Changes in the Ice Cover of the Russian Arctic Seas in the 21st Century Based on the Results of Climate Models of the CMIP6 Project

For more than four decades, geostationary weather satellites have been used successfully by NOAA's National Weather Service for daily weather forecasting and for tracking storm events. At the same time, polar orbit systems, such as NOAA/AVHRR, SeaWIFS and Landsat TM, have provided valuable information about slowly varying features and processes in the open ocean and on land. However, coastal and estuarine applications of remote sensing require significantly finer temporal, spatial, and spectral resolution than open ocean studies. Current polar orbit satellites lack the temporal resolution, while present geostationary orbit satellites have limited spatial and spectral resolution. Geostationary satellites can provide images of clouds, ocean and land surfaces very frequently, since they are positioned above a selected point on the equator and continuously view the same one third of the Earth. However, in order to have the same angular velocity as the Earth, they must be located in an orbit 23,000 miles above the equator, which limits their spatial resolution. NOAA, with support from NASA and the Navy, is developing GOES-R, a geostationary satellite. GOES-R will include a Hyperspectral Environmental Suite for Coastal Waters (HES-CW) and atmospheric sounding sensors which should provide the resolution levels required for coastal and estuarine observations. To achieve this, full use will be made of the fact that a geostationary platform offers a very high sampling frequency and long integration times, allowing for high signal-to-noise (S/N) ratios. Thus, useful trade-offs can be made between S/N, number of spectral bands, and sampling frequency, even though the spatial resolution is constrained to 150-300 m at the equator by system design considerations, such as mirror size, stability and pointing of the spacecraft. The objective of this paper is to illustrate coastal and estuarine resolution requirements for typical coastal features and processes such as: 1) chlorophyll concentration and coastal productivity, 2) harmful algal blooms and eutrophication, 3) current measurement by feature tracking, 4) oil slick drift and capture by estuarine fronts, 5) dynamic behavior of turbidity plumes and ocean-dumped waste, 6) propagation of internal waves on the continental shelf, 7) ice cover for coastal shipping, 8) coastal flooding and other catastrophic events, and 9) coastal ecosystem and wetland habitat changes. These resolution requirements are then compared to current GOES-R specifications.

<p>Agricultural sector, being the largest consumer of water is greatly affected by climatic variability and disasters. Most parts of the world already face an enormous challenge in meeting competitive and conflicting multi-sector water demands. Climate change has further exacerbated this challenge by putting the sustainability of current cropping patterns and irrigation practices in question. For ensuring climate-resilient food production, it is crucial to examine the patterns of the projected climate and potential impacts on the agricultural sector at a basin scale. Hence, this study was carried out for an already water-scarce basin, Rushikulya River basin (RRB), located in the coastal region of eastern India. The bias-corrected NorESM2-MM general circulation model of Coupled Model Intercomparison Project-6 (CMIP6) was used in this study under four shared socioeconomic pathway (SSPs) scenarios, namely SSP126, SSP245, SSP370 and SSP585. The projected climatic parameters and crop water demands of the basin were analyzed assuming existing cropping pattern in the future. Analysis of the results reveals a significant and rapid increase in the temperature at a rate of 0.02-0.5ºC/year during 2026-2100 under all SSPs except SSP126, whereas the rainfall is expected to increase slightly during 2026-2100 as compared to the baseline period (1990-2016), especially in the far future (2076-2100) under all the SSPs. In contrast, monsoon rainfall is predicted to decrease under SSP245 and SSP370, while a slight increase in the monsoon rainfall is evident under SSP126 and SSP585. Although the rainy days will decrease slightly in the future 25-year time window, the number of heavy rainfall events is predicted to increase by two to three times. Also, retrospective analysis of rainfall and evapotranspiration suggested an existence of rainfall deficit (rainfall-evapotranspiration) in the basin throughout the year, except during July to September. The rainfall deficit in the basin during 2026-2100 is found to remain more or less same in the non-monsoon season, except for the month of October under SSP245, SSP370 and SSP585 scenarios where deficit increases by two folds. Rainfall is expected to be in surplus by 4 to 5 times higher under all SSPs except for SSP245. As to the evapotranspiration, an insignificant increasing trend is observed under future climatic condition with only 2 to 4% rise in the crop water demand compared to the baseline period. As the basin is already water stressed during most months in a year under baseline and future climatic conditions, continuing the current practice of monsoon paddy dominant cultivation in the basin will further aggravate this situation. The results of this study will be helpful in formulating sustainable irrigation plans and adaptation measures to address climate-induced water stress in the basin.</p><p><strong>Keywords:</strong> Climate change; CMIP6; SSP; Monsoon rainfall; Temperature; Crop water demand.</p>

