Synergistic angular and spectral estimation of aerosol properties using CHRIS/PROBA-1 and simulated Sentinel-3 data

We develop a method to derive aerosol properties over land surfaces using combined spectral and angular information, such as available from ESA Sentinel-3 mission, to be launched in 2015. A method of estimating aerosol optical depth (AOD) using only angular retrieval has previously been demonstrated on data from the ENVISAT and PROBA-1 satellite instruments, and is extended here to the synergistic spectral and angular sampling of Sentinel-3. The method aims to improve the estimation of AOD, and to explore the estimation of fine mode fraction (FMF) and single scattering albedo (SSA) over land surfaces by inversion of a coupled surface/atmosphere radiative transfer model. The surface model includes a general physical model of angular and spectral surface reflectance. An iterative process is used to determine the optimum value of the aerosol properties providing the best fit of the corrected reflectance values to the physical model. The method is tested using hyperspectral, multi-angle Compact High Resolution Imaging Spectrometer (CHRIS) images. The values obtained from these CHRIS observations are validated using ground-based sun photometer measurements. Results from 22 image sets using the synergistic retrieval and improved aerosol models show an RMSE of 0.06 in AOD, reduced to 0.03 over vegetated targets.

Dyspnea-related cues engage the prefrontal cortex - evidence from functional brain imaging in COPD

Dyspnea is the major source of disability in chronic obstructive pulmonary disease (COPD). In COPD, environmental cues (e.g. the prospect of having to climb stairs) become associated with dyspnea, and may trigger dyspnea even before physical activity commences. We hypothesised that brain activation relating to such cues would be different between COPD patients and healthy controls, reflecting greater engagement of emotional mechanisms in patients. Methods: Using FMRI, we investigated brain responses to dyspnea-related word cues in 41 COPD patients and 40 healthy age-matched controls. We combined these findings with scores of self-report questionnaires thus linking the FMRI task with clinically relevant measures. This approach was adapted from studies in pain that enables identification of brain networks responsible for pain processing despite absence of a physical challenge. Results: COPD patients demonstrate activation in the medial prefrontal cortex (mPFC), and anterior cingulate cortex (ACC) which correlated with the visual analogue scale (VAS) response to word cues. This activity independently correlated with patient-reported questionnaires of depression, fatigue and dyspnea vigilance. Activation in the anterior insula, lateral prefrontal cortex (lPFC) and precuneus correlated with the VAS dyspnea scale but not the questionnaires. Conclusions: Our findings suggest that engagement of the brain's emotional circuitry is important for interpretation of dyspnea-related cues in COPD, and is influenced by depression, fatigue, and vigilance. A heightened response to salient cues is associated with increased symptom perception in chronic pain and asthma, and our findings suggest such mechanisms may be relevant in COPD.

Time-lapse geophysical investigations over known archaeological features using electrical resistivity imaging and earth resistance

Electrical methods of geophysical survey are known to produce results that are hard to predict at different times of the year, and under differing weather conditions. This is a problem which can lead to misinterpretation of archaeological features under investigation. The dynamic relationship between a ‘natural’ soil matrix and an archaeological feature is a complex one, which greatly affects the success of the feature’s detection when using active electrical methods of geophysical survey. This study has monitored the gradual variation of measured resistivity over a selection of study areas. By targeting difficult to find, and often ‘missing’ electrical anomalies of known archaeological features, this study has increased the understanding of both the detection and interpretation capabilities of such geophysical surveys. A 16 month time-lapse study over 4 archaeological features has taken place to investigate the aforementioned detection problem across different soils and environments. In addition to the commonly used Twin-Probe earth resistance survey, electrical resistivity imaging (ERI) and quadrature electro-magnetic induction (EMI) were also utilised to explore the problem. Statistical analyses have provided a novel interpretation, which has yielded new insights into how the detection of archaeological features is influenced by the relationship between the target feature and the surrounding ‘natural’ soils. The study has highlighted both the complexity and previous misconceptions around the predictability of the electrical methods. The analysis has confirmed that each site provides an individual and nuanced situation, the variation clearly relating to the composition of the soils (particularly pore size) and the local weather history. The wide range of reasons behind survey success at each specific study site has been revealed. The outcomes have shown that a simplistic model of seasonality is not universally applicable to the electrical detection of archaeological features. This has led to the development of a method for quantifying survey success, enabling a deeper understanding of the unique way in which each site is affected by the interaction of local environmental and geological conditions.