Observational Capabilities Research Articles

Analyzing the observational capabilities of a Moon-based platform for monitoring Earth’s outgoing radiation

ABSTRACT A Moon-based platform can observe the whole Earth’s disk instantaneously. This paper introduces a fully linked scheme to analyze the observational capabilities of Earth’s outgoing radiation from a Moon-based platform. Precise geometric model for the sensors, including a radiometer and a multi-spectral camera, is established at first. Subsequently, a new method for the reconstruction of the Earth’s outgoing radiation is proposed. We use the Goddard Earth Observing System Version 5 (GEOS-5) data as a reference to simulate the observation data from Moon-based sensors and carry out reconstruction to comprehensively assess the observational capabilities. The results show that: 1) the lowest daily ground coverage rate is about 88.8% and global coverage including polar regions can always be achieved within a month; 2) the sampling interval needs to be less than 4 hours to ensure the accuracy of reconstruction; 3) the image captured by a multi-spectral camera is capable of reconstructing the distribution of the Earth’s outgoing radiation, while the data obtained from a radiometer facilitates the reconstruction of the global Earth’s outgoing radiation, thereby enabling a comprehensive assessment of the Earth’s outgoing radiation. These findings offer valuable insights that can guide the design of sensor parameters for forthcoming missions.

Virtual Integration of Satellite and In-situ Observation Networks (VISION) v1.0: In-Situ Observations Simulator (ISO_simulator)

Abstract. This work presents the first step in the development of the VISION toolkit, a set of Python tools that allows easy, efficient, and more meaningful comparison between global atmospheric models and observational data. Whilst observational data and modelling capabilities are expanding in parallel, there are still barriers preventing these two data sources from being used in synergy. This arises from differences in spatial and temporal sampling between models and observational platforms: observational data from a research aircraft, for example, are sampled on specified flight trajectories at very high temporal resolution. Proper comparison with model data requires generating, storing, and handling a large number of highly temporally resolved model files, resulting in a process which is data-, labour-, and time-intensive. In this paper we focus on comparison between model data and in situ observations (from aircraft, ships, buoys, sondes, etc.). A standalone code, In-Situ Observations Simulator, or ISO_simulator for short, is described here: this software reads modelled variables and observational data files and outputs model data interpolated in space and time to match observations. These model data are then written to NetCDF files that can be efficiently archived due to their small sizes and directly compared to observations. This method achieves a large reduction in the size of model data being produced for comparison with flight and other in situ data. By interpolating global gridded hourly files onto observation locations, we reduce data output for a typical climate resolution run, from ∼3 Gb per model variable per month to ∼15 Mb per model variable per month (a 200-times reduction in data volume). The VISION toolkit is relatively fast to run and can be automated to process large volumes of data at once, allowing efficient data analysis over a large number of years. Although this code was initially tested within the Unified Model (UM) framework, which is shared by the UK Earth System Model (UKESM), it was written as a flexible tool and it can be extended to work with other models.

Integrating UAV and Multisource Satellite Remote Sensing to Estimate Long‐Term River Discharge in High‐Mountain Basins

ABSTRACTIn high‐mountain basins with complex underlying surface, harsh climate and difficult transportation, the conventional monitoring methods are less applicable, and it is also difficult to construct, operate and maintain ground observation stations. This has led to an extreme lack of ground hydrological data, which has restricted the understanding of hydrological processes in alpine basins. This study presents an integrated method for estimating long time‐series discharge using unmanned aerial vehicles (UAVs) and satellite remote sensing (Satellite‐RS). The method can integrate the refined observation capabilities of UAVs with the long time‐series observation capabilities of Satellite‐RS, and the discharge is estimated entirely by UAVs and Satellite‐RS information without relying on ground‐based measured discharge data. To test this method, six reaches within the main stream and tributaries of the Yarlung Zangbo River (YZR) were selected. The results indicate that the mean relative error (MRE) of UAV‐measured river discharge is consistently below 20%, however, larger errors occur for rivers with low water levels and narrow river widths. To address the limitations of the UAV‐based measurement method in capturing discharge variations over time, a discharge estimation formula was devised using the remotely sensed river width as an input variable. At‐a‐section river widths were derived from high‐resolution satellite images (i.e., Landsat‐8, Sentinel‐1, Sentinel‐2 and GF‐2). By integrating the highly precise observations obtained from UAVs with the long time‐series river widths obtained from multisource Satellite‐RS, long time‐series discharge data were estimated at several typical cross‐sections along the YZR. The Nash‐Sutcliffe efficiency values for the discharge estimates ranged from 0.72 to 0.92 during the study period (2014–2020). The results can provide data in support of the study of the YZR discharge composition analysis and other scientific issues, and also offer a theoretical and methodological basis for discharge observations in other high‐mountain basins around the world.

