scholarly journals High-Temporal-Resolution High-Spatial-Resolution Spaceborne SAR Based on Continuously Varying PRF

Sensors ◽  
2017 ◽  
Vol 17 (8) ◽  
pp. 1700 ◽  
Author(s):  
Zhirong Men ◽  
Pengbo Wang ◽  
Chunsheng Li ◽  
Jie Chen ◽  
Wei Liu ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
High Spatial Resolution ◽  
High Temporal Resolution

scholarly journals Simultaneous measurements of 3D wall shear stress and pulse wave velocity in the murine aortic arch

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Patrick Winter ◽  
Kristina Andelovic ◽  
Thomas Kampf ◽  
Jan Hansmann ◽  
Peter Michael Jakob ◽  
...  
Keyword(s):  
Shear Stress ◽  
Wall Shear Stress ◽  
Pulse Wave Velocity ◽  
Aortic Arch ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Pulse Wave ◽  
High Spatial Resolution ◽  
High Temporal Resolution ◽  
4D Flow

Abstract Purpose Wall shear stress (WSS) and pulse wave velocity (PWV) are important parameters to characterize blood flow in the vessel wall. Their quantification with flow-sensitive phase-contrast (PC) cardiovascular magnetic resonance (CMR), however, is time-consuming. Furthermore, the measurement of WSS requires high spatial resolution, whereas high temporal resolution is necessary for PWV measurements. For these reasons, PWV and WSS are challenging to measure in one CMR session, making it difficult to directly compare these parameters. By using a retrospective approach with a flexible reconstruction framework, we here aimed to simultaneously assess both PWV and WSS in the murine aortic arch from the same 4D flow measurement. Methods Flow was measured in the aortic arch of 18-week-old wildtype (n = 5) and ApoE−/− mice (n = 5) with a self-navigated radial 4D-PC-CMR sequence. Retrospective data analysis was used to reconstruct the same dataset either at low spatial and high temporal resolution (PWV analysis) or high spatial and low temporal resolution (WSS analysis). To assess WSS, the aortic lumen was labeled by semi-automatically segmenting the reconstruction with high spatial resolution. WSS was determined from the spatial velocity gradients at the lumen surface. For calculation of the PWV, segmentation data was interpolated along the temporal dimension. Subsequently, PWV was quantified from the through-plane flow data using the multiple-points transit-time method. Reconstructions with varying frame rates and spatial resolutions were performed to investigate the influence of spatiotemporal resolution on the PWV and WSS quantification. Results 4D flow measurements were conducted in an acquisition time of only 35 min. Increased peak flow and peak WSS values and lower errors in PWV estimation were observed in the reconstructions with high temporal resolution. Aortic PWV was significantly increased in ApoE−/− mice compared to the control group (1.7 ± 0.2 versus 2.6 ± 0.2 m/s, p < 0.001). Mean WSS magnitude values averaged over the aortic arch were (1.17 ± 0.07) N/m2 in wildtype mice and (1.27 ± 0.10) N/m2 in ApoE−/− mice. Conclusion The post processing algorithm using the flexible reconstruction framework developed in this study permitted quantification of global PWV and 3D-WSS in a single acquisition. The possibility to assess both parameters in only 35 min will markedly improve the analyses and information content of in vivo measurements.

scholarly journals An automatic contrail tracking algorithm

2010 ◽  
Vol 3 (4) ◽  
pp. 1089-1101 ◽  
Author(s):  
M. Vazquez-Navarro ◽  
H. Mannstein ◽  
B. Mayer
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
High Spatial Resolution ◽  
High Temporal Resolution ◽  
Cirrus Cloud ◽  
Linear Stage ◽  
Geostationary Satellites ◽  
Rapid Scan ◽  
Temporal And Spatial ◽  
Resolution Data

Abstract. A method designed to track the life cycle of contrail-cirrus using satellite data with high temporal and spatial resolution, from its formation to the final dissolution of the aviation-induced cirrus cloud is presented. The method follows the evolution of contrails from their linear stage until they are undistinguishable from natural cirrus clouds. Therefore, the study of the effect of aircraft-induced clouds in the atmosphere is no longer restricted to linear contrails and can include contrail-cirrus. The method takes advantage of the high spatial resolution of polar orbiting satellites and the high temporal resolution of geostationary satellites to identify the pixels that belong to an aviation induced cloud. The high spatial resolution data of the MODIS sensor is used for contrail detection, and the high temporal resolution of the SEVIRI sensor in the Rapid Scan mode is used for contrail tracking. An example is included in which the method is applied to the study of a long lived contrail over the bay of Biscay.

