10.1063/5.0078644.10

In many fluid experiments, we can only obtain low-spatial high-temporal resolution flow images and high-spatial low-temporal resolution flow images due to the limitation of high-speed imaging systems. To solve this problem, we proposed a degradation and super-resolution attention model (D-SRA) using unsupervised machine learning to super-resolution reconstruct high resolution (HR) time-resolved fluid images from coarse data. Unlike the prior research to increase the resolution of coarse data artificially generated by simple bilinear down-sampling, our model that consists of a degradation neural network and a super-resolution neural network aims to learn the mappings between experimental low-resolution data and corresponding HR data. What is more, channel and spatial attention modules are also adopted in D-SRA to facilitate the restoration of abundant and critical details of flow fields. The proposed model is validated by two high-speed schlieren experiments of under-expanded impinging supersonic jets. The comprehensive capability of D-SRA is statistically analyzed based on the synthetic unpaired schlieren images. The spatial-resolution of coarse images can be successfully augmented by 42 times and 82 times with most physical details recovered perfectly, which outperforms the existing method. The D-SRA also exhibits considerable generalization and robustness against unknown-degenerated schlieren images. Moreover, the practicability of the proposed method is also further explored on real unpaired jets schlieren images. It is convincingly demonstrated that the present study successfully surpasses the performance limitations of high-speed cameras and has significant applications in various fluid experiments to obtain flow images with high spatial and temporal resolution.

Initialisation using high spatial, temporal and spectral resolution satellite observations

While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties, the temporal resolution of the data is rather low, which can be easily made worse by cloud contamination. In contrast, although Moderate Resolution Imaging Spectroradiometer (MODIS) can only achieve a spatial resolution of 250 m in its normalised difference vegetation index (NDVI) product, it has a high temporal resolution, covering the Earth up to multiple times per day. To combine the high spatial resolution and high temporal resolution of different data sources, a new method (Spatial and Temporal Adaptive Vegetation index Fusion Model [STAVFM]) for blending NDVI of different spatial and temporal resolutions to produce high spatial–temporal resolution NDVI datasets was developed based on Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). STAVFM defines a time window according to the temporal variation of crops, takes crop phenophase into consideration and improves the temporal weighting algorithm. The result showed that the new method can combine the temporal information of MODIS NDVI and spatial difference information of HJ-1 CCD NDVI to generate an NDVI dataset with both high spatial and high temporal resolution. An application of the generated NDVI dataset in crop biomass estimation was provided. An average absolute error of 17.2% was achieved. The estimated winter wheat biomass correlated well with observed biomass (R 2 of 0.876). We conclude that the new dataset will improve the application of crop biomass estimation by describing the crop biomass accumulation in detail. There is potential to apply the approach in many other studies, including crop production estimation, crop growth monitoring and agricultural ecosystem carbon cycle research, which will contribute to the implementation of Digital Earth by describing land surface processes in detail.

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

MODIS and ASTER synergy for characterizing thermal volcanic activity

Hyperspectral (HS) images obtained from space are useful for monitoring different natural phenomena on regional to global scales. The Environmental Mapping and Analysis Program (EnMAP) is a satellite recently launched by Germany to monitor the environment and explore the capabilities of hyperspectral sensors in the 420 and 2450 nm range of the spectrum. However, the data captured by the EnMAP mission have a ground sampling distance (GSD) of 30 m. This limits the use of the data for some applications that require higher spatial resolution (<10 m). This study examines the potential for improving the resolution of hyperspectral data using high resolution multispectral (MS) data obtained by Cubesats. Specifically, this work uses the data captured by the PlanetScope constellation, which has more than 150 CubeSats in low Earth orbit, with a high spatial and temporal resolution. The approach adopted leverages (1) the spectral capability of the hyperspectral EnMAP sensor, with a bandwidth of 6.5 nm in the visible and near infrared (VNIR) range (420–1000 nm) and 10 nm in the SWIR range (900–2450 nm), and (2) the spatial capability of the multispectral PlanetScope data, with a GSD of 3 meters, to enable significant spatial improvements due to its high spatial resolution. The main components of this work include: (i) area of interest clipping (ii) data co-registration, (iii) HS-MS data fusion, and (iv) quality assessments using the Jointly Spectral and Spatial Quality Index (QNR). In this study, a 2 km x 2 km area of interest was selected in the Malaucene region of France, where six state-of-the-art HS-MS fusion methods were evaluated: (1) fast multi-band image fusion algorithm (FUSE), (2) coupled nonnegative matrix factorization (CNMF), (3) smoothing filtered-based intensity modulation (SFIMHS), (4) maximum a posteriori stochastic mixing model (MAPSMM), (5) Hyperspectral Superresolution (HySure), and (6) generalized laplacian pyramid hypersharpening (GLPHS). Quality assessments of the enhanced data showed that high spectral and spatial fidelity are maintained, with the best performing fusion method being FUSE with a QNR of 0.625 followed by the MAPSMM method with a QNR of 0.604. Overall, this study advocates the benefits associated with the fusion of hyperspectral and multispectral data to obtain enhanced EnMAP data at 3 m GSD. 