Building Ocean Biodiversity Monitoring Capacity: Tracking Marine Animals with Acoustic Telemetry and the Role of the Ocean Tracking Network

Critical gaps exist in the ocean science community’s biological observation capabilities (e.g., Canonico, 2024; Hassoun et al., 2024). Defining exactly what to monitor and how to obtain the necessary resources to do so are subjects of ongoing debates.

Star formation beyond galaxies: widespread in-situ formation of intra-cluster stars

We study the fraction of the intra-cluster light (ICL) formed in-situ in the three most massive clusters of the TNG50 simulation, with virial masses ∼1014. We find that a significant fraction of ICL stars ( 8%- 28%) are born in-situ. This amounts to a total stellar mass comparable to the central galaxy itself. Contrary to simple expectations, only a sub-dominant fraction of these in-situ ICL stars are born in the central regions and later re-distributed to more energetic orbits during mergers. Instead, many in-situ ICL stars form directly hundreds of kiloparsecs away from the central galaxy, in clouds condensing out of the circum-cluster medium. The simulations predict a present-date diffuse star formation rate of $$1 M⊙/yr, with higher rates at higher redshifts. The diffuse star forming component of the ICL is filamentary in nature, extends for hundreds of kiloparsecs and traces the distribution of neutral gas in the cluster host halo. We discuss briefly how numerical details of the baryonic treatment in the simulation, in particular the density threshold for star formation and the equation of state, may play a role in this result. We conclude that a sensitivity of 1.6×10−19−2.6×10−18 erg s −1 cm −2 arcsec −2 in H α flux (beyond current observational capabilities) would be necessary to detect this diffuse star-forming component in galaxy clusters.

Review of Research Progress on Passive Direction-of-Arrival Tracking Technology for Underwater Targets

Utilization of ocean resources and defense of national security heavily rely on underwater target tracking technology, which consequently holds significant strategic importance. The passive tracking technology for underwater target bearings, known for its extensive detection range, capability for long-term observation, and robust real-time capabilities, has emerged as a new focal point of research. This paper reviews the essential concepts, research developments, applications, and limitations of key technologies for passive underwater target bearing tracking, concentrating on three main areas: underwater target bearing estimation technology, target tracking technology, and comprehensive underwater target bearing tracking technology. Specifically, it discusses highly robust methods for tracking single or multiple underwater targets. Ultimately, this paper highlights the primary challenges currently facing research in this field and provides a perspective on future developments.

On-chip fabrication of tailored 3D hydrogel scaffolds to model cancer cell invasion and interaction with endothelial cells.

The high mortality associated with certain cancers can be attributed to the invasive nature of the tumor cells. Yet, the complexity of studying invasion hinders our understanding of how the tumor spreads. This work presents a microengineered three-dimensional (3D) in vitro model for studying cancer cell invasion and interaction with endothelial cells. The model was generated by printing a biomimetic hydrogel scaffold directly on a chip using 2-photon polymerization that simulates the brain's extracellular matrix. The scaffold's geometry was specifically designed to facilitate the growth of a continuous layer of endothelial cells on one side, while also allowing for the introduction of tumor cells on the other side. This arrangement confines the cells spatially and enables in situ microscopy of the cancer cells as they invade the hydrogel scaffold and interact with the endothelial layer. We examined the impact of 3D printing parameters on the hydrogel's physical properties and used patient derived glioblastoma cells to study their effect on cell invasion. Notably, the tumor cells tended to infiltrate faster when an endothelial cell barrier was present. The potential for adjusting the hydrogel scaffold's properties, coupled with the capability for real-time observation of tumor-endothelial cell interactions, offers a platform for studying tumor invasion and tumor-endothelial cell interactions.

