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  • Journal article

    Complementary water quality observations from high and medium resolution Sentinel sensors by aligning chlorophyll-a and turbidity algorithms

    M. Warren, S. Simis, N. Selmes (2021). Complementary water quality observations from high and medium resolution Sentinel sensors by aligning chlorophyll-a and turbidity algorithms. Remote Sensing of Environment, Volume: 265, http://doi.org/10.1016/j.rse.2021.112651
    Abstract

    High resolution imaging spectrometers are prerequisite to address significant data gaps in inland optical water quality monitoring. In this work, we provide a data-driven alignment of chlorophyll-a and turbidity derived from the Sentinel-2 MultiSpectral Imager (MSI) with corresponding Sentinel-3 Ocean and Land Colour Instrument (OLCI) products. For chlorophyll-a retrieval, empirical ‘ocean colour’ blue-green band ratios and a near infra-red (NIR) band ratio algorithm, as well as a semi-analytical three-band NIR-red ratio algorithm, were included in the analysis. Six million co-registrations with MSI and OLCI spanning 24 lakes across five continents were analysed. Following atmospheric correction with POLYMER, the reflectance distributions of the red and NIR bands showed close similarity between the two sensors, whereas the distribution for blue and green bands was positively skewed in the MSI results compared to OLCI. Whilst it is not possible from this analysis to determine the accuracy of reflectance retrieved with either MSI or OLCI results, optimizing water quality algorithms for MSI against those previously derived for the Envisat Medium Resolution Imaging Spectrometer (MERIS) and its follow-on OLCI, supports the wider use of MSI for aquatic applications. Chlorophyll-a algorithms were thus tuned for MSI against concurrent OLCI observations, resulting in significant improvements against the original algorithm coefficients. The mean absolute difference (MAD) for the blue-green band ratio algorithm decreased from 1.95 mg m−3 to 1.11 mg m−3, whilst the correlation coefficient increased from 0.61 to 0.80. For the NIR-red band ratio algorithms improvements were modest, with the MAD decreasing from 4.68 to 4.64 mg m−3 for the empirical red band ratio algorithm, and 3.73 to 3.67 for the semi-analytical 3-band algorithm. Three implementations of the turbidity algorithm showed improvement after tuning with the resulting distributions having reduced bias. The MAD reduced from 0.85 to 0.72, 1.22 to 1.10 and 1.93 to 1.55 FNU for the 665, 708 and 778 nm implementations respectively. However, several sources of uncertainty remain: adjacent land showed high divergence between the sensors, suggesting that high product uncertainty near land continues to be an issue for small water bodies, while it cannot be stated at this point whether MSI or OLCI results are differentially affected. The effect of spectrally wider bands of the MSI on algorithm sensitivity to chlorophyll-a and turbidity cannot be fully established without further availability of in situ optical measurements.

  • Book chapter

    Stakeholder engagement in water quality research: A case study based on the Citclops and MONOCLE projects

    Ceccaroni, L., & Piera, J (2018). Stakeholder engagement in water quality research: A case study based on the Citclops and MONOCLE projects. Citizen Science: Innovation in Open Science, Society and Policy , Pages: 201-209, http://doi.org/10.14324/111.9781787352339
  • Journal article

    Standardized spectral and radiometric calibration of consumer cameras

    O. Burggraaff, N. Schmidt, J. Zamorano, K. Pauly, S. Pascual, C. Tapia, E. Spyrakos, and F. Snik (2019). Standardized spectral and radiometric calibration of consumer cameras. Optics Express, Volume: 27, Pages: 19075-19101, http://doi.org/10.1364/OE.27.019075
    Abstract

    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.

  • Journal article

    Biases from incorrect reflectance convolution

    O.Burggraaff (2020). Biases from incorrect reflectance convolution. Optics Express, Volume: 28, Issue 9, Pages: 13801-13816, http://doi.org/10.1364/OE.391470
    Abstract

    Reflectance, a crucial earth observation variable, is converted from hyperspectral to multispectral through convolution. This is done to combine time series, validate instruments, and apply retrieval algorithms. However, convolution is often done incorrectly, with reflectance itself convolved rather than the underlying (ir)radiances. Here, the resulting error is quantified for simulated and real multispectral instruments, using 18 radiometric data sets (N = 1799 spectra). Biases up to 5% are found, the exact value depending on the spectrum and band response. This significantly affects extended time series and instrument validation, and is similar in magnitude to errors seen in previous validation studies. Post-hoc correction is impossible, but correctly convolving (ir)radiances prevents this error entirely. This requires publication of original data alongside reflectance.

