An algorithm to improve the detection of ocean fronts from whiskbroom scanner images

High-resolution satellite imagery is a valuable data source to analyse ocean submesoscale dynamics (i.e., with spatial scales of the order of 1–10 km) and investigate their impact on turbulent mixing, energetics of mesoscale vortices, instability processes or phytoplankton blooms. However, data acquired by satellite sensors often suffer from instrumental noise that degrades image quality and therefore compromises the detection of ocean fronts as well as the estimation of its physical characteristics. A well-known artefact in data characteristic of whiskbroom scanners is stripe noise. In this article, we propose an algorithm that improves the detection of ocean fronts by removing the impact of striping on the observed gradient field. We use level 2 sea surface temperature and chlorophyll-a products derived from NASA’s Moderate Resolution Imaging Spectroradiometer to illustrate the algorithm performance.     

Comparison of surface reflectance derived by relative radiometric normalization versus atmospheric correction for generating large-scale Landsat mosaics

Generating large-scale Landsat mosaics of surface reflectance is challenging because of the tediousness arising from atmospheric correction for a large number of scenes. To find out an alternative approach, we conducted an empirical investigation to compare the surface reflectance derived by relative radiometric normalization versus atmospheric correction using four pairs of adjoining Landsat Thematic Mapper/Enhanced Thematic Mapper Plus scenes in northern Canada. Each image was first atmospherically corrected to convert top-of-atmosphere radiance to surface reflectance. One of the converted images in each pair was then respectively used as a reference to radiometrically normalize the other original one for deriving surface reflectance. Comparison of the surface reflectance derived by these two different approaches indicates that they can match reasonably well for different landscapes, atmospheric conditions, and sensors, and the difference measured by root mean square error is no more than 0.0098 for the visible band (Band 3), 0.0271 for the near-infrared band (Band 4), and 0.022 for the middle-infrared band (Band 5). Given such a small difference, we would expect that relative radiometric normalization may be used as an alternative approach for reliable and fast retrieval of surface reflectance from Landsat data for generating mosaics of surface reflectance over large areas, overcoming the tediousness arising from atmospheric correction for a large number of scenes.     

Evaluation of multi-temporal and multi-sensor atmospheric correction strategies for land-cover accounting and monitoring in Ireland

Accurate atmospheric correction is an important preprocessing step for studies of multi-temporal land-cover mapping using optical satellite data. Model-based surface reflectance predictions (e.g. 6S – Second Simulation of Satellite Signal in the Solar Spectrum) are highly dependent on the adjustment of aerosol optical thickness (AOT) data. For regions with no or insufficient spatial and temporal coverage of meteorological ground measurements, Moderate Resolution Imaging Spectroradiometer (MODIS)-derived AOT data are a valuable alternative, especially with regard to the dynamics of atmospheric conditions. In this study, atmospheric correction strategies were assessed based on the change in standard deviation (σ) compared to the raw data and also by machine learning land-cover classification accuracies. For three Landsat 8 OLI (acquired in 2013) and two RapidEye (acquired in 2010 and 2014) scenes, seven different correction strategies were tested over an agricultural area in southeast Ireland. Visibility calculated from daily spatial averaged TERRA-MODIS estimates (1° × 1° Aerosol Product) served as input for the atmospheric correction. In almost all cases the standard deviation of the raw data is reduced after incorporation of terrain correction, compared to the atmospheric-corrected data. ATCOR®-IDL-based correction decreases the standard deviation almost consistently (ranging from ?0.3 to ?26.7). The 6S implementation in GRASS GIS showed a tendency of increasing the variation in the data, especially for the RapidEye data. No major differences in overall accuracies (OAs) and kappa values were observed between the three machine learning classification approaches. The results indicate that the ATCOR®-IDL-based correction and MODIS parameterization methods are able to decrease the standard deviation and are therefore an appropriate approach to approximate the top-of-canopy reflectance.     

