Low Spatial Resolution Data Research Articles

High-resolution population mapping based on SDGSAT-1 glimmer imagery and deep learning: a case study of the Guangdong-Hong Kong-Marco Greater Bay Area

ABSTRACT Accurate population mapping is crucial for disaster management, urban planning, etc. However, current methods using nighttime light (NTL) and gridded population datasets are limited by low spatial resolution and insufficient training data for complex models such as deep learning. These models do not adequately utilize spatial information in population mapping. To address these limitations, this study proposes a high-resolution population mapping method using the Sustainable Development Goals Science Satellite 1 (SDGSAT-1) glimmer imager data and deep learning. The method includes a sample generation strategy with multiple regression and multilevel screening to provide sufficient, high-quality samples for deep learning. A Fine Population mapping network (FinePop-net) is also developed to train regression models using image samples, capturing multi-scale features for model training. When applied to the Guangdong-Hong Kong-Macao Greater Bay Area with 40-meter resolution SDGSAT 1 glimmer imagery, the method significantly reduced the average absolute error and root-mean-square error by 9.35% and 11.44%, respectively, compared with those of the pixel-level learning methods. It also outperformed other population spatialization datasets and NTL data by over 30% and 10%, respectively, in terms of error reduction. The results highlight the method’s effectiveness and the value of SDGSAT-1 glimmer imagery for fine population spatialization.

A multi-scale and multi-physical coupling method for the transient characteristics of space nuclear reactor

In the future of deep space exploration, space nuclear reactors (SNRs) are essential because they can provide a robust, sustainable energy supply independent of the environment. SNRs have complex multi-physics coupling effects, and their systems involve many modules. The traditional method uses the lumped parameter model, or single-filed, which cannot accurately describe the transient characteristics and has low spatial resolution and poor data representation. Thus, developing a multi-dimensional, multi-physics coupled method to analyze and design SNRs is necessary. This work proposes a multi-dimensional, multi-physics coupled method for the SNRs. A transient coupling code for the space thermionic reactor TOPAZ-II based on OpenFOAM was developed. The reactor neutron physics model, multi-region heat transfer model, thermoelectric conversion model, electromagnetic pump model, and heat pipe radiator model are included. Different scale models are imported by the code, including a three-dimensional reactor model and a two-dimensional heat pipe. Furthermore, the experimental data from the V-71 test unit were used to verify the developed code. The shutdown and startup of the improved TOPAZ-II system are simulated using the developed code. The results show that the calculated results agree well with the experimental data. During the startup and shutdown processes, the material temperatures remained within acceptable limits, so it can be considered that the developed 2D/3D system analysis for SNRs has successfully achieved a high-dimensional simulation of transient characteristics, accurately capturing the spatial distribution of SNRs at different scales.

Multi-scale multi-temporal landscape pattern analysis using high spatial urban scene classification

ABSTRACTUrban landscape pattern analysis is conducive to understanding the land cover distribution and changes and promoting urban spatial layout rationality and sustainable development. Previous research has often used low and medium spatial resolution data to classify urban land cover at the pixel level and then analyse landscape pattern indices. However, the underlying semantic information provided by pixel classification needs to consider the spatial correlation of surface features, and it is challenging to obtain high-level semantic information on urban functional areas and analyse the relationship between human activities and land use. Leveraging the abundant spatial characteristics in high-resolution remote sensing images, high spatial urban scene classification can infer the feature categories and spatial relationships between features to realize higher-level image interpretation and obtain regional urban scene details. And it contributes to urban planning and decision-making, better meeting the different demands. Therefore, this study aims at a novel methodological framework for landscape pattern analysis of cross-temporal multi-scale urban scene classification. The results imply that: (1) The high-resolution images can make up for the lack of information on urban functional zones in low and medium resolution images; (2) Landscape pattern analysis based on scene classification can analyse urban spatial relationships more rationally than traditional research; and (3) With the continuous advancement of urbanization, the urban functional zoning becomes more apparent, and the dominant industries have changed significantly from industry to commerce and services. This study expands the analytical application range of landscape ecology and remote sensing technology and provides new inspirations for urban planning, environmental monitoring, ecological protection.

