Chlorophyll <i>a</i> and Turbidity Distributions: Applicability of Using a Smartphone “App” Across Two Contrasting Bays

Gallagher, John Barry; Chuan, Chee Hoe

doi:10.2112/jcoastres-d-16-00221.1

Cited by 3 publications

(7 citation statements)

References 14 publications

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“…HydroColor and independent measurements in previous studies [21,22]. Our results of a good correspondence between average HydroColor and Aquafast measurements contrast with the findings of Malthus et al [24], who found no consistent relationship between HydroColor turbidity and gravimetric SPM assays.…”

Section: Discussioncontrasting

confidence: 91%

Participatory science methods to monitor water quality and ground truth remote sensing of the Chesapeake Bay

Neale,

Brown,

Sill

et al. 2024

Preprint

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Measurements by volunteer scientists using participatory science methods in combination with high resolution remote sensing can improve our ability to monitor water quality changes in highly vulnerable and economically valuable nearshore and estuarine habitats. In the Chesapeake Bay (USA), tidal tributaries are a focus of watershed and shoreline management efforts to improve water quality. The Chesapeake Water Watch program seeks to enhance the monitoring of tributaries by developing and testing methods for volunteer scientists to easily measure chlorophyll, turbidity, and colored dissolved organic matter (CDOM) to inform Bay stakeholders and improve algorithms for analogous remote sensing (RS) products. In the program, trained volunteers have measured surface turbidity using a smartphone app, HydroColor, calibrated with a photographer’s gray card. In vivo chlorophyll and CDOM fluorescence were assessed in surface samples with hand-held fluorometers (Aquafluor) located at sample processing “hubs” where volunteers drop-off samples for same day processing. In validation samples, HydroColor turbidity and Aquafluor in vivo chlorophyll and CDOM fluorescence were linearly related to standard analytical measures of turbidity, chlorophyll and CDOM, respectively, with R2values ranging from 0.65 to 0.85. These methods are being used for both repeat sampling at fixed sites of interest and ad-hoc “blitzes” to synoptically sample tributaries all around the Bay in coordination with satellite overpasses. All data is accessible on a public database (serc.fieldscope.org) and can be a resource to monitor long-term trends in the tidal tributaries as well as detect and diagnose causes of events of concern such as algal blooms and storm-induced reductions in water clarity.

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Section: Discussioncontrasting

confidence: 91%

Participatory science methods to monitor water quality and ground truth remote sensing of the Chesapeake Bay

Neale,

Brown,

Sill

et al. 2024

Preprint

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“…While dark current has been implicated in the literature as a major noise source [5,14,34,46,[56][57][58], the results presented in Sect. 4.4 indicate that it is actually typically quite minor. The RMS dark current in the iPhone SE and Galaxy S8 (<2 ADU s −1 ) is similar to values found in [5,51,56,58], while we found larger outliers, such as >300 pixels with >50 ADU s −1 in our Galaxy S8.…”

Section: Discussionmentioning

confidence: 99%

“…Their low cost makes them ideal for projects involving large scale deployment, autonomous monitoring, or citizen science. Successful scientific applications include environmental monitoring [1][2][3][4][5][6][7][8][9][10][11][12][13], cosmic ray detection [14], vegetation mapping [15][16][17][18][19], color science [9,[20][21][22][23][24][25], and biomedical applications [26][27][28][29][30][31][32][33]. However, the use of consumer cameras is made difficult by limited software controls and camera specifications.…”

Section: Introductionmentioning

confidence: 99%

“…These factors severely limit the possible data quality. Nevertheless, HydroColor has already seen significant adoption by the community, and future developments may reduce the aforementioned limitations [2][3][4]. Another recent application of smartphone cameras is bioluminescent-based analyte quantitation by smartphone (BAQS), a technique for the detection of bioluminescent bacteria.…”

Section: Introductionmentioning

confidence: 99%

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Standardized spectral and radiometric calibration of consumer cameras