Due to the limitations of hardware technology, satellite sensors cannot obtain images with high temporal, spatial, and spectral resolutions at the same time. Current spatiotemporal fusion methods try to solve the contradiction between temporal resolution and spatial resolution, which cannot achieve good reconstruction accuracy partly because the data sources are from heterogeneous platforms with long chains difficult to be modeled. Different from the crossing-platform fusion, this work proposes to improve the spatial and temporal resolutions on a single platform. For the 2-m panchromatic images, 8-m multispectral images, and 16-m wide-field-view images captured by the Gaofen-1 satellite, our goal is to produce 2-m multispectral images with high temporal resolutions. Two convolutional neural networks are built to solve this spatiotemporal-spectral fusion issue with pansharpening and spatiotemporal fusion in serial. In the validation stage, the 2-m multispectral images are built and evaluated with the panchromatic images and 8-m multispectral images. The digital and visual evaluations show that our method can produce visually acceptable fusion quality, which may enhance the feasibility of the Gaofen-1 data.

Introduction: Neural crest (NC) cells are involved in cardiac development, and NC defects account for over 10% of all congenital heart disease. The migratory and morphogenic patterns of these NC cells are not well understood because conventional optical microscopes struggle to acquire images of live embryos with sufficient temporal and spatial resolution without causing major photo-damage. To address these issues, the lab has designed and built a light-sheet fluorescence microscope (LSFM) that is capable of acquiring dual-channel signals with high spatial and temporal resolution. Hypothesis: We hypothesize that the LSFM system will capture heart tube development at a cellular level and show the time point and location at which the NC cells integrate with the heart tube. Methods and Results: The transgenic line that is manipulated for this study is the Tg(neural crest:nfsB-mCherry, cardiac: eGFP) in which the heart tube fluoresces green while NC-derived cells fluoresce red. 10 zebrafish were imaged using the 3-D time-lapse LSFM setup from 15 hours post fertilization (hpf) to 36 hpf. The imaging data was qualitative and consistent across fish. Fig. 1 (a) shows mCherry NC-derived cells (red) integrating with eGFP cardiomyocytes (green) and areas of colocalization (yellow). Fig 1 (b) and (c) show the time course of the NC cell migration into the heart tube. By 26-27 hpf, the NC cells contacted the dorsal surface of the heart tube. By the 29 hpf, the NC-derived cells integrated into the heart tube and expressed nuclear eGFP, which is visualized by the yellow fluorescing portions of the image. Conclusions: We conclude that the LSFM was successful in acquiring distinct signals from the heart tube cells and the NC-derived cells. The temporal and spatial resolution was sufficient to observe NC migration at the single cell level and map out exactly where along the heart tubule the NC-cells entered.

Parameterization of air temperature in high temporal and spatial resolution from a combination of the SEVIRI and MODIS instruments

Progress in modern neuroscience critically depends on our ability to observe the activity of large neuronal populations with cellular spatial and high temporal resolution. However, two bottlenecks constrain efforts towards fast imaging of large populations. First, the resulting large video data is challenging to analyze. Second, there is an explicit tradeoff between imaging speed, signal-to-noise, and field of view: with current recording technology we cannot image very large neuronal populations with simultaneously high spatial and temporal resolution. Here we describe multi-scale approaches for alleviating both of these bottlenecks. First, we show that spatial and temporal decimation techniques based on simple local averaging provide order-of-magnitude speedups in spatiotemporally demixing calcium video data into estimates of single-cell neural activity. Second, once the shapes of individual neurons have been identified at fine scale (e.g., after an initial phase of conventional imaging with standard temporal and spatial resolution), we find that the spatial/temporal resolution tradeoff shifts dramatically: after demixing we can accurately recover denoised fluorescence traces and deconvolved neural activity of each individual neuron from coarse scale data that has been spatially decimated by an order of magnitude. This offers a cheap method for compressing this large video data, and also implies that it is possible to either speed up imaging significantly, or to “zoom out” by a corresponding factor to image order-of-magnitude larger neuronal populations with minimal loss in accuracy or temporal resolution.