A tidal correction model for near-infrared (NIR) reflectance over tidal flats

We examined the temporal variation of near-infrared (NIR) reflectance over tidal flats by proposing a tidal correction model for NIR reflectance. Sediment type, exposure time and interstitial and remnant surface waters play a key role in the optical surface reflectance of tidal flats. We established a tidal correction model for NIR reflectance by analysing 1015 scenes from the Geostationary Ocean Color Imager (GOCI), which is the first ocean colour sensor mounted on a geostationary satellite. The GOCI provides eight scenes per day with a 500-m resolution. Our results yielded two main findings. First, the reflectance model shows an asymmetric shape between ebb and flood tides. As the surface reflectance increases gradually during the ebb tide, the slope decreases steeply during the flood tide. Second, at least two models showing surface sediment are required. The tidal correction models for sand- and mixed-flats were similar; however, the one for mud-flats differed significantly. Our models work to correct for the tidal flat reflectance shown in optical images that were acquired under different tidal conditions.     

Review article: Primary aeromedical retrievals in Australia: An interrogation and search for context

Primary aeromedical retrievals are a direct scene response to patients with a critical injury or illness using a medically equipped aircraft. They are often high‐acuity taskings. In Australia, information on primary retrieval taskings is housed by service providers, of which there are many across the country. This exploratory literature review aims to explore the contemporary peer‐reviewed literature on primary aeromedical retrievals in Australia. The focus is on adult primary aeromedical retrievals undertaken in Australia and clinical tools used in this pre‐hospital setting. Included articles were reviewed for research theme (clinical and equipment, systems and/or outcomes), data coverage and appraisal of the evidence. Of the 37 articles included, majority explored helicopter retrievals (n = 32), retrieval systems (n = 21), compared outcomes within a service (n = 10) and explored retrievals in the state of New South Wales (n = 19). Major topics of focus included retrieval of trauma patients and airway management. Overall, the publications had a lower strength of evidence because of the preponderance of cross‐sectional and case‐study methodology. This review provides some preliminary but piecemeal insight into primary retrievals in Australia through a localised systems lens. However, there are several areas for research action and service outcome improvements suggested, all of which would be facilitated through the creation of a national pre‐hospital and retrieval registry. The creation of a registry would enable consideration of the frequency and context of retrievals, comparison across services, more sophisticated data interrogation. Most importantly, it can lead to service and pre‐hospital and retrieval system strengthening.     

An improved water-depth correction algorithm for seagrass mapping using hyperspectral data

Remote detection of seagrass has been limited because of numerous factors including the influence of the water column, which interferes with reflected signals from the seafloor. In a previously published study, a water-depth correction algorithm was developed to improve the detection of underwater vegetation spectral signals. The algorithm successfully corrected laboratory-measured submerged vegetation spectra for water effects, but the water absorption coefficients, derived from the data collected over a white surface, tended to underestimate the actual water absorption when applied to hyperspectral image data. The experimental conditions were modified to reduce the errors associated with the effects of enhanced multi-path scattering, to improve the algorithm using the new empirical data and to apply the algorithm to an airborne hyperspectral image data obtained over Halodule wrightii seagrass beds at Grand Bay National Estuarine Research Reserve, Mississippi, USA. The water absorption and scattering factors (A _w and R _w) for a water depth of 40 cm (the local water thickness above the seagrass canopy measured in the field) were applied to the image data to obtain the reflectance that is attributed to the water bottom surface including bare sand and seagrass beds. The contrast between the dark Halodule patches and the bright sand increased in the bands between 500 and 800 nm after the correction. The correction algorithm also increased the normalized difference vegetation index (NDVI) values for the seagrass pixels by restoring the upwelling signal in the near-infrared.     

