Biases from incorrect reflectance convolution

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.InstrumentationEnvironmental Biolog

Accessible remote sensing of water

Water is all around us and is vital for all aspects of life. Studying the various compounds and life forms that inhabit natural waters lets us better understand the world around us.Remote sensing enables global measurements with rapid response and high consistency. Citizen science provides new knowledge and greatly increases the scientific and social impact of research.In this thesis, we investigate several aspects of citizen science and remote sensing of water, with a focus on uncertainty and accessibility. We improve existing techniques and develop new methods to use smartphone cameras for accessible remote sensing of water.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 776480 (MONOCLE).Conservation BiologyInstrumentatio

Dive into the unknown: embracing uncertainty to advance aquatic remote sensing

Uncertainty is an inherent aspect of aquatic remote sensing, originating from sources such as sensor noise, atmospheric variability, and human error. Although many studies have advanced the understanding of uncertainty, it is still not incorporated routinely into aquatic remote sensing research. Neglecting uncertainty can lead to misinterpretations of results, missed opportunities for innovative research, and a limited understanding of complex aquatic systems. In this article, we demonstrate how working with uncertainty can advance remote sensing through three examples: validation and match-up analysis, targeted improvement of data products, and decision-making based on information acquired through remote sensing. We advocate for a change of perspective: the uncertainty inherent in aquatic remote sensing should be embraced, rather than viewed as a limitation. Focusing on uncertainty not only leads to more accurate and reliable results but also paves the way for innovation through novel insights, product improvements, and more informed decision-making in the management and preservation of aquatic ecosystems.Environmental Biolog

Citizen science with colour blindness: a case study on the Forel-Ule scale

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.Horizon 2020(H2020)776480Environmental Biolog

Accuracy and reproducibility of above-water radiometry with calibrated smartphone cameras using RAW data

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.Horizon 2020(H2020)776480Environmental BiologyInstrumentatio

Data calibration for the MASCARA and bRing instruments

Aims: MASCARA and bRing are photometric surveys designed to detect variability caused by exoplanets in stars with $m_V < 8.4$. Such variability signals are typically small and require an accurate calibration algorithm, tailored to the survey, in order to be detected. This paper presents the methods developed to calibrate the raw photometry of the MASCARA and bRing stations and characterizes the performance of the methods and instruments. Methods: For the primary calibration a modified version of the coarse decorrelation algorithm is used, which corrects for the extinction due to the earth's atmosphere, the camera transmission, and intrapixel variations. Residual trends are removed from the light curves of individual stars using empirical secondary calibration methods. In order to optimize these methods, as well as characterize the performance of the instruments, transit signals were injected in the data. Results: After optimal calibration an RMS scatter of 10 mmag at $m_V \sim 7.5$ is achieved in the light curves. By injecting transit signals with periods between one and five days in the MASCARA data obtained by the La Palma station over the course of one year, we demonstrate that MASCARA La Palma is able to recover 84.0, 60.5 and 20.7% of signals with depths of 2, 1 and 0.5% respectively, with a strong dependency on the observed declination, recovering 65.4% of all transit signals at $\delta > 0^\circ$ versus 35.8% at $\delta < 0^\circ$. Using the full three years of data obtained by MASCARA La Palma to date, similar recovery rates are extended to periods up to ten days. We derive a preliminary occurrence rate for hot Jupiters around A-stars of ${>} 0.4 \%$, knowing that many hot Jupiters are still overlooked. In the era of TESS, MASCARA and bRing will provide an interesting synergy for finding long-period (${>} 13.5$ days) transiting gas-giant planets around the brightest stars.Comment: 18 pages, 17 figures, accepted for publication in A&

Bright Southern Variable Stars in the bRing Survey

In addition to monitoring the bright star β Pic during the near-transit event for its giant exoplanet, the β Pictoris b Ring (bRing) observatories at Siding Springs Observatory, Australia and Sutherland, South Africa have monitored the brightnesses of bright stars (V  4–8 mag) centered on the south celestial pole (δ ≤ −30°) for approximately two years. Here we present a comprehensive study of the bRing time-series photometry for bright southern stars monitored between 2017 June and 2019 January. Of the 16,762 stars monitored by bRing, 353 were found to be variable. Of the variable stars, 80% had previously known variability and 20% were new variables. Each of the new variables was classified, including three new eclipsing binaries (HD 77669, HD 142049, HD 155781), 26 δ Scutis, 4 slowly pulsating B stars, and others. This survey also reclassified four stars based on their period of pulsation, light curve, spectral classification, and color–magnitude information. The survey data were searched for new examples of transiting circumsecondary disk systems, but no candidates were found.Stars and planetary system

Impact of 3 Tesla MRI on interobserver agreement in clinically isolated syndrome : A MAGNIMS multicentre study

The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This research has been supported by a programme grant (14-358e) from the Dutch MS Research Foundation (Voorschoten, The Netherlands). The study in London was supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre.Background: Compared to 1.5 T, 3 T magnetic resonance imaging (MRI) increases signal-to-noise ratio leading to improved image quality. However, its clinical relevance in clinically isolated syndrome suggestive of multiple sclerosis remains uncertain. Objectives: The purpose of this study was to investigate how 3 T MRI affects the agreement between raters on lesion detection and diagnosis. Methods: We selected 30 patients and 10 healthy controls from our ongoing prospective multicentre cohort. All subjects received baseline 1.5 and 3 T brain and spinal cord MRI. Patients also received follow-up brain MRI at 3-6 months. Four experienced neuroradiologists and four less-experienced raters scored the number of lesions per anatomical region and determined dissemination in space and time (McDonald 2010). Results: In controls, the mean number of lesions per rater was 0.16 at 1.5 T and 0.38 at 3 T (p = 0.005). For patients, this was 4.18 and 4.40, respectively (p = 0.657). Inter-rater agreement on involvement per anatomical region and dissemination in space and time was moderate to good for both field strengths. 3 T slightly improved agreement between experienced raters, but slightly decreased agreement between less-experienced raters. Conclusion: Overall, the interobserver agreement was moderate to good. 3 T appears to improve the reading for experienced readers, underlining the benefit of additional training

Manual and automated tissue segmentation confirm the impact of thalamus atrophy on cognition in multiple sclerosis: A multicenter study

Background and rationale: Thalamus atrophy has been linked to cognitive decline in multiple sclerosis (MS) using various segmentation methods. We investigated the consistency of the association between thalamus volume and cognition in MS for two common automated segmentation approaches, as well as fully manual outlining. Methods: Standardized neuropsychological assessment and 3-Tesla 3D-T1-weighted brain MRI were collected (multi-center) from 57 MS patients and 17 healthy controls. Thalamus segmentations were generated manually and using five automated methods. Agreement between the algorithms and manual outlines was assessed with Bland-Altman plots; linear regression assessed the presence of proportional bias. The effect of segmentation method on the separation of cognitively impaired (CI) and preserved (CP) patients was investigated through Generalized Estimating Equations; associations with cognitive measures were investigated using linear mixed models, for each method and vendor. Results: In smaller thalami, automated methods systematically overestimated volumes compared to manual segmentations [ρ=(-0.42)-(-0.76); p-values < 0.001). All methods significantly distinguished CI from CP MS patients, except manual outlines of the left thalamus (p = 0.23). Poorer global neuropsychological test performance was significantly associated with smaller thalamus volumes bilaterally using all methods. Vendor significantly affected the findings. Conclusion: Automated and manual thalamus segmentation consistently demonstrated an association between thalamus atrophy and cognitive impairment in MS. However, a proportional bias in smaller thalami and choice of MRI acquisition system might impact the effect size of these findings