There is an increasing demand for satellite remote sensing data with both high spatial and temporal resolution in many applications. But it still is a challenge to simultaneously improve spatial resolution and temporal frequency due to the technical limits of current satellite observation systems. To this end, much R&D efforts have been ongoing for years and lead to some successes roughly in two aspects, one includes super resolution, pan-sharpen etc. methods which can effectively enhance the spatial resolution and generate good visual effects, but hardly preserve spectral signatures and result in inadequate analytical value, on the other hand, time interpolation is a straight forward method to increase temporal frequency, however it increase little informative contents in fact. In this paper we presented a novel method to simulate high resolution time series data by combing low resolution time series data and a very small number of high resolution data only. Our method starts with a pair of high and low resolution data set, and then a spatial registration is done by introducing LDA model to map high and low resolution pixels correspondingly. Afterwards, temporal change information is captured through a comparison of low resolution time series data, and then projected onto the high resolution data plane and assigned to each high resolution pixel according to the predefined temporal change patterns of each type of ground objects. Finally the simulated high resolution data is generated. A preliminary experiment shows that our method can simulate a high resolution data with a reasonable accuracy. The contribution of our method is to enable timely monitoring of temporal changes through analysis of time sequence of low resolution images only, and usage of costly high resolution data can be reduces as much as possible, and it presents a highly effective way to build up an economically operational monitoring solution for agriculture, forest, land use investigation, environment and etc. applications.

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.

<p>As the Arctic’s sea ice extent continues to decline, remote sensing observations are becoming even more vital for the monitoring and understanding of this process.  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.</p>

There is a need to obtain higher specificity in the detection of breast lesions using MRI. To address this need, Dynamic Contrast-Enhanced (DCE) MRI has been combined with other structural and functional MRI techniques. Unfortunately, owing to time constraints structural images at ultra-high spatial resolution can generally not be obtained during contrast uptake, whereas the relatively low spatial resolution of functional imaging (e.g. diffusion and perfusion) limits the detection of small lesions. To be able to increase spatial as well as temporal resolution simultaneously, the sensitivity of MR detection needs to increase as well as the ability to effectively accelerate the acquisition. The required gain in signal-to-noise ratio (SNR) can be obtained at 7T, whereas acceleration can be obtained with high-density receiver coil arrays. In this case, morphological imaging can be merged with DCE-MRI, and other functional techniques can be obtained at higher spatial resolution, and with less distortion [e.g. Diffusion Weighted Imaging (DWI)]. To test the feasibility of this concept, we developed a unilateral breast coil for 7T. It comprises a volume optimized dual-channel transmit coil combined with a 30-channel receive array coil. The high density of small coil elements enabled efficient acceleration in any direction to acquire ultra high spatial resolution MRI of close to 0.6 mm isotropic detail within a temporal resolution of 69 s, high spatial resolution MRI of 1.5 mm isotropic within an ultra high temporal resolution of 6.7 s and low distortion DWI at 7T, all validated in phantoms, healthy volunteers and a patient with a lesion in the right breast classified as Breast Imaging Reporting and Data System (BI-RADS) IV.

In recent years, ultra-high field functional MRI has allowed researchers to study cortical activity at high spatiotemporal resolution. Advancements in technology have made it possible to perform fMRI of cortical laminae, which is crucial for understanding and mapping of local circuits and overall brain function. Unlike invasive electrophysiology, fMRI provides a non-invasive approach to studying human and animal brain function. However, achieving high spatial resolution has often meant sacrificing temporal resolution. In contrast, line-scanning fMRI maintains both high spatial and temporal resolution, and has been successfully applied to animals to detect laminar differences of the hemodynamic response. Although this method has been extended to human brain imaging in initial studies, staying within SAR safety limits while maintaining a well-defined saturation profile at a short TR is a major challenge. We present a method for gradient-echo-based human line-scanning that uses four saturation regions to achieve a line with narrow FWHM (3.9 mm) at high spatiotemporal resolution (voxel size 0.39 x 3.0 x 3.0 mm3, TR = 250 ms). We demonstrate its use for laminar fMRI by measuring laminar time courses in the hand knob of the primary human motor cortex during a finger-tapping task. Our findings indicate differences in the onset and temporal characteristics of the hemodynamic response across cortical layers. Deeper layers exhibited distinct temporal dynamics compared with the gray matter near the cortical surface. Specifically, the BOLD response reached 95% of the maximum amplitude earlier than the superficial layers, and demonstrated a faster return to baseline after stimulus offset. We demonstrate that line-scanning fMRI offers a valuable tool for investigating recordings at a very high temporal and spatial resolution and could help advance our understanding of the mechanistic nature of the BOLD response.