A dynamical downscaling method of groundwater storage changes using GRACE data

Study regionThe Northwest inland region of China. Study focusThe use of Gravity Recovery and Climate Experiment (GRACE) satellite data for assessing groundwater storage changes (GWSC) is rapidly expanding; however, dealing with such low spatial resolution data is always a challenge. In the present study, a dynamical downscaling method based on groundwater flow processes was proposed to simulate GWSC. Firstly, a hypothetical model constructed by MODFLOW package used to evaluate the efficiency of the developed model. Then, a part of the northwest inland region of China was taken as a real example to verify the applicability of the model. Finally, the downscaled results are used to estimate the groundwater storage changes in the study area. New hydrological insights for the regionThe spatiotemporal change of GWSC from the proposed model in a hypothetical conceptual model matched well with those from the MODFLOW package. The average correlation coefficient between downscaled GWSC and observed groundwater level is over 0.54, and downscaled GWSC showed the reasonable heterogeneity in the study area. From 2008–2011, the area with a decline in groundwater storage accounted for about 12% of the total area, while it increased to 21.8% from 2012 to 2016. The proposed model has the potential to be applied for the large-scale evaluation of GWSC and to improve the understanding of GWSC in remote areas.

Statistical retrieval of volcanic activity in long time series orbital data: Implications for forecasting future activity

Several high spatial resolution thermal infrared (TIR) missions are planned for the coming decade and their data will be crucial to constrain volcanic activity patterns throughout pre- and post-eruption phases. Foundational to these patterns is the subtle (1−2 K) thermal behavior, which is easily overlooked using lower spatial resolution data. In preparation for these new data, we conducted the first study using the entire twenty-two-year archive of higher spatial, lower temporal resolution TIR data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. This archive presents a unique opportunity to quantify low-magnitude temperature anomalies and small plumes over long time periods. We developed a new statistical algorithm to automatically detect the full range of thermal activity and applied it to >5000 ASTER scenes of five volcanoes with well-documented eruptions. Unique to this algorithm is its ability to use both day and night data, account for clouds, quantify accurate background temperatures, and dynamically scale depending on the anomaly size. Results improve upon those from the more commonly used lower spatial resolution data, despite the less frequent temporal coverage of ASTER, and show that high spatial resolution TIR data are equally as effective. Significantly, the smaller, subtle thermal detections served as precursory signals in ∼81% of eruptions, and the algorithm's results create a framework for classifying future eruptive styles.

The Digital Change Detection for Low Resolution Satellite Images in Weathering Estimation

The detection and estimation of weathering conditions have become a very important daily necessity in our life. For this purpose, several satellites of low resolution imagery were launched by the weathering and environmental agencies. The important weather paremeters are temperuter, wind direction, velocity, clould and humidity, etc. The low resolution images often deal with large-scale phenomena and the interpretation and projection of the produced data requires continuous development of tools and criteria. In this paper, the low spatial resolution data generated by the moderate resolution imaging spectroradiometer (MODIS) were used to monitor the cloud density and direction above Iraq and its neighboring countries for the period from 14-12-2021 to 23-12-2021. The MODIS reflectance and surface temperature (HDF format) data were used and processed to achieve the purpose of this research. The reflectance data were projected according to a sinusoidal system due to the presence of three Earth zones distributions (37, 38, and 39, N). The cloud density was estimated from the digital values and the wind direction was estimated from the bands animation. The direction of wind for the cloudy condition was from the North-East. The digital data processing included data preparation and selection, georeference (adding a projection system), features extraction, and displaying the final results. All data were evaluated using ENVI (Environment for Visualizing Images, ver. 4.5.)

Bias amplification and variance inflation in distributed lag models using low spatial resolution data

Bias amplification and variance inflation in distributed lag models using low spatial resolution data

A High-Resolution Remote-Sensing-Based Method for Urban Ecological Quality Evaluation

Urban ecological quality evaluation attracts more and more attention in urban land use and ecosystem planning optimization due to continuity problems from rapid urbanization and population growth. Remote sensing was always considering contribute to the evaluation. However, accurate and efficient evaluation of urban ecological quality is being challenged, as traditional remote-sensing-based methods were mainly based on low spatial resolution data, pixel-based land cover classification, and vegetation condition factors, and ignored object-oriented high spatial resolution classification and urban landscape pattern. Thus, method for urban ecological quality evaluation based on high-resolution remote sensing is greatly needed to support spatially explicit decision-making in urban planning. In this study, a novel high-resolution remote-sensing-based method based on six ecological indicators from vegetation conditions and landscape patterns was proposed to evaluate urban ecological quality. The six ecological indicators were derived from high-resolution remote sensing data using an object-oriented land cover classification. Factor analysis indicated that the sensitivity of landscape patterns to ecological quality is relatively weaken. Therefore, vegetation conditions and landscape patterns were used as two respective variables to generate a linear evaluation model, with their weights calculated from the loadings of factor analysis, to evaluation urban ecological quality. The results showed that the proposed linear model, considering both vegetation conditions and landscape patterns, is effective and trustworthy, and can provide more suitable support to urban land use and ecological planning.