Mesoscale model evaluation with coherent Doppler lidar for wind farm assessment

Wind measurements are fundamental inputs for wind resource assessment and the performance of wind farms. Common approaches for wind energy yield assessment are based on (1) observational data from surface station networks and (2) high-resolution computational fluid dynamic or mesoscale models. In this letter, we investigate the potential of applying a high-resolution nested mesoscale model, Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS), to predict low-level wind characteristics for wind farm domains. The model results are compared to scanning coherent Doppler lidar and tower measurements for a wind energy development. This letter focuses on the magnitude of difference between observations and simulations used for wind energy assessment. The results highlight the challenge for straight-forward application of mesoscale models, even well-established models with a relatively fine resolution on the inner nest (333 m), to produce wind predictions of sufficient fidelity and accuracy appropriate for resource assessment or operational support for individual wind farms. While many of the average wind flow features are captured by the model, their detailed spatial and temporal evolution may be improved through tighter integration with local sensor data.     

Observation of diurnal variations in mesoscale eddy sea-surface currents using GOCI data

The surface current field of a mesoscale eddy in the East Sea (Sea of Japan) was derived from consecutive Geostationary Ocean Color Imager chlorophyll-a (chl-a) concentration images using the normalized maximum cross-correlation method. The estimated current field of the eddy exhibited anticyclonic structure demonstrated by the objective dynamic thresholds of correlation coefficients. The eddy periphery was defined by fitting an ellipse to subjectively selected points from the frontal map of chl-a concentration data. Radial distribution and hourly variation of the current speed around the eddy were presented. In terms of the magnitude and direction, the estimated current field was in good agreement with altimeter-based current field and current vectors from surface drifters. Diurnal variations in the current speeds of the mesoscale eddies showed a quadratic relation to the wind speed.     

基于主成分分析模型的肝双模超声造影改良呼吸运动校正法

目的 提出基于主成分分析(PCA)模型的改良呼吸运动校正法用于肝双模超声造影(CEUS)图像,并观察其应用价值.方法 纳入11例肝内肿瘤患者,采集肝肿瘤CEUS图像,将所获肝内肿瘤CEUS视频转换为图像序列,基于灰阶子图像序列建立PCA模型,以其主成分合成最佳呼吸运动曲线;选取CEUS峰期段与呼吸曲线均值相位处最近的图...     

Subtropical gyre variability as seen from satellites

A satellite multi-sensor approach is used to analyse the biological response of open ocean regions of the subtropical gyres to changes in physical forcing. Thirteen years (1998–2010) of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) chlorophyll-a (chl-a) data, combined with concurrent satellite records of sea-surface temperature (SST) and sea level height, were analysed to investigate the seasonal and inter-annual variability of chl-a concentration within these immense so-called ocean deserts. The seasonal variability of chl-a within the gyres is driven mostly by the warming/cooling of surface waters. Summer warming promotes shallower mixed layers and lower chl-a due to a reduction of vertical mixing and consequently a decrease in nutrient supply. The opposite happens during the winter cooling period. Therefore, long-term trends in SST have the potential to cause an impact on the inter-annual variability of chl-a. Our analyses show that, during the 13 whole years of SeaWiFS data record, the North Pacific, Indian Ocean and North Atlantic gyres experienced a decrease in chl-a of 9%, 12% and 11%, respectively, with corresponding SST increases of 0.27°C, 0.42°C and 0.32°C, respectively. The South Pacific and South Atlantic gyres also showed warming trends but with weak positive trends in chl-a that are not statistically significant. We hypothesize that the warming of surface waters in these two gyres is counterbalanced by other interacting physical- and biological-driving mechanisms, as indicated in previous studies.     