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.

The accurate measurement of airflow patterns at high spatial and temporal resolutions is of great importance for building design and operation. Microelectromechanical Systems (MEMS) technology can be used to develop miniaturized airflow sensors capable of detecting both airflow speed and direction at high temporal resolution. Wireless sensor networks (WSNs) are used extensively for large-scale monitoring at high spatial resolution. Thus, the integration of MEMS and WSN technologies enables the measurement of airflow patterns at high spatial and temporal resolution. In this work, we developed a prototype MEMS-based airflow sensor network and deployed it in a building environment.

Coastal ocean areas are very dynamic regions subject to strong anthropogenic pressure (e.g. industry, tourism, renewable energies, population). Satellite data constitute a unique tool to study and monitor these complex areas at high spatial and temporal resolutions. While “traditional” ocean colour satellites like Sentinel-3 provide daily temporal resolution, their sensors do not measure at the spatial resolution needed to correctly resolve complex coastal dynamics. On the other hand, while high spatial resolution sensors, like the MSI onboard Sentinel-2 (10m - 60m resolution), are able to resolve these small scales, their revisit time is far from optimal (2-5 days for the Sentinel-2 A & B tandem). Additionally, both high spatial resolution datasets and traditional ones are hindered by the presence of clouds, resulting in a large amount of missing data. Given the high complementarity of these two measurement strategies (Sentinel-3 and Sentinel-2), we present a methodology to derive gap-filled multi resolution ocean colour products from the synergistic use of Sentinel-2 and Sentinel-3 data. Applying DINEOF (Data Interpolating Empirical Orthogonal Functions), we exploit and combine both the high-resolution spatial variability information contained in Sentinel-2 products as well as the high temporal information of Sentinel-3. Both Sentinel-2 and Sentinel-3 products are generated using the Copernicus Marine High-Resolution processor, which uses automated switching algorithms adapted to the local water conditions (at the pixel level) to retrieve optimal remote sensing spectra and water quality variables. With this approach, we address the high variability of different water types with small scale changes. The combined Sentinel-2 and Sentinel-3 products consist of Chlorophyll-a concentration and turbidity retrieved through a multi-algorithm approach with optimized quality flagging. An analysis of turbidity and chlorophyll daily data at 100m resolution in the northern Adriatic Sea and the Belgian coastal zone will be presented, alongside an analysis of the spatial scales resolved by the original and merged DINEOF datasets.

Runtime and memory requirements for typical formulations of energy system models increase non-linearly with resolution, computationally constraining large-scale models despite state-of-the-art solvers and hardware. This scaling paradigm requires omission of detail which can affect key outputs to an unknown degree. Recent algorithmic innovations employing decomposition have enabled linear increases in runtime and memory use as temporal resolution increases. Newly tractable, higher resolution systems can be compared with lower resolution configurations commonly employed today in academic research and industry practice, providing a better understanding of the potential biases or inaccuracies introduced by these abstractions. We employ a state-of-the art electricity system planning model and new high-resolution systems to quantify the impact of varying degrees of spatial, temporal, and operational resolution on results salient to policymakers and planners. We find models with high spatial and temporal resolution result in more realistic siting decisions and improved emissions, reliability, and price outcomes. Errors are generally larger in systems with low spatial resolution, which omit key transmission constraints. We demonstrate that high temporal resolution cannot overcome biases introduced by low spatial resolution, and vice versa. While we see asymptotic improvements to total system cost and reliability with increased resolution, other salient outcomes such as siting accuracy and emissions exhibit continued improvement across the range of model resolutions considered. We conclude that modelers should carefully balance resolution on spatial, temporal, and operational dimensions and that novel computational methods enabling higher resolution modeling are valuable and can further improve the decision support provided by this class of models.