Application of Drone Technologies in Surface Water Resources Monitoring and Assessment: A Systematic Review of Progress, Challenges, and Opportunities in the Global South

  1. Sibanda, Mbulisi
  2. Mutanga, Onisimo
  3. Chimonyo, Vimbayi G. P.
  4. Clulow, Alistair D.
  5. Shoko, Cletah
  6. Mazvimavi, Dominic
  7. Dube, Timothy
  8. Mabhaudhi, Tafadzwanashe
  9. González Aguilera, Diego ed. lit. 1
  1. 1 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

Journal:
Drones

ISSN: 2504-446X

Year of publication: 2021

Volume: 5

Issue: 3

Pages: 84

Type: Article

DOI: 10.3390/DRONES5030084 GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Drones

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Metrics

Cited by

  • Web of Science Cited by: 21 (09-09-2023)
  • Dimensions Cited by: 16 (30-03-2023)

JCR (Journal Impact Factor)

  • Year 2021
  • Journal Impact Factor: 5.532
  • Journal Impact Factor without self cites: 4.848
  • Article influence score: 0.89
  • Best Quartile: Q2
  • Area: REMOTE SENSING Quartile: Q2 Rank in area: 10/34 (Ranking edition: SCIE)

SCImago Journal Rank

  • Year 2021
  • SJR Journal Impact: 0.995
  • Best Quartile: Q1
  • Area: Information Systems Quartile: Q1 Rank in area: 82/380
  • Area: Computer Science Applications Quartile: Q1 Rank in area: 171/791
  • Area: Aerospace Engineering Quartile: Q1 Rank in area: 21/140
  • Area: Control and Systems Engineering Quartile: Q1 Rank in area: 66/281
  • Area: Artificial Intelligence Quartile: Q2 Rank in area: 79/286

Scopus CiteScore

  • Year 2021
  • CiteScore of the Journal : 7.2
  • Area: Aerospace Engineering Percentile: 91
  • Area: Computer Science Applications Percentile: 83
  • Area: Information Systems Percentile: 83
  • Area: Control and Systems Engineering Percentile: 82
  • Area: Artificial Intelligence Percentile: 76

Journal Citation Indicator (JCI)

  • Year 2021
  • Journal Citation Indicator (JCI): 0.94
  • Best Quartile: Q2
  • Area: REMOTE SENSING Quartile: Q2 Rank in area: 18/57

Dimensions

(Data updated as of 30-03-2023)
  • Total citations: 16
  • Recent citations: 16

Abstract

Accurate and timely information on surface water quality and quantity is critical for various applications, including irrigation agriculture. In-field water quality and quantity data from unmanned aerial vehicle systems (UAVs) could be useful in closing spatial data gaps through the generation of near-real-time, fine resolution, spatially explicit information required for water resources accounting. This study assessed the progress, opportunities, and challenges in mapping and modelling water quality and quantity using data from UAVs. To achieve this research objective, a systematic review was adopted. The results show modest progress in the utility of UAVs, especially in the global south. This could be attributed, in part, to high costs, a lack of relevant skills, and the regulations associated with drone procurement and operational costs. The progress is further compounded by a general lack of research focusing on UAV application in water resources monitoring and assessment. More importantly, the lack of robust and reliable water quantity and quality data needed to parameterise models remains challenging. However, there are opportunities to advance scientific inquiry for water quality and quantity accounting by integrating UAV data and machine learning.

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