Monsoon Effects on Chlorophyll-a, Sea Surface Temperature, and Ekman Dynamics Variability along the Southern Coast of Lesser Sunda Islands and Its Relation to ENSO and IOD Based on Satellite Observations

The Asian–Australian Monsoon (AAM), the El Nino-Southern Oscillation (ENSO), and the Indian Ocean Dipole (IOD) have been known to induce variability in ocean surface characteristics along the southern coast of Lesser Sunda Island (LSI). However, previous studies used low spatial resolution data and little Ekman dynamics analysis. This study aims to investigate the direct influence of AAM winds on ocean surface conditions and to determine how ENSO and IOD affect the ocean surface and depth with higher spatial resolution data. In addition, the variability in Ekman dynamics is also described along with the inconsistent relationship between wind and sea surface temperature (SST) in four different areas. The results indicate that persistent southeasterly winds are likely to induce low SST and chlorophyll-a blooms. Based on the interannual variability, the positive chlorophyll-a (up to 0.5 mg m−3) and negative SST (reaching −1.5 °C) anomalies observed in the El Nino of 2015 coincide with +IOD, which also corroborates positive wind stress and Ekman Mass Transport (EMT) anomalies. In contrast, the La Nina of 2010 coincides with -IOD, and positive SST and negative chlorophyll-a anomalies (more than 1.5 °C and −0.5 mg m−3 respectively) were observed. Furthermore, we found that southern coast of Java and Bali Island have a different generated mechanism that controls SST variability.

Predicting the location of larval habitats of Anopheles mosquitoes using remote sensing and soil type data

Malaria is a mosquito-borne infectious disease transmitted by the bite of Anopheles mosquitoes. Accurate and timely identification of Anopheles larval habitats and analysis of environmental factors affecting the formation and stability of these locations are very effective in the prevention and spread of malaria. In the absence of sufficient field observations and environmental parameters required in a suitable spatial coverage, remote sensing data can be effective in predicting the habitats of Anopheles mosquitoes. In this article, high-risk depressions that have the potential for Anopheles larval habitats had been identified by fusing a Digital Surface Model (DSM) extracted from very high spatial resolution aerial stereo images with land-use and soil type maps. land-useFinally, the high-risk map of malaria based on the prone larval habitats of Anopheles was created. To evaluate the results, 22 important depressions were identified using field observations in the 1.5 km buffer around Qaleh-Ganj city, Kerman Province, Iran. Of these 22 samples, 14 of them were stable for more than one month at temperatures above 30 °C and the rest were able to store water for three to four weeks. 12 out of 14 samples were consistent with the identified 130 high-risk depressions in the buffer range. Also, 19 of these 22 samples were compatible with the optimal depressions in the buffer range. By comparing the proposed method with previous methods based on data with medium and low spatial resolution or meteorological data, it was concluded that the correct and accurate seasonal and temporary position of Anopheles larval habitats depend on higher spatial resolution of remote sensing data. In addition, this study demonstrated that the use of vegetation and water indices cannot predict the exact location of all habitats of Anopheles. Because many of these habitats were temporary and these indices could not estimate and predict their exact location. The proposed method can be applied to search for suitable larval habitats of Anopheles around local residential areas, where neither medium and low spatial resolution data nor limited field observations can be used.

Regional Geology of the Hypanis Valles System, Mars

AbstractWe present a geomorphic map of the Hypanis Valles watershed and a geomorphic map of the Hypanis deposit region at its terminus. We mapped these two regions at different scales: 1:2,000,000 for the catchment map (−5° to 10°N and 300°–315°E) and 1:500,000 for the Hypanis deposit map (10°–13.0°N and 313°–316.5°E). Our mapping provides new morphologic insights beyond previous efforts which used lower spatial resolution data. We defined units based on morphology, albedo, thermal inertia, elevation, and spectral parameters. We propose that episodic volcanism and aqueous activity filled the Chryse basin from the early Noachian. Hypanis Valles was active during the Noachian, forming the Hypanis terminal deposits in the southern Chryse region. Hundreds of kilometer‐sized mounds and cones stratigraphically post‐date Hypanis fluvial deposition as these features appear to have erupted or effused through all other major map units. We propose sedimentary diapirism or mud volcanism may be responsible for these features, a hypothesis consistent with the compressional wrinkle ridge tectonism in a sedimentary basin. Future work could further investigate the formation of these cones and mounds and better assess their astrobiologic importance.

