The binaural interaction component (BIC) of the auditory brainstem response is a noninvasive electroencephalographic signature of neural processing of binaural sounds. Despite its potential as a clinical biomarker, the neural structures and mechanism that generate the BIC are not known. We explore here the hypothesis that the BIC emerges from excitatory-inhibitory interactions in auditory brainstem neurons. We measured the BIC in response to click stimuli while varying interaural time differences (ITDs) in subjects of either sex from five animal species. Species had head sizes spanning a 3.5-fold range and correspondingly large variations in the sizes of the auditory brainstem nuclei known to process binaural sounds [the medial superior olive (MSO) and the lateral superior olive (LSO)]. The BIC was reliably elicited in all species, including those that have small or inexistent MSOs. In addition, the range of ITDs where BIC was elicited was independent of animal species, suggesting that the BIC is not a reflection of the processing of ITDs per se. Finally, we provide a model of the amplitude and latency of the BIC peak, which is based on excitatory-inhibitory synaptic interactions, without assuming any specific arrangement of delay lines. Our results show that the BIC is preserved across species ranging from mice to humans. We argue that this is the result of generic excitatory-inhibitory synaptic interactions at the level of the LSO, and thus best seen as reflecting the integration of binaural inputs as opposed to their spatial properties. Noninvasive electrophysiological measures of sensory system activity are critical for the objective clinical diagnosis of human sensory processing deficits. The binaural component of sound-evoked auditory brainstem responses is one such measure of binaural auditory coding fidelity in the early stages of the auditory system. Yet, the precise neurons that lead to this evoked potential are not fully understood. This paper provides a comparative study of this potential in different mammals and shows that it is preserved across species, from mice to men, despite large variations in morphology and neuroanatomy. Our results confirm its relevance to the assessment of binaural hearing integrity in humans and demonstrates how it can be used to bridge the gap between rodent models and humans.

As part of the Sentinel-2 mission, a Radiometric Uncertainty Tool (RUT) has been recently released to the community. This tool estimates the Sentinel-2 radiometric uncertainty associated with each pixel in the top-of-atmosphere (TOA) reflectance factor images provided by the European Space Agency (ESA). The use of such information enables users to assess the "fitness for purpose" of the data to their specific application. The work described here summarises the efforts and results of integrating the RUT for radiometric validation activities for the Sentinel-2 mission. Starting from the results provided by the RUT, the focus will be on providing a methodology to calculate the uncertainty associated with the mean TOA reflectance factor in a Region of Interest (ROI). Two different methodsone simple method directly using the RUT and a more rigorous one based on Monte Carlo method (MCM) propagationare proposed and compared. These two methods focus on the effect of the spectral, spatial and temporal correlation of the errors in different ROI pixels and the impact of correlation on the uncertainty associated with the mean TOA reflectance factor. The study has also considered the impact of uncertainty contributions not included in the first version of the RUT.

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Providing uncertainties in satellite datasets used for Earth observation can be a daunting prospect because of the many processing stages and input data required to convert raw detector counts to calibrated radiances. The Sea and Land Surface Temperature Radiometer (SLSTR) was designed to provide measurements of the Earth’s surface for operational and climate applications. In this paper the authors describe the traceability chain and derivation of uncertainty estimates for the thermal infrared channel radiometry. Starting from the instrument model, the contributing input quantities are identified to build up an uncertainty effects tree. The characterisation of each input effect is described, and uncertainty estimates provided which are used to derive the combined uncertainties as a function of scene temperature. The SLSTR Level-1 data products provide uncertainty estimates for fully random effects (noise) and systematic effects that can be mapped for each image pixel, examples of which are shown.

The Sentinel-3 mission is part of the Copernicus programme space segment and has the objective of making global operational observations of ocean, land and atmospheric parameters with its four on-board sensors. Two Sentinel-3 satellites are currently on orbit, providing near-daily global coverage. Sentinel-3A was launched on 16 February 2016 and Sentinel-3B on 25 April 2018. For the early part of its operation, Sentinel-3B flew in tandem with Sentinel-3A, flying 30 s ahead of its twin mission. This provided a unique opportunity to compare the instruments on the two satellites, and to test the per pixel uncertainty values in a metrologically-robust manner. In this work, we consider the tandem-phase data from the infrared channels of one of the on-board instruments: the Sea and Land Surface Temperature Radiometer, SLSTR. A direct comparison was made of both the Level 1 radiance values and the Level 2 sea surface temperature values derived from those radiances. At Level 1, the distribution of differences between the sensor values were compared to the declared uncertainties for data gridded on to a regular latitude-longitude grid with propagated pixel uncertainties. The results showed good overall radiometric agreement between the two sensors, with mean differences of ∼0.06 K, although there was a scene-temperature dependent difference for the oblique view that was consistent with what was expected from a stray light effect observed pre-flight. We propose a means to correct for this effect based on the tandem data. Level 1 uncertainties were found to be representative of the variance of the data, expect in those channels affected by the stray light effect. The sea surface temperature results show a very small difference between the sensors that could be in part due to the fact that the Sentinel-3A retrieval coefficients were also applied to the Sentinel-3B retrieval because the Sentinel-3B coefficients are not currently available. This will lead to small errors between the S3A and S3B retrievals. The comparison also suggests that the retrieval uncertainties may need updating for two of the retrieval processes that there are extra components of uncertainty related the quality level and the probability of cloud that should be included. Finally, a study of the quality flags assigned to sea surface temperature pixel values provided valuable insight into the origin of those quality levels and highlighted possible uncertainties in the defined quality level.