Soil moisture retrieval over croplands using novel dual-polarization SAR vegetation index

Synthetic Aperture Radar (SAR) data, known for its high spatial resolution and all-weather observation capabilities, holds immense promise in soil moisture monitoring. The Water Cloud Model (WCM) is widely applied in soil moisture inversion using SAR data. However, the optical vegetation indices employed in traditional WCM cannot synchronize with SAR data, and the polarimetric scattering information contained in current SAR vegetation indices is incomplete, consequently compromising the accuracy of SAR-based soil moisture retrieval. Therefore, this study proposes a method for soil moisture retrieval over cropland using a novel dual-polarization SAR vegetation index. The method initially combines SAR data covariance elements with backscatter information to establish a polarization scattering contrast parameter (mcp). Then, based on mcp and incorporating the degree of polarization, a polarization scattering correlation contrast parameter (Rcp) is defined. Rcp integrates the distinctive features of scattering differences and polarization states. Building on Rcp, a novel dual-polarization SAR vegetation index (DRVIs) is introduced. Ultimately, DRVIs are utilized in the WCM to achieve surface soil moisture retrieval in cropland. This research conducts experiments in four crop cover areas of the Carman test site in Canada, namely soybean, wheat, canola, and corn. Under VV and VH polarizations, the overall correlation coefficients between measured in-situ data and SSM estimates reach 0.89 and 0.84, respectively. Compared to SSM estimates based on NDVI and LAI products, SSM estimates based on DRVIs exhibit a notable improvement in accuracy, with enhancements of approximately 7.2% and 12.7%, respectively. This novel DRVIs is poised to expand the utilization of SAR data in monitoring vegetation growth and soil moisture retrieval.

High-resolution aerial monitoring using DL for identifying abnormal activity based on visual patterns in drone videos

Unmanned aerial vehicles (UAVs) and sophisticated deep learning (DL) models have made the application of artificial intelligence (AI) more popular. This has resulted in an increase in the number of attempts to improve high-resolution aerial monitoring using DL for identifying abnormal activity based on visual patterns in drone videos. The study introduces a one-class support vector machine (OC-SVM) oddity locator for low-altitude, limited-scope UAVs used for ethereal video surveillance. The primary goal is to improve UAV-based observation capabilities by identifying areas or things of interest without prior knowledge, hence improving tasks like queue control, vehicle following, and hazardous product identification. The framework makes use of OC-SVM because of its quick and lightweight setup, making it suitable for continuous operation on low-computational UAVs. It empowers the identification of several peculiarities necessary for low-elevation reconnaissance by using textural characteristics to recognise both large-scale and tiny structures. Examine the UAV mosaicking and change location (UMCD) dataset to demonstrate the effectiveness of the framework, which achieves excellent accuracy and outperforms traditional methods by about one fifth in a variety of metrics. The suggested model compares with current methods, demonstrating superior accuracy and performance in recognition of peculiarities. Evaluation metrics include F1-score, review, exactness, and accuracy. The model demonstrates that it always encounters an oddity with a review compromise of up to seven on ten, achieving complete accuracy.

Investigation of the optical performance of microchannel plate (MCP) collimators for the eXTP-LAD telescope

The Enhanced X-ray Timing and Polarimetry mission (eXTP) represents a next-generation flagship satellite for X-ray astronomy. A critical component of eXTP is the Large Area Detector (LAD), which consists of 40 modules, each equipped with a set of 4 × 4 microchannel plate (MCP) collimators. The optical characteristics of the MCP are essential for the observational capabilities of the eXTP-LAD. In this study, we provide a detailed description of the point-to-point soft X-ray test equipment. The measurements of the point spread function (PSF) of the collimators reveal discrepancies when compared to theoretical predictions. Specifically, the full width at half maximums (FWHM) and angular resolution are larger than expected, primarily due to the channel errors. The simulation results are consistent with the measured results and demonstrate that channel errors and inner wall roughness significantly impact the optical performance of collimators.

Monitoring mangroves with multi-sensor Earth Observation data sets: from one-shot historical cartography to online and near-real-time monitoring tools

Abstract. This study outlines advancements in mangrove monitoring using Earth Observation (EO) technologies, highlighting a transition from historical cartography to modern, near-real-time monitoring systems. Mangroves, critical for biodiversity, climate regulation, and coastal protection, face threats from climate change and human activities. The need for accurate and recurrent mapping tools is emphasized, addressing the limitations of current global datasets like the Global Mangrove Watch (GMW) by incorporating high-resolution data and field measurements. The French National Research Institute for Sustainable Development (IRD) has pioneered mangrove monitoring since the 1990s, with key methodologies including the use of Sentinel-2 and Pleiades satellite imagery for high-resolution mapping, texture-based analysis, and the incorporation of LiDAR data for accurate biomass estimation. Historical cartography is contrasted with contemporary monitoring efforts, focusing on the integration of multi-source data and the importance of localized ground-truthing. This approach enhances the capabilities of earth observation for carbon stock assessments and supports informed decision-making in conservation and climate policy. The work also highlights the need for further integration of local knowledge and advanced EO sensor data to refine carbon sequestration models and improve mangrove management strategies.