MODIS BRDF effects over Brazilian tropical forests and savannahs: a comparative analysis

Bidirectional reflectance distribution function (BRDF) effects over Brazilian tropical forests (Amazon) and savannahs (Cerrado) were inspected for differences in high-quality pixel retrievals, view direction, view zenith angle (VZA), solar zenith angle (SZA) and relative azimuth angle (RAA). By comparing Moderate Resolution Imaging Spectroradiometer (MODIS) data, corrected and non-corrected for BRDF effects (2000–2014), we evaluated the magnitude of such effects over reflectance and vegetation indices (VIs). The VIs were the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI). From the Amazon to the Cerrado, we observed a higher frequency of high-quality pixel retrievals, a decrease in VZA, and an increase in seasonal SZA amplitude. Brightness increased in the backscattering direction and with shifts in RAA over tropical forests toward the BRDF hotspot at the end of dry season (September). Compared to the savannahs from the Cerrado or those from northern Amazon, stronger BRDF effects were observed for Amazonian tropical forests and for the EVI. BRDF changes in the dry season NDVI amplitude (NDVI_Sept minus NDVI_June) were lower than 5% for both biomes. For the EVI amplitude, we observed changes up to 20% over the savannahs and close to 60% for most of the Amazon. However, even after BRDF correction and in spite of the observed differences close to 0.02, EVI values from June and September were still statistically different from each other.     

Thoracic respiratory motion estimation from MRI using a statistical model and a 2-D image navigator

Respiratory motion models have potential application for estimating and correcting the effects of motion in a wide range of applications, for example in PET-MR imaging. Given that motion cycles caused by breathing are only approximately repeatable, an important quality of such models is their ability to capture and estimate the intra- and inter-cycle variability of the motion. In this paper we propose and describe a technique for free-form nonrigid respiratory motion correction in the thorax. Our model is based on a principal component analysis of the motion states encountered during different breathing patterns, and is formed from motion estimates made from dynamic 3-D MRI data. We apply our model using a data-driven technique based on a 2-D MRI image navigator. Unlike most previously reported work in the literature, our approach is able to capture both intra- and inter-cycle motion variability. In addition, the 2-D image navigator can be used to estimate how applicable the current motion model is, and hence report when more imaging data is required to update the model. We also use the motion model to decide on the best positioning for the image navigator. We validate our approach using MRI data acquired from 10 volunteers and demonstrate improvements of up to 40.5% over other reported motion modelling approaches, which corresponds to 61% of the overall respiratory motion present. Finally we demonstrate one potential application of our technique: MRI-based motion correction of real-time PET data for simultaneous PET-MRI acquisition.     

Accurate registration of optical satellite imagery with elevation models for topographic correction

It is necessary to remove the effects of topography from optical satellite imagery if automated techniques are to be used to infer surface properties. This is especially the case in mountainous terrain where variable slope normals cause variation in both illumination and reflectance of light. Digital elevation models (DEMs) are required to model slope normals and make topographic corrections. However, it is difficult to achieve accurate registration between ortho-rectified satellite images and DEMs. We show how this mis-registration, which can be spatially variable, may be accounted for with the use of a local correlation filter. The filter determines the offset between a DEM shade map and ortho-rectified satellite image for every pixel. Association of satellite image pixels with the ‘correct’ slope normal in topographic correction removes the majority of ghosting and high-frequency artefacts.     

A test of a sun glint correction method for the near-3.9 μm channels of the FengYun-3D Hyperspectral InfraRed Atmospheric Sounder (HIRAS)

ABSTRACT

The Hyperspectral InfraRed Atmospheric Sounder (HIRAS), the first Chinese sun-synchronous hyperspectral infrared sounder onboard FengYun 3D (FY-3D), was launched on 15 November 2017. It will play a significant role in improving forecast accuracy of numerical weather prediction (NWP) systems. However, sun glint effects on the FY-3D HIRAS 3.9 μm channels cannot be currently estimated by radiative transfer models. In this study, a new method for quantifying sun glint effects has been developed through a cubic fitting relationship between sun glint angles and the observed brightness temperature minus simulated brightness temperature (O ? B) after bias correction. The results show that the brightness temperatures of channel 2275 (2550 cm^?1) are higher over the ocean between 40°S and 40°N within areas of sun glint angle less than 40°; in these regions, O ? B values can reach 10 K during the daytime. Daytime global mean clear-sky O ? B biases from channel 2035 (2400 cm^?1) to channel 2275 (2550 cm^?1) increase gradually from 0 to 2.5 K. In contrast, daytime O ? B biases after sun glint correction are within ±0.2 K, indicating that the corrected radiances are suitable to be assimilated into NWP systems.     

