Evidence map›Paper›PMID 38367097›Full record

ReviewEnvironmental monitoring and assessment2024

UAV and satellite remote sensing for inland water quality assessments: a literature review.

Eden T Wasehun, Leila Hashemi Beni, Courtney A Di Vittorio

Abstract readReview
PubMed Publisher
In one paragraph

Review in Environmental monitoring and assessment, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
11.9field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 59 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Eden T WasehunApplied Science and Technology, North Carolina A &T State University, 1601 E Market St, Greensboro, NC, 27411, USA.
Leila Hashemi BeniDepartment of Build Environment, North Carolina A &T State University, 1601 E Market St, Greensboro, NC, 27411, USA. lhashemibeni@ncat.edu.
Courtney A Di VittorioDepartment of Engineering, Wake Forest University, 1834 Wake Forest Rd, Winston-Salem, NC, 27109, USA.
North Carolina Agricultural and Technical State University · USWake Forest University · US

Funding

NOAA NA21OAR4590358
6 · The paper itself

Abstract

High spatial and temporal resolution data is crucial to comprehend the dynamics of water quality fully, support informed decision-making, and allow efficient management and protection of water resources. Traditional in situ water quality measurement techniques are both time-consuming and labor-intensive, resulting in databases with limited spatial and temporal frequency. To address these challenges, satellite-driven water quality assessment has emerged as an efficient and effective solution, offering comprehensive data on larger-scale water bodies. Numerous studies have utilized multispectral and hyperspectral remote sensing data from various sensors to assess water quality, yielding promising results. However, the recent popularity of unmanned aerial vehicle (UAV) remote sensing can be attributed to its high spatial and temporal resolution, flexibility, ability to capture data at different times of day, and relatively low cost compared to traditional platforms. This study presents a comprehensive review of the current state of the art in monitoring water quality in small inland water bodies using satellite and UAV remote sensing data. It encompasses an overview of atmospheric correction algorithms and the assessment of different water quality parameters. Furthermore, the review addresses the challenges associated with monitoring water quality in these bodies of water and emphasizes the potential of UAVs to overcome these challenges by providing accurate and reliable data.

Indexed as

Remote Sensing TechnologyWater QualityAlgorithmsEnvironmental MonitoringUnmanned Aerial DevicesHigh resolutionHyperspectralMultispectralSatellite imagesUAVWater quality

Identifiers

PMID38367097
OpenAlexW4391899027

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.