Spatiotemporal Fusion Modelling Using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria

The increasing availability and variety of global satellite products provide a new level of data with different spatial, temporal, and spectral resolutions; however, identifying the most suited resolution for a specific application consumes increasingly more time and computation effort. The region’s cloud coverage additionally influences the choice of the best trade-off between spatial and temporal resolution, and different pixel sizes of remote sensing (RS) data may hinder the accurate monitoring of different land cover (LC) classes such as agriculture, forest, grassland, water, urban, and natural-seminatural. To investigate the importance of RS data for these LC classes, the present study fuses NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16 days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16 days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, eight day)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions’ cloud or shadow gaps without losing spatial information. These eight synthetic NDVI STARFM products (2: high pair multiply 4: low pair) offer a spatial resolution of 10 or 30 m and temporal resolution of 1, 8, or 16 days for the entire state of Bavaria (Germany) in 2019. Due to their higher revisit frequency and more cloud and shadow-free scenes (S = 13, L = 9), Sentinel-2 (overall R2 = 0.71, and RMSE = 0.11) synthetic NDVI products provide more accurate results than Landsat (overall R2 = 0.61, and RMSE = 0.13). Likewise, for the agriculture class, synthetic products obtained using Sentinel-2 resulted in higher accuracy than Landsat except for L-MOD13Q1 (R2 = 0.62, RMSE = 0.11), resulting in similar accuracy preciseness as S-MOD13Q1 (R2 = 0.68, RMSE = 0.13). Similarly, comparing L-MOD13Q1 (R2 = 0.60, RMSE = 0.05) and S-MOD13Q1 (R2 = 0.52, RMSE = 0.09) for the forest class, the former resulted in higher accuracy and precision than the latter. Conclusively, both L-MOD13Q1 and S-MOD13Q1 are suitable for agricultural and forest monitoring; however, the spatial resolution of 30 m and low storage capacity makes L-MOD13Q1 more prominent and faster than that of S-MOD13Q1 with the 10-m spatial resolution.

Active Fire Mapping on Brazilian Pantanal Based on Deep Learning and CBERS 04A Imagery

Fire in Brazilian Pantanal represents a serious threat to biodiversity. The Brazilian National Institute of Spatial Research (INPE) has a program named Queimadas, which estimated from January 2020 to October 2020, a burned area in Pantanal of approximately 40,606 km2. This program also provides daily data of active fire (fires spots) from a methodology that uses MODIS (Aqua and Terra) sensor data as reference satellites, which presents limitations mainly when dealing with small active fires. Remote sensing researches on active fire dynamics have contributed to wildfire comprehension, despite generally applying low spatial resolution data. Convolutional Neural Networks (CNN) associated with high- and medium-resolution remote sensing data may provide a complementary strategy to small active fire detection. We propose an approach based on object detection methods to map active fire in the Pantanal. In this approach, a post-processing strategy is adopted based on Non-Max Suppression (NMS) to reduce the number of highly overlapped detections. Extensive experiments were conducted, generating 150 models, as five-folds were considered. We generate a public dataset with 775-RGB image patches from the Wide Field Imager (WFI) sensor onboard the China Brazil Earth Resources Satellite (CBERS) 4A. The patches resulted from 49 images acquired from May to August 2020 and present a spatial and temporal resolutions of 55 m and five days, respectively. The proposed approach uses a point (active fire) to generate squared bounding boxes. Our findings indicate that accurate results were achieved, even considering recent images from 2021, showing the generalization capability of our models to complement other researches and wildfire databases such as the current program Queimadas in detecting active fire in this complex environment. The approach may be extended and evaluated in other environmental conditions worldwide where active fire detection is still a required information in fire fighting and rescue initiatives.

