Observational Capabilities Research Articles

Three-dimensional time-dependent convection model for asteroseismology

Context. Due to an ill-depicting model of the convective layers below the photosphere in 1D stellar models (structural contribution) and/or a misrepresentation of the coupling between convection and oscillations (modal contribution), a well-known deviation appears between observed and theoretical frequencies, which grows towards high frequencies; the so-called surface effects. While satisfying solutions have been found regarding the structural contribution, the accurate modeling of the modal effect still represents a challenge. Alongside the frequency, the interaction between convection and oscillations also impacts the damping rate of the modes and forms an important part of the driving mechanism behind the stellar oscillations of low-mass stars. With increasing observational capabilities at our disposal with Kepler and TESS, shortcomings in modeling constitute the main limitation to accurate seismic probing of solar-like and red giant stars. Aims. We present the formalism of an approach that changes the current paradigm by addressing three-dimensional space. This new formalism consists in an original nonadiabatic 3D time-dependent convection model for asteroseismology. Methods. We aim to keep the entire 3D structure of the astrophysical flow in these superficial layers in order to fully account for the nature of turbulence in our model via the use of advanced hydrodynamic simulation. We use the perturbative approach and introduce a spectral decomposition approach that results in an entirely new formalism describing standing waves in 3D. This formalism is set to solve the quasi-radial global nonadiabatic oscillation equations in a full 3D framework. Results. Based on physical assumptions, we establish an eigenvalue problem describing the 3D quasi-radial global nonadiabatic stellar oscillation. We also provide a prescription for its numerical resolution alongside a proposed iteration method for our formalism. Finally, we derive the peculiar 3D work integral and establish the expression of the damping rate. We show how our formalism offers the possibility to probe the complex structure of stars and is able to precisely locate regions of the driving and damping of the modes as well as their physical origin.

GEOSatDB: global civil earth observation satellite semantic database

ABSTRACT Satellite remote sensing, characterized by extensive coverage, frequent revisits, and continuous monitoring, provides essential data support for addressing global challenges. Over the past six decades, thousands of Earth observation satellites and sensors have been deployed worldwide. These valuable Earth observation assets are contributed independently by various nations and organizations employing diverse methodologies. This poses a significant challenge in effectively discovering global Earth observation resources and realizing their full potential. In this paper, we describe the development of GEOSatDB, the most complete semantic database of civil Earth observation satellites developed based on a unified ontology model. A similarity matching method is used to integrate satellite information and a prompt strategy is used to extract unstructured sensor information. The resulting semantic database contains 127,949 semantic statements for 2,340 remote sensing satellites and 1,021 observation sensors. The global Earth observation capabilities of 195 countries worldwide have been analyzed in detail, and a concrete use case along with an associated query demonstration is presented. This database provides significant value in effectively facilitating the semantic understanding and sharing of Earth observation resources.

Opportunities and limits of lunar gravitational-wave detection.

A new era of lunar exploration has begun with participation of all major space agencies. This activity brings opportunities for revolutionary science experiments and observatories on the Moon. The idea of a lunar gravitational-wave detector wasalready proposed during the Apollo programme. The key characteristic of the Moon is that it is seismically extremely quiet. It was also pointed out that the permanently shadowed regions at the lunar poles provide ideal conditions for gravitational-wave detection. In recent years, three different detector concepts were proposed with varying levels of technological complexity and science potential. In this paper, we confront the three concepts in terms of their observational capabilities based on a first more detailed modelling of instrumental noise. We identify important technological challenges and potential show-stoppers. This article is part of a discussion meeting issue 'Astronomy from the Moon: the next decades (part 2)'.