A comparison of iopromide and iopamidol,two acidoCEST MRI contrast media that measure tumor extracellular pH

Acidosis within tumor and kidney tissues has previously been quantitatively measured using a molecular imaging technique known as acidoCEST MRI. The previous studies used iopromide and iopamidol, two iodinated contrast agents that are approved for clinical CT diagnoses and have been repurposed for acidoCEST MRI studies. We aimed to compare the performance of the two agents for measuring pH by optimizing image acquisition conditions, correlating pH with a ratio of CEST effects from an agent, and evaluating the effects of concentration, endogenous T₁ relaxation time and temperature on the pH–CEST ratio correlation for each agent. These results showed that the two agents had similar performance characteristics, although iopromide produced a pH measurement with a higher dynamic range while iopamidol produced a more precise pH measurement. We then compared the performance of the two agents to measure in vivo extracellular pH (pHe) within xenograft tumor models of Raji lymphoma and MCF‐7 breast cancer. Our results showed that the pHe values measured with each agent were not significantly different. Also, iopromide consistently measured a greater region of the tumor relative to iopamidol in both tumor models. Therefore, an iodinated contrast agent for acidoCEST MRI should be selected based on the measurement properties needed for a specific biomedical study and the pharmacokinetic properties of a specific tumor model. Copyright © 2015 John Wiley & Sons, Ltd.     

Estimation of planetary boundary layer height using Suomi NPP-CrIS soundings

The study estimates planetary boundary layer height (PBLH) using temperature and humidity profiles from Cross-Track Infrared Sounder (CrIS) onboard Suomi National Polar-Orbiting Operational Environmental Satellite System Preparatory Project (SNPP). PBLH is estimated using six different methods and also using an integrated approach. PBLH estimated from SNNP-CrIS soundings by the integrated approach is compared with the PBLH values estimated using atmospheric profiles from radiosonde ascents and Constellation Observing System for Meteorology Ionosphere and Climate-radio occultation (COSMIC-RO) measurements. Analysis shows reasonable agreement of PBLH values from SNPP-CrIS soundings with those from radiosonde measurements and COSMIC-RO retrievals, thus revealing the capability of atmospheric profiles from SNNP-CrIS for the characterization of planetary boundary layer (PBL) with excellent spatial resolution and coverage. PBLH from SNNP-CrIS soundings are observed to be overestimated by around 336 m compared to the estimates from radiosonde ascends and is partly attributed to the difference in time of observations and coarse vertical resolution of the SNPP-CrIS-derived profiles. Compared to the estimates from SNPP-CrIS soundings, PBLH values from COSMIC-RO profiles are overestimated by around 596 m. Estimates from SNPP-CrIS soundings, through the integrated approach, are further used to examine the spatial and seasonal variations of PBLH over the Indian landmass. PBLH is observed highest in pre-monsoon and lowest in winter over most of the regions. Due to rainfall and associated changes in lower atmosphere and surface characteristics, PBLH is observed shallow during monsoon.     

Estimation of atmospheric water vapour content from direct measurements of radiance in the thermal infrared region

Atmospheric water vapour content is a required parameter in thermal infrared (TIR) to carry out processes such as atmospheric correction or retrieving atmospheric factors (downwelling or upwelling irradiance, transmittance of the atmosphere and so on). This study proposes an alternative method to the ones already in use to measure water vapour content from direct measurements of downwelling atmospheric radiance in the TIR range. It was possible to estimate a linear relationship between atmospheric water vapour and downwelling atmospheric radiance using a simulated study, based on data from a radiosounding database. A subsequent validation concludes that it is possible to obtain water vapour content with an uncertainty of 0.5 cm using in situ measurements of downwelling atmospheric radiance in the TIR range of 11.5–12.5 μm.     