A Multi-Strategy Siberian Tiger Optimization Algorithm for Task Scheduling in Remote Sensing Data Batch Processing.

With advancements in integrated space-air-ground global observation capabilities, the volume of remote sensing data is experiencing exponential growth. Traditional computing models can no longer meet the task processing demands brought about by the vast amounts of remote sensing data. As an important means of processing remote sensing data, distributed cluster computing's task scheduling directly impacts the completion time and the efficiency of computing resource utilization. To enhance task processing efficiency and optimize the allocation of computing resources, this study proposes a Multi-Strategy Improved Siberian Tiger Optimization (MSSTO) algorithm based on the original Siberian Tiger Optimization (STO) algorithm. The MSSTO algorithm integrates the Tent chaotic map, the Lévy flight strategy, Cauchy mutation, and a learning strategy, showing significant advantages in convergence speed and global optimal solution search compared to the STO algorithm. By combining stochastic key encoding schemes and uniform allocation encoding schemes, taking the task scheduling of aerosol optical depth retrieval as a case study, the research results show that the MSSTO algorithm significantly shortens the completion time (21% shorter compared to the original STO algorithm and an average of 15% shorter compared to nine advanced algorithms, such as a particle swarm algorithm and a gray wolf algorithm). It demonstrates superior solution accuracy and convergence speed over various competing algorithms, achieving the optimal execution sequence and machine allocation scheme for task scheduling.

Design and Offshore Testing of the Nodal Deep Sea Seismic–Electromagnetic Joint Acquisition System

Abstract This paper presents the design, development, and offshore validation of OBSEM, a pioneering marine seismic-electromagnetic joint acquisition system aimed at enhancing comprehensive underwater observation capabilities. The system integrates the functionalities of ocean bottom seismometers (OBS) and ocean bottom electro-magnetometers (OBEM), enabling synchronous collection of seismic and electromagnetic signals. Detailed are the structural components, including sensors, data acquisition unit, power supply, clock synchronization system, and signal conditioning techniques employed to augment the signal-to-noise ratio of low-frequency electromagnetic signals. Noteworthy is the clock synchronization methodology employing a hybrid GPS and atomic clock configuration for precise temporal alignment. Furthermore, an automatic leveling system is engineered to maintain the seismometer’s horizontal orientation, crucial for ensuring data fidelity. The integration of seismic and electromagnetic data offers a significant advancement in marine geophysical surveys, promising heightened exploration efficacy and inversion accuracy. Offshore trials conducted in the South China Sea affirm the efficacy of OBSEM, with future endeavors aimed at deep-sea validation.

Stalling North Atlantic Tropical Cyclones

Abstract Tropical cyclone (TC) translation speed influences rainfall accumulation, storm surge, and exposure to high winds. These effects are greatest when storms stall. Here, we provide a definition and climatology of slow-moving or stalling TCs in the North Atlantic from 1900 to 2020. A stall is defined as a TC with a track contained in a circular area (“corral”) with a radius of ≤200 km for 72 h. Of the 1274 North Atlantic TCs, 191 storms met this definition (15%). Ten are multistalling storms or those that experienced more than one stall period. Hurricane Ginger in 1971 stalled the most with four separate stalls. Stalling TC locations are clustered in the western Caribbean, the central Gulf Coast, the Bay of Campeche, and near Florida and the Carolinas. Stalling was most common in October TCs (17.3% of October total) and least common in August (8.2%). The estimated annual frequency of stalls significantly increased over the satellite era (1966–2020) by 1.5% yr−1, and the cumulative frequency in the number of stalls compared to all storms also increased. Stalling storms have a significantly higher frequency of major hurricane status than nonstalling storms. Storms are also more likely to stall near the coast (≤200 km). Approximately 40% (n = 77) of the stalling TCs experienced a period of rapid intensification, and five did so within 200 km of a coastal zone. These results will aid emergency managers in regions that experience frequent stalls by providing information they can use to better prepare for the future. Significance Statement The forward movement of a tropical cyclone can influence rainfall, storm surge height, and exposure time to high wind speeds. Storms that slow down or stall can increase total damage by prolonging the exposure time to intense conditions. This study aims to define a stalling storm and then provide a geographic snapshot into our historical experience with these storms. There have been a consistent number of stalling storms over time, with a modest increase starting in the satellite era, likely as a product of increased observational capabilities. Stalls tend to occur in similar places over time and happen more frequently later in the hurricane season (October) when compared to the middle (August). Emergency managers can use this information to identify the likely location and timing for stalls throughout the North Atlantic tropical cyclone season.