Burggraaff¹,

Schmidt²,

Zamorano³

et al. 2019

Opt. Express

108

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Consumer cameras, particularly onboard smartphones and UAVs, are now commonly used as scientific instruments. However, their data processing pipelines are not optimized for quantitative radiometry and their calibration is more complex than that of scientific cameras. The lack of a standardized calibration methodology limits the interoperability between devices and, in the ever-changing market, ultimately the lifespan of projects using them. We present a standardized methodology and database (SPECTACLE) for spectral and radiometric calibrations of consumer cameras, including linearity, bias variations, read-out noise, dark current, ISO speed and gain, flat-field, and RGB spectral response. This includes golden standard ground-truth methods and do-it-yourself methods suitable for non-experts. Applying this methodology to seven popular cameras, we found high linearity in RAW but not JPEG data, inter-pixel gain variations >400% correlated with large-scale bias and read-out noise patterns, non-trivial ISO speed normalization functions, flat-field correction factors varying by up to 2.79 over the field of view, and both similarities and differences in spectral response. Moreover, these results differed wildly between camera models, highlighting the importance of standardization and a centralized database. accuracy of color measurements and their conversion to standard measures, such as the CIE 1931 XYZ and CIELAB color spaces, is limited by distortions in the observed colors [20] and differences in spectral response functions [21,[23][24][25].Extensive (spectro-)radiometric calibrations of consumer cameras are laborious and require specialized equipment and are thus not commonly performed [23,54,60]. A notable exception is the spectral and absolute radiometric calibration of a Raspberry Pi 3 V2 webcam by Pagnutti et al. [57], including calibrations of linearity, exposure stability, thermal and electronic noise, flat-field, and spectral response. Using this absolute radiometric calibration, digital values can be converted into SI units of radiance. However, the authors noted the need to characterize a large number of these cameras before the results could be applied in general. Moreover, certain calibrations are device-dependent and would need to be done separately on each device. Spectral and radiometric calibrations of seven cameras, including the Raspberry Pi, are given in [51]. These calibrations include dark current, flat-fielding, linearity, and spectral characterization. However, for the five digicams included in this work, JPEG data were used, severely limiting the quality and usefulness of these calibrations, as described above.Spectral characterizations are more commonly published since these are vital for quantitative color analysis. Using various methods, the spectral responses of several Canon [1,19,23,24,46,51,54,61], Nikon [1,13,23,24,54,[59][60][61][62], Olympus [23, 24, 51], Panasonic [46], SeaLife [13], Sigma [60], and Sony [1, 23, 46, 51, 61] digital cameras (digicams), as well as a number of smartpho...

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“…Some use additional fore-optics to measure hyperspectrally (Burggraaff et al, 2020;Stuart et al, 2021), while most use the camera as it is Leeuw and Boss, 2018;Gao et al, 2022). Smartphone science apps are also commonly used for educational purposes and in professional research (Gallagher and Chuan, 2018;Ayeni and Odume, 2020;Al-Ghifari et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Accuracy and Reproducibility of Above-Water Radiometry With Calibrated Smartphone Cameras Using RAW Data

Burggraaff

Werther²,

Boss³

et al. 2022

Front. Remote Sens.

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Consumer cameras, especially on smartphones, are popular and effective instruments for above-water radiometry. The remote sensing reflectance Rrs is measured above the water surface and used to estimate inherent optical properties and constituent concentrations. Two smartphone apps, HydroColor and EyeOnWater, are used worldwide by professional and citizen scientists alike. However, consumer camera data have problems with accuracy and reproducibility between cameras, with systematic differences of up to 40% in intercomparisons. These problems stem from the need, until recently, to use JPEG data. Lossless data, in the RAW format, and calibrations of the spectral and radiometric response of consumer cameras can now be used to significantly improve the data quality. Here, we apply these methods to above-water radiometry. The resulting accuracy in Rrs is around 10% in the red, green, and blue (RGB) bands and 2% in the RGB band ratios, similar to professional instruments and up to 9 times better than existing smartphone-based methods. Data from different smartphones are reproducible to within measurement uncertainties, which are on the percent level. The primary sources of uncertainty are environmental factors and sensor noise. We conclude that using RAW data, smartphones and other consumer cameras are complementary to professional instruments in terms of data quality. We offer practical recommendations for using consumer cameras in professional and citizen science.

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Chlorophyll a and Turbidity Distributions: Applicability of Using a Smartphone “App” Across Two Contrasting Bays

Cited by 3 publications

References 14 publications

Participatory science methods to monitor water quality and ground truth remote sensing of the Chesapeake Bay

Participatory science methods to monitor water quality and ground truth remote sensing of the Chesapeake Bay

Standardized spectral and radiometric calibration of consumer cameras

Accuracy and Reproducibility of Above-Water Radiometry With Calibrated Smartphone Cameras Using RAW Data

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