2021
DOI: 10.3390/drones5030084
|View full text |Cite|
|
Sign up to set email alerts
|

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

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 quant… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
45
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
6
2
1

Relationship

0
9

Authors

Journals

citations
Cited by 58 publications
(45 citation statements)
references
References 73 publications
0
45
0
Order By: Relevance
“…This systematic review is relevant to multiple research domains, including, but not limited to RS, geographic information science, computer science, data science, information science, geoscience, hydrology, and water resource management. This paper does not attempt to review the application of RS to water resources and hydrology more generally; for recent reviews of these topics, see [ 13 , 21 , 22 , 23 , 24 ]. A survey of DL applications in hydrology and water resources can be found in [ 25 ]; a survey of AI in the water domain can be found in [ 26 ]; and a survey of water quality applications using satellite data solely focused on ML can be found in [ 27 ].…”
Section: Audience and Scopementioning
confidence: 99%
“…This systematic review is relevant to multiple research domains, including, but not limited to RS, geographic information science, computer science, data science, information science, geoscience, hydrology, and water resource management. This paper does not attempt to review the application of RS to water resources and hydrology more generally; for recent reviews of these topics, see [ 13 , 21 , 22 , 23 , 24 ]. A survey of DL applications in hydrology and water resources can be found in [ 25 ]; a survey of AI in the water domain can be found in [ 26 ]; and a survey of water quality applications using satellite data solely focused on ML can be found in [ 27 ].…”
Section: Audience and Scopementioning
confidence: 99%
“…A harmful algal bloom (HAB) detection system HABNet (Hill et al, 2020) for coastal waters used a CNN combined with an LSTM, SVM, or RF applied on data from moderate resolution imaging spectroradiometer (MODIS) data. Remote sensing datasets relevant to river water quality are likely to grow in the future with newer high‐resolution satellites, instruments with hyperspectral bands, and increasing use of unmanned autonomous vehicles (UAV) to acquire water quality data (Sibanda et al, 2021; Topp et al, 2020), and can potentially address data gaps for water quality ML (Section 4.4).…”
Section: State‐of‐the‐art Machine Learning In River Water Quality Modelsmentioning
confidence: 99%
“…The work of Sibanda et al [197] shows a systematic review to assess the quality and quantity of water using UAVs. In Table 9, dissolved oxygen, turbidity, pH level, ammonia nitrogen, nitrate, water temperature, chlorophyll-a, redox potential, phytoplankton counts, salinity, colored dissolved organic matter (CDOM), fluorescent dye, and electrical conductivity were among the collected parameters for water monitoring.…”
Section: Water Quality and Pollutants Detection And Assessmentmentioning
confidence: 99%