scholarly journals An automatic contrail tracking algorithm

2010 ◽  
Vol 3 (2) ◽  
pp. 1439-1494
Author(s):  
M. Vazquez-Navarro ◽  
H. Mannstein ◽  
B. Mayer
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
High Spatial Resolution ◽  
High Temporal Resolution ◽  
Cirrus Cloud ◽  
Linear Stage ◽  
Geostationary Satellites ◽  
Rapid Scan ◽  
Temporal And Spatial ◽  
Resolution Data

Abstract. A method designed to track the life cycle of contrail-cirrus using satellite data with high temporal and spatial resolution, from its formation to the final dissolution of the aviation-induced cirrus cloud is presented. The method follows the evolution of contrails from their linear stage until they are undistinguishable from natural cirrus clouds. Therefore, the study of the effect of aircraft-induced clouds in the atmosphere is no longer restricted to linear contrails and can include contrail-cirrus. The method takes advantage of the high spatial resolution of polar orbiting satellites and the high temporal resolution of geostationary satellites to identify the pixels that belong to an aviation induced cloud. The high spatial resolution data of the MODIS sensor is used for contrail detection, and the high temporal resolution of the SEVIRI sensor in the Rapid Scan mode is used for contrail tracking. An example is included in which the method is applied to the study of a long lived contrail over the bay of Biscay.

scholarly journals Dual-satellite (Sentinel-2 and Landsat 8) remote sensing of supraglacial lakes in Greenland

2018 ◽  
Author(s):  
Andrew G. Williamson ◽  
Alison F. Banwell ◽  
Ian C. Willis ◽  
Neil S. Arnold
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
High Spatial Resolution ◽  
Greenland Ice Sheet ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Supraglacial Lakes ◽  
Dual Sensor ◽  
Sentinel 2

Abstract. Although remote sensing is commonly used to monitor supraglacial lakes on the Greenland Ice Sheet, most satellite records must trade-off high spatial resolution for high temporal resolution (e.g. MODIS) or vice versa (e.g. Landsat). Here, we overcome this issue by developing and applying a dual-sensor method that can monitor changes to lake areas and volumes at high spatial resolution (10–30 m) with a frequent revisit time (~ 3 days). We achieve this by mosaicking imagery from the Landsat 8 OLI with imagery from the recently launched Sentinel-2 MSI for a ~ 12 000 km2 area of West Greenland in summer 2016. First, we validate a physically based method for calculating lake depths with Sentinel-2 by comparing measurements against those derived from the available contemporaneous Landsat 8 imagery; we find close correspondence between the two sets of values (R2 = 0.841; RMSE = 0.555 m). This provides us with the methodological basis for automatically calculating lake areas, depths and volumes from all available Landsat 8 and Sentinel-2 images. These automatic methods are incorporated into an algorithm for Fully Automated Supraglacial lake Tracking at Enhanced Resolution (FASTER). The FASTER algorithm produces time series showing lake evolution during the 2016 melt season, including automated rapid (≤ 4 day) lake-drainage identification. With the dual Sentinel-2-Landsat 8 record, we identify 184 rapidly draining lakes, many more than identified with either imagery collection alone (93 with Sentinel-2; 66 with Landsat 8), due to their inferior temporal resolution, or would be possible with MODIS, due to its omission of small lakes 

Dynamic Processes of Nanobubbles: Growth, Collapse, and Coalescence

2021 ◽  
Author(s):  
Heejun Choi ◽  
Calvin Li ◽  
G.P. 'Bud' Peterson
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
High Spatial Resolution ◽  
Characteristic Length ◽  
High Temporal Resolution ◽  
X Rays ◽  
Force Microscopy ◽  
Atomic Force ◽  
Theoretical Investigations ◽  
Microscopy Techniques

Abstract Abstract Nanobubbles are typically classified as gas/vapor phase cavities in an aqueous solution with a characteristic length of approximately 100 nanometers (nm). The theoretical lifetime of these nanobubbles has been estimated to be less than ~1 microsecond at a diameter of 100 nm based upon the Young-Laplace pressure, but experimental observations have been reported that indicate that they may exist for many hours, or even days. These nanobubbles can be generated by a number of different methods, such as solvent exchange, pressure and/or temperature variations, chemical reactions, or through the electron beam radiolysis of water. The imaging methods utilized to observe these nanobubbles have evolved from low temporal resolution/high spatial resolution, using atomic force microscopy (AFM); or low spatial resolution/high temporal resolution, using optical microscopy (x-rays); or finally, high spatial/high temporal resolution using more recent electron microscopy techniques. A review of the various methods utilized in the nucleation of nanobubbles and the different imaging technologies utilized, along with a summary of the most recent experimental and theoretical investigations of the dynamic behavior and processes of these nanobubbles, including nanobubble growth, nanobubble collapse, and nanobubble coalescence, are presented, discussed and summarized.