Weather radar networks are an excellent tool for quantitative precipitation estimation (QPE), due to their high resolution in space and time, particularly in remote mountain areas such as the Tropical Andes. Nevertheless, reduction of the temporal and spatial resolution might severely reduce the quality of QPE. Thus, the main objective of this study was to analyze the impact of spatial and temporal resolutions of radar data on the cumulative QPE. For this, data from the world’s highest X-band weather radar (4450 m a.s.l.), located in the Andes of Ecuador (Paute River basin), and from a rain gauge network were used. Different time resolutions (1, 5, 10, 15, 20, 30, and 60 min) and spatial resolutions (0.5, 0.25, and 0.1 km) were evaluated. An optical flow method was validated for 11 rainfall events (with different features) and applied to enhance the temporal resolution of radar data to 1-min intervals. The results show that 1-min temporal resolution images are able to capture rain event features in detail. The radar–rain gauge correlation decreases considerably when the time resolution increases (r from 0.69 to 0.31, time resolution from 1 to 60 min). No significant difference was found in the rain total volume (3%) calculated with the three spatial resolution data. A spatial resolution of 0.5 km on radar imagery is suitable to quantify rainfall in the Andes Mountains. This study improves knowledge on rainfall spatial distribution in the Ecuadorian Andes, and it will be the basis for future hydrometeorological studies.

Satellite data holds considerable potential as a source of information on rice crop growth which can be used to inform agronomy. However, given the typical field sizes in many rice-growing countries such as China, data from coarse spatial resolution satellite systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are inadequate for resolving crop growth variability at the field scale. Nevertheless, systems such as MODIS do provide images with sufficient frequency to be able to capture the detail of rice crop growth trajectories throughout a growing season. In order to generate high spatial and temporal resolution data suitable for mapping rice crop phenology, this study fused MODIS data with lower frequency, higher spatial resolution Landsat data. An overall workflow was developed which began with image preprocessing, calculation of multi-temporal normalized difference vegetation index (NDVI) images, and spatiotemporal fusion of data from the two sensors. The Spatial and Temporal Adaptive Reflectance Fusion Model was used to effectively downscale the MODIS data to deliver a time-series of 30 m spatial resolution NDVI data at 8-day intervals throughout the rice-growing season. Zonal statistical analysis was used to extract NDVI time-series for individual fields and signal filtering was applied to the time-series to generate rice phenology curves. The downscaled MODIS NDVI products were able to characterize the development of paddy rice at fine spatial and temporal resolutions, across wide spatial extents over multiple growing seasons. These data permitted the extraction of key crop seasonality parameters that quantified inter-annual growth variability for a whole agricultural region and enabled mapping of the variability in crop performance between and within fields. Hence, this approach can provide rice crop growth data that is suitable for informing agronomic policy and practice across a wide range of scales.