Mesoscale structure of the atmospheric boundary layer across a natural roughness transition

The structure and intensity of turbulence in the atmospheric boundary layer (ABL) drive fluxes of sediment, contaminants, heat, moisture, and CO[Formula: see text] at the Earth's surface. Where ABL flows encounter changes in roughness-such as cities, wind farms, forest canopies, and landforms-a new mesoscopic flow scale is introduced: the internal boundary layer (IBL), which represents a near-bed region of transient flow adjustment that develops over kilometers. Measurement of this new mesoscopic scale lies outside present observational capabilities of ABL flows, and simplified models fail to capture the sensitive dependence of turbulence on roughness geometry. Here, we use large-eddy simulations, run over high-resolution topographic data and validated against field observations, to examine the structure of the ABL across a natural roughness transition: the emergent sand dunes at White Sands National Park. We observe that development of the IBL is triggered by the abrupt transition from smooth playa surface to dunes; however, continuous changes in the size and spacing of dunes over several kilometers influence the downwind patterns of boundary stress and near-bed turbulence. Coherent flow structures grow and merge over the entire [Formula: see text]10 km distance of the dune field and modulate the influence of large-scale atmospheric turbulence on the bed. Simulated boundary stresses in the developing IBL counter existing expectations and explain the observed downwind decrease in dune migration, demonstrating a mesoscale coupling between flow and form that governs landscape dynamics. More broadly, our findings demonstrate the importance of resolving both turbulence and realistic roughness for understanding fluid-boundary interactions in environmental flows.

ADF-Net: An Attention-Guided Dual-Branch Fusion Network for Building Change Detection near the Shanghai Metro Line Using Sequences of TerraSAR-X Images

Building change detection (BCD) plays a vital role in city planning and development, ensuring the timely detection of urban changes near metro lines. Synthetic Aperture Radar (SAR) has the advantage of providing continuous image time series with all-weather and all-time capabilities for earth observation compared with optical remote sensors. Deep learning algorithms have extensively been applied for BCD to realize the automatic detection of building changes. However, existing deep learning-based BCD methods with SAR images suffer limited accuracy due to the speckle noise effect and insufficient feature extraction. In this paper, an attention-guided dual-branch fusion network (ADF-Net) is proposed for urban BCD to address this limitation. Specifically, high-resolution SAR images collected by TerraSAR-X have been utilized to detect building changes near metro line 8 in Shanghai with the ADF-Net model. In particular, a dual-branch structure is employed in ADF-Net to extract heterogeneous features from radiometrically calibrated TerraSAR-X images and log ratio images (i.e., difference images (DIs) in dB scale). In addition, the attention-guided cross-layer addition (ACLA) blocks are used to precisely locate the features of changed areas with the transformer-based attention mechanism, and the global attention mechanism with the residual unit (GAM-RU) blocks is introduced to enhance the representation learning capabilities and solve the problems of gradient fading. The effectiveness of ADF-Net is verified using evaluation metrics. The results demonstrate that ADF-Net generates better building change maps than other methods, including U-Net, FC-EF, SNUNet-CD, A2Net, DMINet, USFFCNet, EATDer, and DRPNet. As a result, some building area changes near metro line 8 in Shanghai have been accurately detected by ADF-Net. Furthermore, the prediction results are consistent with the changes derived from high-resolution optical remote sensing images.

Harmonic suppression of active disturbance rejection control for virtual synchronous generators

SummaryVirtual synchronous generators (VSGs) are comprised of an outer power loop and inner cascaded control loops. The dynamic performance of the output interface is severely degraded when microgrid loads exhibit variability and nonlinearity. Therefore, robustness against load disturbances must be possessed by any voltage control scheme. In this paper, a proportional resonance‐linear active disturbance rejection control (PR‐LADRC) scheme based on an improved linear extended state observer (ILESO) is proposed for VSG voltage control. In comparison with the traditional linear extended state observer (LESO), the differential state variable of the output voltage is utilized to increase the observation bandwidth; consequently, the proposed ILESO can effectively overcome the disadvantage of phase lag when tracking disturbances. Additionally, a proportional resonance controller is incorporated into the LESO feedforward channel to suppress voltage harmonics. The proposed scheme enhances the disturbance observation bandwidth compared to traditional strategies, resulting in a substantial improvement in both the disturbance observation capability of LADRC and the harmonic suppression ability of the VSG system. Theoretical analysis and simulations are conducted to validate the effectiveness of the proposed PR‐LADRC approach.