An open-source method of constructing cloud-free composites of forest understory temperature using MODIS

Surface air temperature (T_air) is a critical driver of ecosystem processes and phenological dynamics, and can be estimated in near-real time with satellite remote sensing. However, persistent cloud cover often creates large spatial and temporal gaps in our observation records. Previous studies have successfully mapped T_air; however, the challenges of mapping forest understory temperatures (T_ust) are relatively unexplored. This study describes a methodology for constructing cloud-free composites of T_ust at 250 m spatial resolution. We used generalized linear models to correlate daily average T_ust with ground-surveyed forest structural characteristics and land surface temperature (LST) obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). Models were applied to all four daily MODIS overpasses and combined in to a single image to maximize cloud-free spatial coverage. Pixel temperatures within the remaining cloud gaps were estimated using a temporal averaging algorithm that incorporated a novel approach for factoring the relative cloudiness between days. Models predicted T_ust to within 1.5°C (R² ~ 0.87), with an overall final map accuracy having a mean absolute error of 2.2°C. Maps were produced for two growing seasons using in situ observation data from forested sites throughout the Rocky Mountains of Alberta, Canada. By avoiding complex physical models, our procedure is computationally efficient and capable of processing large volumes of data using open-source programming languages and desktop computers.     

A semi-automated approach for the generation of a new land use and land cover product for Germany based on Landsat time-series and Lucas in-situ data

Information on land cover and land use at high spatial resolutions is essential for advancing earth system science as well as for environmental monitoring to support decision-making and reporting processes. In view of this, we present the first version of the DFD Land Use and Land Cover Product for Germany, DFD-LULC_DE, for the year 2014, generated from 702 Landsat-7 and Landat-8 scenes at 30 m resolution. The results were derived based on a fully automated preprocessing chain that integrates data acquisition, radiometric, atmospheric and topographic correction, as well as spectral–temporal feature extraction for all Landsat surface reflectance bands, brightness temperature and various spectral indices. The classification followed a two-step approach: first, an initial classification is performed using a Random Forest classifier trained on ground truth data obtained from the LUCAS survey of EUROSTAT, followed by a semi-automated sampling of additional training data to further improve the initial classification results. Automatic selection of appropriate training samples is based on the vote entropy derived from the initial classification, thereby keeping manual user interaction low. The approach demonstrated is promising, also with respect to a European wide application, and contributes towards the advancement and enhancement of the DLR-DFD’s processing chains, which are directed towards the generation of land cover products at regular intervals being of central importance to related land monitoring and reporting services.     

Mining a Sea of Data: Deducing the Environmental Controls of Ocean Chlorophyll

Chlorophyll biomass in the surface ocean is regulated by a complex interaction of physiological, oceanographic, and ecological factors and in turn regulates the rates of primary production and export of organic carbon to the deep ocean. Mechanistic models of phytoplankton responses to climate change require the parameterization of many processes of which we have limited knowledge. We develop a statistical approach to estimate the response of remote-sensed ocean chlorophyll to a variety of physical and chemical variables. Irradiance over the mixed layer depth, surface nitrate, sea-surface temperature, and latitude and longitude together can predict 83% of the variation in log chlorophyll in the North Atlantic. Light and nitrate regulate biomass through an empirically determined minimum function explaining nearly 50% of the variation in log chlorophyll by themselves and confirming that either light or macronutrients are often limiting and that much of the variation in chlorophyll concentration is determined by bottom-up mechanisms. Assuming the dynamics of the future ocean are governed by the same processes at work today, we should be able to apply these response functions to future climate change scenarios, with changes in temperature, nutrient distributions, irradiance, and ocean physics.