Rapid delineation of potential damage from hurricanes to the natural and human-modified landscape is critical for disaster management and for assessment of ecological and economic impacts.Geospatial technologies including Remote Sensing (RS), Geographic Information Systems/Science (GIS), and Global Positioning Systems (GPS) provide new tools for delineating damage potential and for assessing destruction after natural disasters.Historically, airborne and space RS missions have been used for post-hurricane impact assessment, but these data are limited in a variety of ways.Space-based RS data have limitations including differing specifications for spatial, spectral, and temporal resolution.As an example, predicting damage or assessing damage from land-falling hurricanes via satellite data is often hampered by temporal resolution (re-visit frequency) that can result in unavailability of cloudfree images over impacted areas.Airborne RS data are not temporally limited to a specific window of ground coverage and can even acquire data during cloudy conditions.Airborne RS instruments may provide data of higher spectral and spatial resolution than space-borne instruments; however complete 'wall-to-wall' coverage of landscapes impacted by tropical systems as large as hurricane Katrina can be impractical and prohibitively expensive.Consequently, many geospatial analysts first use space-borne data and geospatial modeling at a relatively coarse-resolution to provide an overview of hurricane impacted areas, and then they use this information to pinpoint locations where they need to acquire airborne RS data for higher-resolution subsets of damaged areas.Therefore, there is a need to develop more accurate geospatial models that incorporate readily available data at a regional scale to assist emergency and disaster management agencies with their decision-support.According to Hodgson et al., (2010), agencies with first responder responsibilities have expressed the critical need for image acquisitions over disaster impacted areas within 24 hours, or within three days of the event at a minimum.Given the limitations for rapid acquisition of RS imagery over hurricane-impacted areas, researchers at Mississippi State University in coordination with Mississippi Emergency Management Agency (MEMA) advisors have developed a geospatial modeling-based tool that enables rapid depiction (within 24 -48 hours after landfall) of a damage probability statistical grid which incorporates readily available data sources such as storm surge predictions and precipitation and wind forecasts.This grid can be overlain on a variety of image and GIS base layers to help plan for evacuation procedures prior to landfall and to help orient and manage the effective allocation of resources to impacted areas.www.intechopen.

Introduction Dynamic MRI may be used to image motion that is needed for accurate radiotherapy treatment planning. The tradeoff between temporal and spatial resolution has been a major issue affecting the use of dynamic MRI. Here we propose to improve temporal resolution by reconstructing high spatial resolution components with deformably registered motion data. Dynamic MRI with increased temporal resolution without scarifying spatial resolution for several sites are presenting. Methods and Materials High and low spatial resolution components of the image typically move similarly, i.e. share most of the motion vector information. The motion vectors can be obtained by deformable registration of the central k‐space, and applied to the high spatial resolution data. This allows one to acquire the peripheral k‐space at a lower temporal resolution than the central k‐space therefore increase the overall sampling rate. We tested the possible gain by this method for 2‐dimensional dynamic MRIs (SIEMENS 3T Verio) of swallowing, breathing and peristalsis. Results The mean error in the displacement vectors obtained using only the central 20% of k‐space vs. using the full k‐space was less than 0.5mm, which reduced to 0.2mm if 50% of the radius is used. Peristalsis motion with sudden changes shared least information between central and peripheral k‐space. The reconstructed images with as low as central 20% of the k‐space radius are very similar to images reconstructed with full k‐space. The resulting probability distance histograms and regions of interest show minimal variation. Conclusion Temporal resolution can be improved considerably without compromising the spatial resolution if the low‐resolution vector information is used to construct high spatial resolution data as demonstrated in this work. The deformable registration accuracy is highly critical for the overall performance of the method. Conflict of Interest: This work is supported partially by Siemens Healthcare.

Abstract. Meteorological forcing plays a critical role in accurately simulating the watershed hydrological cycle. With the advancement of high-performance computing and the development of integrated watershed models, simulating the watershed hydrological cycle at high temporal (hourly to daily) and spatial resolution (tens of meters) has become efficient and computationally affordable. These hyperresolution watershed models require high resolution of meteorological forcing as model input to ensure the fidelity and accuracy of simulated responses. In this study, we utilized the Advanced Terrestrial Simulator (ATS), an integrated watershed model, to simulate surface and subsurface flow and land surface processes using unstructured meshes at the Coal Creek Watershed near Crested Butte (Colorado). We compared simulated watershed hydrologic responses including streamflow and distributed variables such as evapotranspiration, snow water equivalent (SWE), and groundwater table driven by three publicly available, gridded meteorological forcings (GMFs) – Daily Surface Weather and Climatological Summaries (Daymet), the Parameter-elevation Regressions on Independent Slopes Model (PRISM), and the North American Land Data Assimilation System (NLDAS). By comparing various spatial resolutions (ranging from 400 m to 4 km) of PRISM, the simulated streamflow only becomes marginally worse when spatial resolution of meteorological forcing is coarsened to 4 km (or 30 % of the watershed area). However, the 4 km-resolution has much worse performance than finer resolution in spatially distributed variables such as SWE. Using the temporally disaggregated PRISM, we compared models forced by different temporal resolutions (hourly to daily), and sub-daily resolution preserves the dynamic watershed responses (e.g., diurnal fluctuation of streamflow) that are absent in results forced by daily resolution. Conversely, the simulated streamflow shows better performance using daily resolution compared to that using sub-daily resolution. Our findings suggest that the choice of GMF and its spatiotemporal resolution depends on the quantity of interest and its spatial and temporal scale, which may have important implications for model calibration and watershed management decisions.