Bridging New Observational Capabilities and Process-Level Simulation: Insights into Aerosol Roles in the Earth System

Abstract The spatial distribution of ambient aerosol particles significantly impacts aerosol–radiation–cloud interactions, which contribute to the largest uncertainty in global anthropogenic radiative forcing estimations. However, the atmospheric boundary layer and lower free troposphere have not been adequately sampled in terms of spatiotemporal resolution, hindering a comprehensive characterization of various atmospheric processes and impeding our understanding of the Earth system. To address this research data gap, we have leveraged the development of uncrewed aerial systems (UAS) and advanced measurement techniques to obtain mesoscale spatial data on aerosol microphysical and optical properties around the U.S. Southern Great Plains (SGP) atmospheric observatory. Our study also benefits from state-of-the-art laboratory facilities that include three-dimensional molecular imaging techniques enabled by secondary ion mass spectrometry and nanogram-level chemical composition analysis via micronebulization aerosol mass spectrometry. Through our study, we have developed a framework for observation–modeling integration, enabling an examination of how various assumptions about the organic–inorganic components mixing state, inferred from chemical analysis, affect clouds and radiation in observation-constrained model simulations. By integrating observational constraints (derived from offline chemical analysis of the aerosol surface using collected samples) with in situ UAS observations, we have identified a prominent role of organic-enriched nanometer layers located at the surface of aerosol particles in determining profiles of aerosol optical and hygroscopic properties over the SGP observatory. Furthermore, we have improved the agreement between predicted clouds and ground-based cloud lidar measurements. This UAS–model–laboratory integration exemplifies how these new advanced capabilities can significantly enhance our understanding of aerosol–radiation–cloud interactions.

Hydrologic Consistency of Multi-Sensor Drought Observations in Forested Environments

Drought can have significant impacts on forests, with long periods of water scarcity leading to water stress in trees and possible damages to their eco-physiological functions. Satellite-based remote sensing offers a valuable tool for monitoring and assessing drought conditions over large and remote forested regions. The objective of this study is to evaluate the hydrological consistency in the context of drought of precipitation, soil moisture, evapotranspiration, and land surface temperature observations against in situ measurements in a number of well-monitored sites in New Zealand. Results showed that drought indicators were better captured from soil moisture observations compared to precipitation satellite observations. Nevertheless, we found statistically significant causality relationships between the multi-sensor satellite observations (median p-values ranging from 0.001 to 0.019), with spatial resolution appearing to be an important aspect for the adequate estimation of drought characteristics. Understanding the limitations and capabilities of satellite observations is crucial for improving the accuracy of forest drought monitoring, which, in turn, will aid in sustainable forest management and the development of mitigation and adaptation strategies in the face of changing climate conditions.

Water Quality Estimation Using Gaofen-2 Images Based on UAV Multispectral Data Modeling in Qinba Rugged Terrain Area

This study presents an innovative method for large-scale surface water quality assessment in rugged terrain areas, specifically tailored for regions like the Qinba Mountains. The approach combines the use of high-resolution (10 cm) multispectral data acquired by unmanned aerial vehicles (UAVs) with synchronized ground sampling and 1 m resolution multispectral imagery from China’s Gaofen-2 satellite. By integrating these technologies, the study aims to capitalize on the convenience and synchronized observation capabilities of UAV remote sensing, while leveraging the broad coverage of satellite remote sensing to overcome the limitations of each individual technique. Initially, a multispectral estimation model is developed for key water quality parameters, including chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP), utilizing data from UAVs and coordinated ground samples. Subsequently, a comparison is made between the spectral band ratios (R/G and NIR/G) obtained from the UAV data and those from the Gaofen-2 satellite data, revealing a substantial similarity. Ultimately, this integrated methodology is successfully employed in monitoring water quality across a vast area, particularly along the midstream of the Hanjiang River in the Qinba Mountain region. The results underscore the feasibility, advantages, improved efficiency, and enhanced accuracy of this approach, making it particularly suitable for large-scale water quality monitoring in mountainous terrain. Furthermore, this method reduces the burden associated with traditional ground-based spectral acquisitions, paving the way for a more practical and cost-effective solution in monitoring vast water bodies.