Exploring the Cosmos: Impact and Significance of the James Webb Space Telescope

The James Webb Space Telescope (JWST) marks a pivotal advancement in astronomical research, building upon the legacy of its predecessors by enhancing the observational capabilities and expanding the understanding of the universe. This study explores the transformative impact of JWST, emphasizing its role in studying the formation and evolution of early galaxies, stars, and planetary systems. By utilizing its advanced infrared capabilities, JWST can penetrate cosmic dust and observe celestial phenomena that were previously obscured, thereby providing unprecedented insights into the early universe and the origins of life’s building blocks. The findings from JWST are expected to challenge existing theories and assumptions in cosmology and astrophysics, facilitating a paradigm shift in the comprehension of cosmic structures and the fundamental processes governing them. Furthermore, the telescope’s ability to analyze exoplanetary atmospheres will contribute significantly to the search for life beyond Earth. Ultimately, the JWST not only represents a technological marvel but also serves as a cornerstone for future astronomical discoveries, shaping the conception of the cosmos for generations to come.

Persistent surveillance by heterogeneous multi-agents using mutual information based on observation capability

Persistent surveillance by heterogeneous multi-agents using mutual information based on observation capability

ALICENET – an Italian network of automated lidar ceilometers for four-dimensional aerosol monitoring: infrastructure, data processing, and applications

Abstract. ​​​​​​​Vertically resolved information on aerosol particles represents a key aspect in many atmospheric studies, including aerosol–climate interactions and aerosol impacts on air quality and human health. This information is primarily derived by lidar active remote sensing, in particular with extensive networks currently in operation worldwide. In Italy, the Institute of Atmospheric Sciences and Climate (ISAC) of the National Research Council (CNR) established the ALICENET network of automated lidar ceilometers (ALCs) in 2015. Since then, ALICENET has grown as a cooperative effort of Italian institutions dealing with atmospheric science and monitoring, and it currently includes instruments run by regional environmental protection agencies, universities, research centres, and private companies. In the current configuration, the network makes use of both single-channel ALCs and dual-channel, polarisation-sensitive-system ALCs (referred to as PLCs). The systems operate in very different environments (urban, coastal, mountainous, and volcanic areas) from northern to southern Italy, thus allowing the continuous monitoring of the aerosol vertical distribution across the country. ALICENET also contributes to the EUMETNET programme E-PROFILE, filling an Italian observational gap compared to other EU member states, which generally run extended ALC networks through national meteorological services. In this work, we present the ALICENET infrastructure and the specifically developed data processing centralised at CNR-ISAC, converting raw instrumental data into quantitative, quality-controlled information on aerosol properties ranging from attenuated backscatter to aerosol mass and vertical stratifications. This setup allows us to get insights into the 4D aerosol field over Italy with applications from near-real-time monitoring to long-term analyses, examples of which are reported in this work. Specific comparisons of the ALICENET products to independent measurements obtained with different techniques, such as particulate matter (PM) concentrations from in situ samplers and aerosol optical depth (AOD) from sun photometers, are also included here, revealing the good performances of the ALICENET algorithms. Overall, ALICENET represents a valuable resource to extend the current aerosol observational capabilities in Italy and in the Mediterranean area, and it contributes to bridging the gap between atmospheric science and its application to specific sectors, among which are air quality, solar energy, and aviation safety.

Multi-aperture telescopes at the quantum limit of superresolution imaging : Detecting subRayleigh object near a star