The aim of this study was to evaluate free-breathing single-shot real-time cine imaging for functional cardiac imaging at 3 T with increased spatial resolution. Special emphasis of this study was placed on the influence of parallel imaging techniques. Gradient echo phantom images were acquired with GRAPPA and modified SENSE reconstruction using both integrated and separate reference scans as well as TGRAPPA and TSENSE. In vivo measurements were performed for GRAPPA reconstruction with an integrated and a separate reference scan, as well as TGRAPPA using balanced steady-state free precession protocols. Three clinical protocols, rtLRInt (Tres = 51.3 milliseconds; voxel, 2.5 × 5.0 × 10 mm³), rtMRSep (Tres = 48.8 milliseconds; voxel, 1.9 × 3.1 × 10 mm³), and rtHRSep (Tres = 48.3 milliseconds; voxel, 1.6 × 2.6 × 10 mm), were investigated on 20 volunteers using GRAPPA reconstruction with internal as well as separate reference scans. End-diastolic volume, end-systolic volume, ejection fraction, peak ejection rate, peak filling rate, and myocardial mass were evaluated for the left ventricle and compared with an electrocardiogram-triggered segmented readout cine protocol used as standard of reference. All studies were performed at 3 T. Phantom and in vivo data demonstrate that the combination of GRAPPA reconstruction with a separate reference scan provides an optimal compromise of image quality as well as spatial and temporal resolution. Functional values (P values) for the standard of reference, rtLRInt, rtMRSep, and rtHRSep end-diastolic volume were 141 ± 24 mL, 138 ± 21 mL, 138 ± 19 mL, and 128 ± 33 mL, respectively (P = 0.7, 0.7, 0.4); end-systolic volume, 55 ± 15 mL, 61 ± 14 mL, 58 ± 12 mL, and 55 ± 20 mL, respectively (P = 0.23, 0.43, 0.62); ejection fraction, 61% ± 5%, 57% ± 5%, 58% ± 4%, and 56% ± 8%, respectively (P = 0.01, 0.11, 0.06); peak ejection rate, 481 ± 73 mL/s, 425 ± 62 mL/s, 434 ± 67 mL/s, and 381 ± 86 mL/s, respectively (P = 0.03, 0.04, 0.01); peak filling rate, 555 ± 80 mL/s, 480 ± 70 mL/s, 500 ± 70 mL/s, and 438 ± 108 mL/s, respectively (P= 0.007, 0.05, 0.004); and myocardial mass, 137 ± 26 g, 141 ± 25 g, 141 ± 23 g, and 130 ± 31 g, respectively (P = 0.62, 0.54, 0.99). Using a separate reference scan and high acceleration factors up to R = 6, single-shot real-time cardiac imaging offers adequate temporal and spatial resolution for accurate assessment of global left ventricular function in free breathing with short examination times.