Principled Limitations on Self-Representation for Generic Physical Systems.

The ideas of self-observation and self-representation, and the concomitant idea of self-control, pervade both the cognitive and life sciences, arising in domains as diverse as immunology and robotics. Here, we ask in a very general way whether, and to what extent, these ideas make sense. Using a generic model of physical interactions, we prove a theorem and several corollaries that severely restrict applicable notions of self-observation, self-representation, and self-control. We show, in particular, that adding observational, representational, or control capabilities to a meta-level component of a system cannot, even in principle, lead to a complete meta-level representation of the system as a whole. We conclude that self-representation can at best be heuristic, and that self models cannot, in general, be empirically tested by the systems that implement them.

Multiresolution soil moisture products based on a spatially adaptive estimation model and CYGNSS data

ABSTRACT The estimation of soil moisture (SM) utilizing the data from the Cyclone Global Navigation Satellite System (CYGNSS) has attracted significant interest in recent times. However, CYGNSS’ inherent capability of variable resolution has not been fully exploited, often resulting in a loss of detailed spatial information in the raw data. In this paper, a novel downscaling scheme tailored for CYGNSS data is introduced to yield a “self-adjusting adaptive resolution” SM product, which dynamically varies the resolution of SM estimates based on the available CYGNSS data resolution at different geographic locations. Initially, a direct quantitative relationship is established between the key CYGNSS parameters reflecting SM variations and the reference SM from the Soil Moisture Active Passive (SMAP) mission with a coarse resolution of 36 km. This model is then applied to CYGNSS observations with resolutions down to 3 km to generate high-resolution, self-adjusting SM estimates that better conserve the fine-scale information linked to the original CYGNSS data. Extensive experimental results with error ratio diagrams show that the advanced geographically weighted regression (GWR)-based SM estimation method outperforms other competing estimation models and better retains localized spatial relationships and patterns. This study underscores the potential of CYGNSS as a novel and robust independent data source capable of delivering fine-resolution SM estimations by harnessing its unique multiresolution observational capability.

A Method for Retrieving Cloud Microphysical Properties Using Combined Measurement of Millimeter-Wave Radar and Lidar

Clouds are an important component of weather systems and are difficult to effectively characterize using current climate models and estimation of radiative forcing. Due to the limitations in observational capabilities, it remains difficult to obtain high-spatiotemporal-resolution, continuous, and accurate observations of clouds. To overcome this issue, we propose a novel and practical combined retrieval method using millimeter-wave radar and lidar, which enables the microphysical properties of thin liquid water clouds, such as cloud droplet effective radius, number concentration, and liquid water content, to be retrieved. This method was utilized to analyze the clouds observed at the Shanghai World Expo Park and was validated through synchronous observations with a microwave radiometer. Furthermore, the most suitable extinction backscatter ratio was determined through sensitivity analysis. This study provides vertical distributions of cloud microphysical properties with a time resolution of 1 min and a spatial resolution of 30 m, demonstrating the scientific potential of this combined retrieval method.

The Need for Multi‐Century Projections of Sea Level Rise

AbstractThe latest assessment report of the Intergovernmental Panel on Climate Change (IPCC) provided scenario‐based local sea level projections to 2150 and characterized the long‐term committed global mean sea level rise on 2,000‐ and 10,000‐year time horizons associated with peak surface warming levels. Turner et al. build on the scientific assessment of the IPCC to provide time‐continuous projections of future sea level rise to 2500. These projections fill an important knowledge gap to help inform coastal decision‐making processes and more fully quantify the benefits of mitigation actions in terms of limiting future sea level rise. However, limited understanding of ice instability processes remains a key scientific challenge and improved observational and modeling capability are critical to reducing uncertainties and monitoring the trajectory of observed change.