A four-channel ICCD framing camera with nanosecond temporal resolution and high spatial resolution

2021 ◽  
pp. 1-9
Author(s):  
Yuman Fang ◽  
Minrui Zhang ◽  
Junfeng Wang ◽  
Lehui Guo ◽  
Xueling Liu ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
High Spatial Resolution ◽  
Framing Camera

Large-scale Arctic sea ice motion from Sentinel-1 and the RADARSAT Constellation Mission

2021 ◽  
Author(s):  
Stephen Howell ◽  
Mike Brady ◽  
Alexander Komarov
Keyword(s):  
Sea Ice ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Large Scale ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
High Temporal Resolution ◽  
Sar Images ◽  
Sea Ice Extent ◽  
Arctic Sea

&lt;p&gt;As the Arctic&amp;#8217;s sea ice extent continues to decline, remote sensing observations are becoming even more vital for the monitoring and understanding of this process.&amp;#160; Recently, the sea ice community has entered a new era of synthetic aperture radar (SAR) satellites operating at C-band with the launch of Sentinel-1A in 2014, Sentinel-1B in 2016 and the RADARSAT Constellation Mission (RCM) in 2019. These missions represent a collection of 5 spaceborne SAR sensors that together can routinely cover Arctic sea ice with a high spatial resolution (20-90 m) but also with a high temporal resolution (1-7 days) typically associated with passive microwave sensors. Here, we used ~28,000 SAR image pairs from Sentinel-1AB together with ~15,000 SAR images pairs from RCM to generate high spatiotemporal large-scale sea ice motion products across the pan-Arctic domain for 2020. The combined Sentinel-1AB and RCM sea ice motion product provides almost complete 7-day coverage over the entire pan-Arctic domain that also includes the pole-hole. Compared to the National Snow and Ice Data Center (NSIDC) Polar Pathfinder and Ocean and Sea Ice-Satellite Application Facility (OSI-SAF) sea ice motion products, ice speed was found to be faster with the Senintel-1AB and RCM product which is attributed to the higher spatial resolution of SAR imagery. More sea ice motion vectors were detected from the Sentinel-1AB and RCM product in during the summer months and within the narrow channels and inlets compared to the NSIDC Polar Pathfinder and OSI-SAF sea ice motion products. Overall, our results demonstrate that sea ice geophysical variables across the pan-Arctic domain can now be retrieved from multi-sensor SAR images at both high spatial and temporal resolution.&lt;/p&gt;

scholarly journals Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

2019 ◽  
Vol 11 (11) ◽  
pp. 1266 ◽  
Author(s):  
Mingzheng Zhang ◽  
Dehai Zhu ◽  
Wei Su ◽  
Jianxi Huang ◽  
Xiaodong Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Kalman Filter ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Meteorological Data ◽  
High Temporal Resolution ◽  
Growth Monitoring ◽  
Landsat 8 ◽  
Data Set

Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals ( Δ P R K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.

scholarly journals Sensitivity of hydrological models to temporal and spatial resolutions of rainfall data

2019 ◽  
Vol 23 (6) ◽  
pp. 2647-2663 ◽  
Author(s):  
Yingchun Huang ◽  
András Bárdossy ◽  
Ke Zhang
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
Model Performance ◽  
Daily Rainfall ◽  
High Temporal Resolution ◽  
Dense Network ◽  
Spatially Distributed ◽  
Important Input ◽  
Temporal And Spatial ◽  
Improved Model

Abstract. Rainfall is the most important input for rainfall–runoff models. It is usually measured at specific sites on a daily or sub-daily timescale and requires interpolation for further application. This study aims to evaluate whether a higher temporal and spatial resolution of rainfall can lead to improved model performance. Four different gridded hourly and daily rainfall datasets with a spatial resolution of 1 km × 1 km for the state of Baden-Württemberg in Germany were constructed using a combination of data from a dense network of daily rainfall stations and a less dense network of sub-daily stations. Lumped and spatially distributed HBV models were used to investigate the sensitivity of model performance to the spatial resolution of rainfall. The four different rainfall datasets were used to drive both lumped and distributed HBV models to simulate daily discharges in four catchments. The main findings include that (1) a higher temporal resolution of rainfall improves the model performance if the station density is high; (2) a combination of observed high temporal resolution observations with disaggregated daily rainfall leads to further improvement in the tested models; and (3) for the present research, the increase in spatial resolution improves the performance of the model insubstantially or only marginally in most of the study catchments.

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