A method in near-surface estimation of air temperature (NEAT) in times following the satellite passing time using MODIS images

Neutron imaging is an ideal method to study water transport phenomena in proton exchange membrane fuel cells (PEMFCs). Neutrons readily penetrate aluminum, carbon and platinum, yet have a high sensitivity to hydrogenous materials, including water. In addition, unlike x-rays, neutrons are a non-destructive probe and do not alter the performance of PEMFCs due to irradiation. As such, there have been numerous studies that revealed the heat and mass transport phenomena in the flow fields and gas diffusion layers [1], freeze operation [2], impacts of carbon corrosion [3], and the impact of porosity in thick non-precious metal catalyst layers [4]. The primary limitation of neutron imaging is the achievable spatial and temporal resolutions. Current state of the art spatial resolution is about 20 µm, with a 20 minute image acquisition time. The spatial resolution is primarily limited by the range of the charged particles that are the result of the neutron capture reaction and are used to detect the neutron. This range is of order 5 µm for many common detector schemes. The temporal resolution is limited by the brightness of neutron sources, which are in general many orders of magnitude less bright than synchrotron x-ray sources; thus to have reasonable time resolution conventional neutron imaging requires the use of beam defining apertures that are of order 1 mm for high resolution imaging of PEMFCs, so that one cannot make use of geometric image magnification. We are pursuing three efforts to improve the achievable image spatial resolution and time resolution. The simplest approach to improving the spatial resolution is to place a narrow slit in front of the test section and scan it across the active area. The challenge of this method is to create a narrow, well-defined, completely opaque slit with opening area ~1 µm. KAERI and Pusan National University have devised a Gadox powder filling method that enables creating such slits, enabling imaging with 1 µm resolution [5]. By amplifying the scintillation light and using a high speed camera, it is possible to record individual neutron scintillation events and using a centroid algorithm determine the position of the neutron with high precision (less than 10 µm). The measured through-plane water content of a PEMFC is shown in figure 1 for the present state of both methods. The final effort is the development of a neutron microscope objective based on reflective neutron optics [6]. With a lens, one no longer requires the use of small beam defining apertures and thus the image temporal resolution can increase by at least a factor of 100. We will report on the progress of the first optics to reach 20 µm resolution and discuss the possibility of achieving 1 µm spatial resolution through neutron image magnification. References T.A Trabold et al, “Use of neutron imaging for proton exchange membrane fuel cell (PEMFC) performance analysis and design”, Handbook of fuel cells v. 5, 2009. R Mukundan et al, “Performance of PEM fuel cells at sub-freezing temperatures”, ECS Transactions 11 (1), 543-552, 2007.JD Fairweather et al, “Effects of cathode corrosion on through-plane water transport in proton exchange membrane fuel cells”, JECS 160 (9), F980-F993, 2013.D Spernjak et al, “Water Management in PEM Fuel Cells with Non-Precious Metal Catalyst Electrodes”, 228th ECS Meeting abstracts, p. 1540, 2015.J. Kim et al, “Fabrication and characterization of the source grating for visibility improvement of neutron phase imaging with gratings”, RSI 84(6):063705, 2013.D. Liu et al “Demonstration of achromatic cold-neutron microscope utilizing axisymmetric focusing mirrors”, Applied Physics Letters 102 (18), 183508, 2013. Figure 1 Comparison of the image quality of the current state of the art (a), the slit imaging method with 4 µm resolution (b), and the reconstructed image from centroiding (c). Figure 1

Global surveillance based on remote sensing always has a trade-off between spectral, temporal, and spatial resolutions of the sensor data. Since unique land cover information could be estimated at a good spatial scale than the temporal resolution, the Sentinel sensor, for example, has a revisit of 5 days and gathers images at a 10-20m spatial resolution. The Landsat sensor can only be revisited every 16 days with a spatial scale of 30 m. Fine temporal and spatial resolution data are essential for seasonable observation of dynamic agricultural, environmental, or ecological phenomena. This trade-off problem can be solved by using fusion methods based on the linear differences between images with fine (Sentinel) and coarse (Landsat) spatial resolution. As a result, an image with high spatial resolution can be predicted than acquired from a single Landsat-like sensor. The proposed work will combine Landsat and Sentinel data to bridge the gap between the Landsat image (spatial resolution,30m) and the Sentinel image (temporal resolution,10m or 20m). As a result, detailed investigations of the Earth’s surface and dynamics will be possible. Evaluation metrics – RMSE, SRE, and SSIM are used to compare the predicted image with the Sentinel image of the predicted date. The metrics analysis found that the predicted image is like the reference image with optimal values for all three-evaluation metrics.