Observing the LMC with APEX: Signatures of large-scale feedback in the molecular clouds of 30 Doradus

Context. Stellar feedback plays a crucial role in star formation and the life cycle of molecular clouds. The intense star formation region 30 Doradus, which is located in the Large Magellanic Cloud (LMC), is a unique target for detailed investigation of stellar feedback owing to the proximity of the hosting galaxy and modern observational capabilities that together allow us to resolve individual molecular clouds – nurseries of star formation. Aims. We study the impact of large-scale feedback on the molecular gas using the new observational data in the 12CO(3 − 2) line obtained with the APEX telescope. Methods. Our data cover an unprecedented area of 13.8 sq. deg. of the LMC disc with a spatial resolution of 5 pc and provide an unbiased view of the molecular clouds in the galaxy. Using these data, we located molecular clouds in the disc of the galaxy, estimated their properties, such as the areal number density, relative velocity and separation, width of the line profile, CO line luminosity, size, and virial mass, and compared these properties of the clouds of 30 Doradus with those in the rest of the LMC disc. Results. We find that, compared with the rest of the observed molecular clouds in the LMC disc, those in 30 Doradus show the highest areal number density; they are spatially more clustered, they move faster with respect to each other, and they feature larger linewidths. In parallel, we do not find statistically significant differences in such properties as the CO line luminosity, size, and virial mass between the clouds of 30 Doradus and the rest of the observed field. Conclusions. We interpret our results as signatures of gas dispersal and fragmentation due to high-energy large-scale feedback.

Modular Multiplatform Compatible Air Measurement System (MoMuCAMS): a new modular platform for boundary layer aerosol and trace gas vertical measurements in extreme environments

Abstract. The Modular Multiplatform Compatible Air Measurement System (MoMuCAMS) is a newly developed in situ aerosol and trace gas measurement platform for lower-atmospheric vertical profiling. MoMuCAMS has been primarily designed to be attached to a Helikite, a rugged tethered balloon type that is suitable for operations in cold and windy conditions. The system addresses the need for detailed vertical observations of atmospheric composition in the boundary layer and lower free troposphere, especially in polar and alpine regions. The MoMuCAMS encompasses a box that houses instrumentation, a heated inlet, a single-board computer to transmit data to the ground for in-flight decisions and a power distribution system. The enclosure can accommodate various combinations of instruments within its weight limit (e.g., 20 kg for a 45 m3 balloon). This flexibility represents a unique feature, allowing for the study of multiple aerosol properties (number concentration, size distribution, optical properties, chemical composition and morphology), as well as trace gases (e.g., CO, CO2, O3, N2O) and meteorological variables (e.g., wind speed and direction, temperature, relative humidity, pressure). Different instrumental combinations are therefore possible to address the specific scientific focus of the observations. It is the first tethered-balloon-based system equipped with instrumentation providing a size distribution for aerosol particles within a large range, i.e., from 8 to 3370 nm, which is vital to understanding atmospheric processes of aerosols and their climate impacts through interaction with radiation and clouds. Here we present a characterization of the specifically developed inlet system and previously unreported instruments, most notably the miniaturized scanning electrical mobility spectrometer and a near-infrared carbon monoxide monitor. As of December 2022, MoMuCAMS has been tested during two field campaigns in the Swiss Alps in winter and fall 2021. It was further deployed in Fairbanks, Alaska, USA, in January–February 2022, as part of the ALPACA (Alaskan Layered Pollution and Chemical Analysis) campaign and in Pallas, Finland, in September–October 2022, as part of the PaCE2022 (Pallas Cloud Experiment) study. Three cases from one of the Swiss Alpine studies are presented to illustrate the various observational capabilities of MoMuCAMS. Results from the first two case studies illustrate the breakup of a surface-based inversion layer after sunrise and the dilution of a 50–70 m thick surface layer. The third case study illustrates the capability of the system to collect samples at a given altitude for offline chemical and microscopic analysis. Overall, MoMuCAMS is an easily deployable tethered-balloon payload with high flexibility, able to cope with the rough conditions of extreme environments. Compared to uncrewed aerial vehicles (drones) it allows for observation of aerosol processes in detail over multiple hours, providing insights into their vertical distribution and processes, e.g., in low-level clouds, that were difficult to obtain beforehand.

