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Measurement of Water Leaving Reflectance Using a Digital Camera Based on Multiple Reflectance Reference Cards
With the development of citizen science, digital cameras and smartphones are increasingly utilized in water quality monitoring. The smartphone application HydroColor quantitatively retrieves water quality parameters from digital images. HydroColor assumes a linear relationship between the digital pi...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7698626/ https://www.ncbi.nlm.nih.gov/pubmed/33217939 http://dx.doi.org/10.3390/s20226580 |
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author | Gao, Min Li, Junsheng Zhang, Fangfang Wang, Shenglei Xie, Ya Yin, Ziyao Zhang, Bing |
author_facet | Gao, Min Li, Junsheng Zhang, Fangfang Wang, Shenglei Xie, Ya Yin, Ziyao Zhang, Bing |
author_sort | Gao, Min |
collection | PubMed |
description | With the development of citizen science, digital cameras and smartphones are increasingly utilized in water quality monitoring. The smartphone application HydroColor quantitatively retrieves water quality parameters from digital images. HydroColor assumes a linear relationship between the digital pixel number (DN) and incident radiance and applies a grey reference card to derive water leaving reflectance. However, image DNs change with incident light brightness non-linearly, according to a power function. We developed an improved method for observing and calculating water leaving reflectance from digital images based on multiple reflectance reference cards. The method was applied to acquire water, sky, and reflectance reference card images using a Cannon 50D digital camera at 31 sampling stations; the results were validated using synchronously measured water leaving reflectance using a field spectrometer. The R(2) for the red, green, and blue color bands were 0.94, 0.95, 0.94, and the mean relative errors were 27.6%, 29.8%, 31.8%, respectively. The validation results confirm that this method can derive accurate water leaving reflectance, especially when compared with the results derived by HydroColor, which systematically overestimates water leaving reflectance. Our results provide a more accurate theoretical foundation for quantitative water quality monitoring using digital and smartphone cameras. |
format | Online Article Text |
id | pubmed-7698626 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-76986262020-11-29 Measurement of Water Leaving Reflectance Using a Digital Camera Based on Multiple Reflectance Reference Cards Gao, Min Li, Junsheng Zhang, Fangfang Wang, Shenglei Xie, Ya Yin, Ziyao Zhang, Bing Sensors (Basel) Article With the development of citizen science, digital cameras and smartphones are increasingly utilized in water quality monitoring. The smartphone application HydroColor quantitatively retrieves water quality parameters from digital images. HydroColor assumes a linear relationship between the digital pixel number (DN) and incident radiance and applies a grey reference card to derive water leaving reflectance. However, image DNs change with incident light brightness non-linearly, according to a power function. We developed an improved method for observing and calculating water leaving reflectance from digital images based on multiple reflectance reference cards. The method was applied to acquire water, sky, and reflectance reference card images using a Cannon 50D digital camera at 31 sampling stations; the results were validated using synchronously measured water leaving reflectance using a field spectrometer. The R(2) for the red, green, and blue color bands were 0.94, 0.95, 0.94, and the mean relative errors were 27.6%, 29.8%, 31.8%, respectively. The validation results confirm that this method can derive accurate water leaving reflectance, especially when compared with the results derived by HydroColor, which systematically overestimates water leaving reflectance. Our results provide a more accurate theoretical foundation for quantitative water quality monitoring using digital and smartphone cameras. MDPI 2020-11-18 /pmc/articles/PMC7698626/ /pubmed/33217939 http://dx.doi.org/10.3390/s20226580 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gao, Min Li, Junsheng Zhang, Fangfang Wang, Shenglei Xie, Ya Yin, Ziyao Zhang, Bing Measurement of Water Leaving Reflectance Using a Digital Camera Based on Multiple Reflectance Reference Cards |
title | Measurement of Water Leaving Reflectance Using a Digital Camera Based on Multiple Reflectance Reference Cards |
title_full | Measurement of Water Leaving Reflectance Using a Digital Camera Based on Multiple Reflectance Reference Cards |
title_fullStr | Measurement of Water Leaving Reflectance Using a Digital Camera Based on Multiple Reflectance Reference Cards |
title_full_unstemmed | Measurement of Water Leaving Reflectance Using a Digital Camera Based on Multiple Reflectance Reference Cards |
title_short | Measurement of Water Leaving Reflectance Using a Digital Camera Based on Multiple Reflectance Reference Cards |
title_sort | measurement of water leaving reflectance using a digital camera based on multiple reflectance reference cards |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7698626/ https://www.ncbi.nlm.nih.gov/pubmed/33217939 http://dx.doi.org/10.3390/s20226580 |
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