compound stresses. However, such levels of understanding of system dynamics have been associated with many challenges, mostly related to the heterogeneity of the system which is complex and difficult to capture with only process-based models or data collection via field campaigns. Therefore, methods for water resource management should be adjusted and aligned with innovative theories, approaches, and technology for sustainable development. In recent decades, remote sensing technology has significantly contributed to opening new perspectives for agro-hydrological monitoring, water resources protection and planning, and irrigation water utilization owing to its fast detection capacity. It has provided information on wide spatial coverage using multiple spectral characteristics and indicators such as NDVI and NDWI.

Due to the importance of this topic, this Special Issue focuses specifically on innovative methods based on satellite and UAV drone products for hydrological and water resource planning and management. The issue is not limited to only remote sensing but also those related to methods related to the assimilation of remote sensing into inversion simulation models and pathways for sustainable crop and water production.

  • Irrigation and water resource management;
  • Monitoring agricultural water use using remote sensing data;
  • Remote sensing for groundwater irrigation water management;
  • Irrigation and crop water management;
  • Irrigation and sustainable production;
  • The contribution of remote sensing to precision agriculture;
  • Remote sensing and agricultural digitalization;
  • Data assimilation for agroecosystem modeling of irrigated systems;
  • Water management using UAV drones and satellites in agriculture.

Dr. Bahareh Kamali
Guest Editor

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.

Dr. Bahareh Kamali
Guest Editor

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

  • water resource management
  • agricultural water use
  • groundwater irrigation water management
  • irrigation and crop water management
  • irrigation and sustainable production
  • remote sensing for precision agriculture
  • agricultural digitalization

Published Papers (1 paper)

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Research

20 pages, 2539 KiB  
Article
Determination of Crop Coefficients and Evapotranspiration of Potato in a Semi-Arid Climate Using Canopy State Variables and Satellite-Based NDVI
by Alex Mukiibi, Angelinus Cornelius Franke and Joachim Martin Steyn
Remote Sens. 2023, 15(18), 4579; https://doi.org/10.3390/rs15184579 - 17 Sep 2023
Cited by 4 | Viewed by 1865
Abstract
Estimating crop coefficients and evapotranspiration (ET) accurately is crucial for optimizing irrigation. Remote sensing techniques using green canopy cover, leaf area index (LAI), and normalized difference vegetation index (NDVI) have been applied to estimate basal crop coefficients (Kcb) and ET for different crops. [...] Read more.
Estimating crop coefficients and evapotranspiration (ET) accurately is crucial for optimizing irrigation. Remote sensing techniques using green canopy cover, leaf area index (LAI), and normalized difference vegetation index (NDVI) have been applied to estimate basal crop coefficients (Kcb) and ET for different crops. However, analysis of the potential of these techniques to improve water management in irrigated potato (Solanum tuberosum L.) is still lacking. This study aimed to assess the modified nonlinear relationship between LAI, Kcb and NDVI in estimating crop coefficients (Kc) and ET of potato. Moreover, Kc and ET were derived from the measured fraction of green canopy cover (FGCC) and the FAO-56 approach. ET estimated from the FAO-56, FGCC and NDVI approaches were compared with the ET simulated using the LINTUL-Potato model. The results showed that the Kc values based on FGCC and NDVI were on average 0.16 lower than values based on FAO-56 Kc during the mid-season growing stage. ET estimated from FAO-56, FGCC and NDVI compared well with ET calculated by the LINTUL-Potato model, with RMSE values of 0.83, 0.79, and 0.78 mm day−1, respectively. These results indicate that dynamic crop coefficients and potato ET can be estimated from canopy cover and NDVI. The outcomes of this study will assist potato growers in determining crop water requirements using real-time ETo, canopy state variables and NDVI data from satellite images. Full article
(This article belongs to the Special Issue Remote Sensing for Irrigation and Water Management in Agriculture)
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