Low-level Arctic clouds: a blind zone in our knowledge of the radiation budget

Abstract. Quantifying the role of clouds in the earth's radiation budget is essential for improving our understanding of the drivers and feedback mechanisms of climate change. This holds in particular for the Arctic, the region currently undergoing the most rapid changes. This region, however, also poses significant challenges to remote-sensing retrievals of clouds and radiative fluxes, introducing large uncertainties in current climate data records. In particular, low-level stratiform clouds are common in the Arctic but are, due to their low altitude, challenging to observe and characterize with remote-sensing techniques. The availability of reliable ground-based observations as reference is thus of high importance. In the present study, radiative transfer simulations using state-of-the-art ground-based remote sensing of clouds are contrasted with surface radiative flux measurements to assess their ability to constrain the cloud radiative effect. Cloud radar, lidar, and microwave radiometer observations from the PS106 cruise in the Arctic marginal sea ice zone in summer 2017 were used to derive cloud micro- and macrophysical properties by means of the instrument synergy approach of Cloudnet. Closure of surface radiative fluxes can only be achieved by a realistic representation of the low-level liquid-containing clouds in the radiative transfer simulations. The original, most likely erroneous, representation of these low-level clouds in the radiative transfer simulations led to errors in the cloud radiative effect of 54 W m−2. In total, the proposed method could be applied to 11 % of the observations. For the data, where the proposed method was utilized, the average relative error decreased from 109 % to 37 % for the simulated solar and from 18 % to 2.5 % for the simulated terrestrial downward radiative fluxes at the surface. The present study highlights the importance of jointly improving retrievals for low-level liquid-containing clouds which are frequently encountered in the high Arctic, together with observational capabilities both in terms of cloud remote sensing and radiative flux observations. Concrete suggestions for achieving these goals are provided.

Rice Height Estimation with Multi-Baseline PolInSAR Data and Optimal Detection Baseline Combination Analysis

Rice is a primary food source, and height is a crucial parameter affecting its growth status. Consequently, high-precision, real-time monitoring of quantitative changes in crop height are required for improved crop production. Polarimetric interferometric SAR (PolInSAR) has both polarization and interferometric observation capabilities. Due to the short height of crops and rapid growth changes, the large spatial and short temporal baselines of PolInSAR data are essential for effective crop height inversion; however, relevant satellite-borne SAR and airborne SAR data are currently limited. This study presents a PolInSAR rice height inversion algorithm that uses the oriented volume over ground (OVoG) mode with PolInSAR 0-time and controllable spatial baseline data from a LAMP microwave anechoic chamber. Exploiting the advantages of microwave anechoic chamber measurement data, which includes continuous frequency bands, multiple baselines, and varied incidence angles, the influences of incident angles, baseline length, number of baselines, and baseline combinations are assessed. The highest accuracy rice plant height inversion has a root mean square deviation (RMSE) of 0.1093 m and is achieved with an incidence angle of 35–55°, baseline length of 0.25°, and three to four baselines. Furthermore, an imaging geometric equivalence analysis provides reliable foundation data to guide research into new earth observation SAR systems. The results indicate that, under simulated observations from the GF3 satellite at an altitude of 755 km, the optimal spatial baseline ranges for various frequency bands are as follows: L-band: 10.93–42.59 km; S-band: 4.10–15.97 km; C-band: 2.48–9.64 km; X-band: 1.36–5.32 km; Ku-band: 0.87–3.40 km. Notably, the measurement modes corresponding to the C, X, and Ku bands are ultimately identified as the most suitable for PolInSAR rice height inversion.