Leaf water content estimation using top-of-canopy airborne hyperspectral data

Remotely sensed estimation of leaf water content (LWC) using optical data at early crop growth stage is important for identification of water-stressed plants. However, its accurate estimation is currently a major challenge due to the coarse spatial and spectral resolution of the available optical data, and the atmospheric impact on satellite-based remotely sensed data. Moreover, during early growth stages the canopy coverage is low, increasing the effect of the bare soil background on low spatial resolution data. Consequently, broadband optical data is insensitive to overtone frequencies of O-H stretching bonds of water molecules. Accordingly, this research developed a new model for estimating LWC based on newly identified, pure-pixel, water sensitive indices from high spatial resolution hyperspectral data. A hand-held field spectroradiometer and drone-based hyperspectral imager were used to collect temporal high spectral resolution hyperspectral data (Range: 400–1000 nm; Bandwidth: ~2.1 nm) at leaf level, together with destructively sampled leaves to measure their LWC using the oven-drying method. The spectroradiometer data were used to explore the wavelengths sensitive to vibrational overtone frequencies of O-H bonds of water molecules present in leaves. A total of seven water-sensitive wavelengths were identified, and corresponding normalised indices created for use with pure pixel narrowband hyperspectral data from vegetation. Farm scale maps of LWC were then created using drone-based hyperspectral data, based on minimum and maximum values of the above indices and ‘days after sowing’ information, through a gradient boost machine (GBM) model. The early growth stage maps of LWC were able to distinguish between water-stressed and well-irrigated plots with an R2 of 0.93 and RMSE of 1.6% (g/g).

Mapping Paddy Rice with Sentinel-1/2 and Phenology-, Object-Based Algorithm—A Implementation in Hangjiahu Plain in China Using GEE Platform

In tropical/subtropical monsoon regions, accurate rice mapping is hampered by the following factors: (1) The frequent occurrence of clouds in such areas during the rice-growing season interferes strongly with optical remote sensing observations; (2) The agro-landscape in such regions is fragmented and scattered. Rice maps produced using low spatial resolution data cannot well delineate the detailed distribution of rice, while pixel-based mapping using medium and high resolutions has significant salt-and-pepper noise. (3) The cropping system is complex, and rice has a rotation schedule with other crops. Therefore, the Phenology-, Object- and Double Source-based (PODS) paddy rice mapping algorithm is implemented, which consists of three steps: (1) object extraction from multi-temporal 10-m Sentinel-2 images where the extracted objects (fields) are the basic classification units; (2) specifying the phenological stage of transplanting from Savitzky–Golay filtered enhanced vegetation index (EVI) time series using the PhenoRice algorithm; and (3) the identification of rice objects based on flood signal detection from time-series microwave and optical signals of the Sentinel-1/2. This study evaluated the potential of the combined use of the Sentinel-1/2 mission on paddy rice mapping in monsoon regions with the Hangzhou-Jiaxin-Huzhou (HJH) plain in China as the case study. A cloud computing approach was used to process the available Sentinel-1/2 imagery from 2019 and MODIS images from 2018 to 2020 in the HJH plain on the Google Earth Engine (GEE) platform. An accuracy assessment showed that the resultant object-based paddy rice map has a high accuracy with a producer (user) accuracy of 0.937 (0.926). The resultant 10-m paddy rice map is expected to provide unprecedented detail, spatial distribution, and landscape patterns for paddy rice fields in monsoon regions.

Flat rotation curves of z ∼ 1 star-forming galaxies

ABSTRACT We investigate the shape of the rotation curves (RCs) of z ∼ 1 star-forming galaxies (SFGs) and compare them with local SFGs. For this purpose, we have used 344 galaxies from the K-band Multi-Object Spectrograph (KMOS) for Redshift One Spectroscopic Survey (KROSS). This sample covers the redshift range 0.57 ≤ z ≤ 1.04, the effective radii 0.69 ≤ Re [kpc] ≤ 7.76, and the stellar masses 8.7 ≤ log (M* [M⊙]) ≤ 11.32. Using 3DBAROLO, we extract the H α kinematic maps and corresponding RCs. The main advantage of 3DBAROLO is that it incorporates the beam smearing in the 3D observational space, which provide us with the intrinsic rotation velocity even in the low spatial resolution data. We have corrected the RCs for pressure support, which seems to be a more dominant effect than beam smearing in high-z galaxies. Only a combination of the three techniques (3D-kinematic modelling + 3D-beam smearing correction + pressure gradient correction) yields the intrinsic RC of an individual galaxy. Further, we present the co-added and binned RCs constructed out of 256 high-quality objects. We do not see any change in the shape of RCs with respect to the local SFGs. Moreover, we notice a significant evolution in the stellar-disc length (RD) of the galaxies as a function of their circular velocity. Therefore, we conclude that the stellar disc of SFGs evolves over cosmic time (from z ∼ 1) while the total mass stays constant (within ∼20 kpc).