  • Conference paper

    iSPEX 2: A universal smartphone add-on for portable spectroscopy and polarimetry

    O. Burggraaff, A. Perdujin, R. van Hek, N. Schmidt, C. Keller and F. Snik (2020). iSPEX 2: A universal smartphone add-on for portable spectroscopy and polarimetry. Proc. SPIE 11389, Micro- and Nanotechnology Sensors, Systems, and Applications XII, 113892K, http://doi.org/10.1117/12.2558562
    Abstract

    Spectropolarimetry is a powerful technique for remote sensing of the environment. It enables the retrieval of particle shape and size distributions in air and water to an extent that traditional spectroscopy cannot. SPEX is an instrument concept for spectropolarimetry through spectral modulation, providing snapshot, and hence accurate, hyperspectral intensity and degree and angle of linear polarization. Successful SPEX instruments have included groundSPEX and SPEX airborne, which both measure aerosol optical thickness with high precision, and soon SPEXone, which will fly on PACE. Here, we present a low-cost variant for consumer cameras, iSPEX 2, with universal smartphone support. Smartphones enable citizen science measurements which are significantly more scaleable, in space and time, than professional instruments. Universal smartphone support is achieved through a modular hardware design and SPECTACLE data processing. iSPEX 2 will be manufactured through injection molding and 3D printing. A smartphone app for data acquisition and processing is in active development. Production, calibration, and validation will commence in the summer of 2020. Scientific applications will include citizen science measurements of aerosol optical thickness and surface water reflectance, as well as low-cost laboratory and portable spectroscopy.

  • Technical note

    Construction of the Solar-tracking Radiometry platform (So-Rad)

    A. Wright & S. Simis. (2021). Construction of the Solar-tracking Radiometry platform (So-Rad). Zenodo, http://doi.org/10.5281/zenodo.4485805
    Abstract

    The purpose of the Solar-tracking Radiometry platform is to maintain optimal viewing angles of radiance sensors recording water-leaving reflectance (water colour), avoiding sun glint and platform shading even from moving platforms such as ships or buoys. The system is developed to operate autonomously, with low power consumption, integrating commercially available (ir)radiance sensors and providing remote connectivity. All hardware and software are open-source, through this repository and the associated software repository (https://github.com/monocle-h2020/so-rad). Their use is licensed under a creative commons non-commercial license.

  • Conference paper

    Validation of S3 OLCI observations

    S. Peters, S. Ghezehegn, S. Lazaros, M. Laanen & A. Hommersom (2020). Validation of S3 OLCI observations. Zenodo, http://doi.org/10.5281/zenodo.4570188
    Abstract

    The Eems Estuary is a very dynamic area featuring highly variable turbidity and Chlorophyll-a values. There is an interest to decrease theturbidity and monitoring is being put into place to observe the current status and changes. Remote sensing using Sentinel 3 OLCIobservations is a candidate monitoring technique but should provide robust and validated results. Obtaining high quality turbidity estimates starts with validated Bottom of Atmosphere reflectances.

  • Journal article

    Citizen science with colour blindess: A case study on the Forel-Ule scale

    O. Burggraaff, S. Panchagnula, F. Snik (2021). Citizen science with colour blindess: A case study on the Forel-Ule scale. PLOS ONE, Volume: 16(4): e0249755, http://doi.org/10.1371/journal.pone.0249755
    Abstract

    Many citizen science projects depend on colour vision. Examples include classification of soil or water types and biological monitoring. However, up to 1 in 11 participants are colour blind. We simulate the impact of various forms of colour blindness on measurements with the Forel-Ule scale, which is used to measure water colour by eye with a 21-colour scale. Colour blindness decreases the median discriminability between Forel-Ule colours by up to 33% and makes several colour pairs essentially indistinguishable. This reduces the precision and accuracy of citizen science data and the motivation of participants. These issues can be addressed by including uncertainty estimates in data entry forms and discussing colour blindness in training materials. These conclusions and recommendations apply to colour-based citizen science in general, including other classification and monitoring activities. Being inclusive of the colour blind increases both the social and scientific impact of citizen science.

  • Journal article

    Meta-classification of remote sensing reflectance to estimate trophic status of inland and nearshore waters

    M . Werther, E. Spyrakos, S. Simis, D. Odermatt, K. Stelzer, H. Krawczyk, O. Berlage, P. Hunter, A. Tyler (2021). Meta-classification of remote sensing reflectance to estimate trophic status of inland and nearshore waters. ISPRS Journal of Photogrammetry and Remote Sensing, Volume: 176, Pages: 109-126, http://doi.org/10.1016/j.isprsjprs.2021.04.003
    Abstract

    Common aquatic remote sensing algorithms estimate the trophic state (TS) of inland and nearshore waters through the inversion of remote sensing reflectance (Rrs (λ)) into chlorophyll-a (chla) concentration. In this study we present a novel method that directly inverts Rrs (λ)) into TS without prior chla retrieval. To successfully cope with the optical diversity of inland and nearshore waters the proposed method stacks supervised classification algorithms and combines them through meta-learning. We demonstrate the developed methodology using the waveband configuration of the Sentinel-3 Ocean and Land Colour Instrument on 49 globally distributed inland and nearshore waters (567 observations). To assess the performance of the developed approach, we compare the results with TS derived through optical water type (OWT) switching of chla retrieval algorithms. Meta-classification of TS was on average 6.75% more accurate than TS derived via OWT switching of chla algorithms. The presented method achieved 90% classification accuracies for eutrophic and hypereutrophic waters and was 12% more accurate for oligotrophic waters than derived through OWT chla retrieval. However, mesotrophic waters were estimated with lower accuracy from both our developed method and through OWT chla retrieval (52.17% and 46.34%, respectively), highlighting the need for improved base algorithms for low - moderate biomass waters. Misclassified observations were characterised by highly absorbing and/or scattering optical properties for which we propose adaptations to our classification strategy.