Evaluation of in situ observations on Marine Weather Observer during Typhoon Sinlaku

Abstract. The mobile ocean weather observation system, named Marine Weather Observer (MWO), developed by the Institute of Atmospheric Physics (IAP), consists of a fully solar-powered, unoccupied vehicle and meteorological and hydrological instruments. One of the MWOs completed a long-term continuous observation, actively approaching the center of Typhoon Sinlaku from 24 July to 2 August 2020, over the South China Sea. The in situ and high-temporal-resolution (1 min) observations obtained from MWO were analyzed and evaluated through comparison with the observations made by two types of buoys during the evolution of Typhoon Sinlaku. First, the air pressure and wind speed measured by MWO are in good agreement with those measured by the buoys before the typhoon, reflecting the equivalent measurement capabilities of the two methods under normal sea conditions. The sea surface temperature (SST) between MWO and the mooring buoys is highly consistent throughout the observation period, indicating the high stability and accuracy of SST measurements from MWO during the typhoon evolution. The air temperature and relative humidity measured by MWO have significant diurnal variations, generally lower than those measured by the buoys, which may be related to the mounting height and sensitivity of sensors. When actively approaching the typhoon center, the air pressure from MWO can reflect some drastic and subtle changes, such as a sudden drop to 980 hPa, which is difficult to obtain by other observation methods. As a mobile meteorological and oceanographic observation station, MWO has shown its unique advantages over traditional observation methods, and the results preliminarily demonstrate the reliable observation capability of MWO in this paper.

A Geoscience-Aware Network (GASlumNet) Combining UNet and ConvNeXt for Slum Mapping

Approximately 1 billion people worldwide currently inhabit slum areas. The UN Sustainable Development Goal (SDG 11.1) underscores the imperative of upgrading all slums by 2030 to ensure adequate housing for everyone. Geo-locations of slums help local governments with upgrading slums and alleviating urban poverty. Remote sensing (RS) technology, with its excellent Earth observation capabilities, can play an important role in slum mapping. Deep learning (DL)-based RS information extraction methods have attracted a lot of attention. Currently, DL-based slum mapping studies typically uses three optical bands to adapt to existing models, neglecting essential geo-scientific information, such as spectral and textural characteristics, which are beneficial for slum mapping. Inspired by the geoscience-aware DL paradigm, we propose the Geoscience-Aware Network for slum mapping (GASlumNet), aiming to improve slum mapping accuracies via incorporating the DL model with geoscientific prior knowledge. GASlumNet employs a two-stream architecture, combining ConvNeXt and UNet. One stream concentrates on optical feature representation, while the other emphasizes geo-scientific features. Further, the feature-level and decision-level fusion mechanisms are applied to optimize deep features and enhance model performance. We used Jilin-1 Spectrum 01 and Sentinel-2 images to perform experiments in Mumbai, India. The results demonstrate that GASlumNet achieves higher slum mapping accuracy than the comparison models, with an intersection over union (IoU) of 58.41%. Specifically, GASlumNet improves the IoU by 4.60~5.97% over the baseline models, i.e., UNet and ConvNeXt-UNet, which exclusively utilize optical bands. Furthermore, GASlumNet enhances the IoU by 10.97% compared to FuseNet, a model that combines optical bands and geo-scientific features. Our method presents a new technical solution to achieve accurate slum mapping, offering potential benefits for regional and global slum mapping and upgrading initiatives.

Full field-of-view hexagonal lattice structured illumination microscopy based on the phase shift of electro-optic modulators.

High throughput has become an important research direction in the field of super-resolution (SR) microscopy, especially in improving the capability of dynamic observations. In this study, we present a hexagonal lattice structured illumination microscopy (hexSIM) system characterized by a large field of view (FOV), rapid imaging speed, and high power efficiency. Our approach employs spatial light interference to generate a two-dimensional hexagonal SIM pattern, and utilizes electro-optical modulators for high-speed phase shifting. This design enables the achievement of a 210-µm diameter SIM illumination FOV when using a 100×/1.49 objective lens, capturing 2048 × 2048 pixel images at an impressive 98 frames per second (fps) single frame rate. Notably, this method attains a near 100% full field-of-view and power efficiency, with the speed limited only by the camera's capabilities. Our hexSIM demonstrates a substantial 1.73-fold improvement in spatial resolution and necessitates only seven phase-shift images, thus enhancing the imaging speed compared to conventional 2D-SIM.