Angular resolution is a critical aspect of astronomical observation, as it determines the minimum resolvable angle between two objects. This is governed by the Rayleigh criterion, which states that the minimum resolvable angle is proportional to the wavelength (λ) over the diameter (D) of the aperture of a monolithic telescope. While larger aperture telescopes have advantages such as smaller angular resolution and higher sensitivity to dim and small exoplanets, they also have disadvantages such as higher launch cost, design intricacies, and high mission cost. Using multi-aperture telescopes can be a cost-effective alternative as they work on the principles of baseline interferometry, making the minimum resolvable angle proportional to λ/B, where B is the baseline. In this work, we compare the performance of a single monolithic telescope to a multi-aperture alternative with the same effective glass area in the context of hypothesis testing between two scenarios - H1 (a star) and H2 (a star with an exoplanet). We formulate the theory based on likelihood ratio tests and find that the multi-aperture telescope performs better than the monolithic telescope when direct detection is used on the focal plane. While this is expected, the performance can be further improved by using quantum-inspired detecting strategies. We utilize Quantum Binary Spatial Mode Demultiplexing (BSPADE) to process the point spread function (PSF) of the telescopes and find better performance compared to the respective direct detection measurement. Therefore, our efforts can be viewed as one of the initial steps toward employing quantum-inspired detection techniques in sparse aperture configurations for high-contrast imaging applications. In conclusion, our work shows that multi-aperture telescopes are an effective alternative to monolithic telescopes for object discrimination and potentially for super-resolution imaging and their performance can be further improved by using quantum-inspired detection strategies. With their cost-effectiveness (see Appendix C) and potential for high performance, multi-aperture telescopes can significantly advance our ability to observe and study exoplanets and other celestial objects. Future research can explore ways to optimize the performance of multi-aperture telescopes and further improve their capabilities for astronomical observation, potentially facilitating the detection and imaging of Earth-like exoplanets.

A Biomimetic Adhesive Disc for Robotic Adhesion Sliding Inspired by the Net-Winged Midge Larva.

Net-winged midge larvae (Blephariceridae) are known for their remarkable ability to adhere to and crawl on the slippery surfaces of rocks in fast-flowing and turbulent alpine streams, waterfalls, and rivers. This remarkable performance can be attributed to the larvae's powerful ventral suckers. In this article, we first develop a theoretical model of the piston-driven sucker that considers the lubricated state of the contact area. We then implement a piston-driven robotic sucker featuring a V-shaped notch to explore the adhesion-sliding mechanism. Each biomimetic larval sucker has the unique feature of an anterior-facing V-shaped notch on its soft disc rim; it slides along the shear direction while the entire disc surface maintains powerful adhesion on the benthic substrate, just like the biological counterpart. We found that this biomimetic sucker can reversibly transit between "high friction" (4.26 ± 0.34 kPa) and "low friction" (0.41 ± 0.02 kPa) states due to the piston movement, resulting in a frictional enhancement of up to 93.9%. We also elucidate the frictional anisotropy (forward/backward force ratio: 0.81) caused by the V-shaped notch. To demonstrate the robotic application of this adhesion-sliding mechanism, we designed an underwater crawling robot Adhesion Sliding Robot-1 (ASR-1) equipped with two biomimetic ventral suckers. This robot can successfully crawl on a variety of substrates such as curved surfaces, sidewalls, and overhangs and against turbulent water currents with a flow speed of 2.4 m/s. In addition, we implemented a fixed-wing aircraft Adhesion Sliding Robot-2 (ASR-2) featuring midge larva-inspired suckers, enabling transit from rapid water surface gliding to adhesion sliding in an aquatic environment. This adhesion-sliding mechanism inspired by net-winged midge larvae may pave the way for future robots with long-term observation, monitoring, and tracking capabilities in a wide variety of aerial and aquatic environments.

Autonomous tracking of honey bee behaviors over long-term periods with cooperating robots.

Digital and mechatronic methods, paired with artificial intelligence and machine learning, are transformative technologies in behavioral science and biology. The central element of the most important pollinator species-honey bees-is the colony's queen. Because honey bee self-regulation is complex and studying queens in their natural colony context is difficult, the behavioral strategies of these organisms have not been widely studied. We created an autonomous robotic observation and behavioral analysis system aimed at continuous observation of the queen and her interactions with worker bees and comb cells, generating behavioral datasets of exceptional length and quality. Key behavioral metrics of the queen and her social embedding within the colony were gathered using our robotic system. Data were collected continuously for 24 hours a day over a period of 30 days, demonstrating our system's capability to extract key behavioral metrics at microscopic, mesoscopic, and macroscopic system levels. Additionally, interactions among the queen, worker bees, and brood were observed and quantified. Long-term continuous observations performed by the robot yielded large amounts of high-definition video data that are beyond the observation capabilities of humans or stationary cameras. Our robotic system can enable a deeper understanding of the innermost mechanisms of honey bees' swarm-intelligent self-regulation. Moreover, it offers the possibility to study other social insect colonies, biocoenoses, and ecosystems in an automated manner. Social insects are keystone species in all terrestrial ecosystems; thus, developing a better understanding of their behaviors will be invaluable for the protection and even the restoration of our fragile ecosystems globally.