Efficient downscaling of satellite oceanographic data with convolutional neural networks

Space-borne satellite radiometers measure Sea Surface Temperature (SST), which is pivotal to studies of air-sea interactions and ocean features. Under clear sky conditions, high resolution measurements are obtainable. But under cloudy conditions, data analysis is constrained to the available low resolution measurements. We assess the efficiency of Deep Learning (DL) architectures, particularly Convolutional Neural Networks (CNN) to downscale oceanographic data from low spatial resolution (SR) to high SR. With a focus on SST Fields of Bay of Bengal, this study proves that Very Deep Super Resolution CNN can successfully reconstruct SST observations from 15 km SR to 5km SR, and 5km SR to 1km SR. This outcome calls attention to the significance of DL models explicitly trained for the reconstruction of high SR SST fields by using low SR data. Inference on DL models can act as a substitute to the existing computationally expensive downscaling technique: Dynamical Downsampling. The complete code is available on this Github Repository.

Изучение острова тепла Москвы по данным космических снимков и мезоклиматического моделирования

Using the example of the Moscow region, a detailed comparison of the thermal regime of a highly urbanized territory was carried out according to in-situ data and different-scale satellite images. For the summer season, fundamental differences in the characteristics of the spatial variability of LST and air temperature in the daytime are revealed. It is shown that the relationship between the fields of these quantities is practically absent, primarily for urbanized territories, for which the effect of the heat island in the LST field is pronounced, and is practically not expressed in the air temperature field. However, for the winter season, some cases were identified with a more pronounced relationship between these values. The results obtained are independently confirmed by the results of numerical experiments with the COSMO model and the TERRA_URB parameterization with a 1 km grid step. At the same time, the prospects of using high spatial resolution images for model verification are shown: they made it possible to draw conclusions about the choice of the optimal configuration of model settings. For both summer and winter seasons, a comparison was made of high spatial resolution satellite data from Landsat with low spatial resolution data from MODIS. It is shown that, in spite of the generally good agreement between the two types of data, in some cases there are significant discrepancies between them due to differences in the values of the emissivity used and other factors that require more detailed analysis in further studies.

No Evidence for [C ii] Halos or High-velocity Outflows in z ≳ 6 Quasar Host Galaxies

Abstract We study the interstellar medium in a sample of 27 high-redshift quasar host galaxies at z ≳ 6, using the [C ii] 158 μm emission line and the underlying dust continuum observed at ∼1 kpc resolution with Atacama Large Millimeter Array. By performing uv-plane spectral stacking of both the high and low spatial resolution data, we investigate the spatial and velocity extent of gas and the size of the dust-emitting regions. We find that the average surface brightness profile of both the [C ii] and the dust continuum emission can be described by a steep component within a radius of 2 kpc and a shallower component with a scale length of 2 kpc, detected up to ∼10 kpc. The surface brightness of the extended emission drops below ∼1% of the peak at radius of ∼5 kpc, beyond which it constitutes 10%–20% of the total measured flux density. Although the central component of the dust continuum emission is more compact than that of the [C ii] emission, the extended components have equivalent profiles. The observed extended components are consistent with those predicted by hydrodynamical simulations of galaxies with similar infrared luminosities, where the dust emission is powered by star formation. The [C ii] spectrum measured in the mean uv-plane stacked data can be described by a single Gaussian, with no observable [C ii] broad-line emission (velocities in excess of ≳500 km s−1), which would be indicative of outflows. Our findings suggest that we are probing the interstellar medium and associated star formation in the quasar host galaxies up to radii of 10 kpc, whereas we find no evidence for halos or outflows.

Multivariate image fusion: A pipeline for hyperspectral data enhancement

Hyperspectral cameras provide high spectral resolution data, but their usual low spatial resolution when compared to color (RGB) instruments is still a limitation for more detailed studies. This article presents a simple yet powerful method for fusing co-registered high spatial and low spectral resolution image data – e.g. RGB – with low spatial and high spectral resolution data – Hyperspectral. The proposed method exploits the overlap in observed phenomena by the two cameras to create a model through least square projections. This yields two images: 1) A high-resolution image spatially correlated with the input RGB image but with more spectral information than just the 3 RGB bands. 2) A low-resolution image showing the spectral information what is spatially uncorrelated with the RGB image. We show results for semi-artificial benchmark datasets and a real-world application. Performance metrics indicate the method is well suited for data enhancement.