Space-based Platforms Research Articles

Concept of Sporadic E Monitoring Using Space-Based Low Power Multiple Beacon-Systems

Current monitoring systems to detect sporadic E use ground-based setups, ionosondes, and the network of GNSS satellites in order to assess the phenomenon of sporadic E. This paper aims to monitor sporadic E using a miniature space-based platform in an atypical way. The setup consists of multiple radio-amateur beacon systems aboard satellites, each having a specific modulation and transmission scheme. This Radio Amateur Beacon System for the Investigation of the Ionosphere (RABSII) is coupled to a GNSS receiver, revealing the location of the platform. Multiple beacon data streams are sequentially sent from a satellite platform towards the Earth. By receiving and comparing the Signal-to-Noise ratios of these streams using a dedicated ground-based radio-amateur network of receiving stations, the presence of sporadic E can be determined, and a location-based model can be built. The advantage of this miniaturized, low-power, low-cost instrument is its ability to be put on any satellite platform in the future in order to map sporadic E.

A Beam Hopping Scheme Based on Adaptive Beam Radius for LEO Satellites.

Toward the vision of seamless global connectivity in the 6G era, the non-terrestrial network (NTN) in space-air-ground integrated networks (SAGINs) network architecture is one of the highly promising solutions. From the perspective of relay nodes, NTN includes satellite nodes and space-based platform nodes. As a resource management technology in satellite communication, beam-hopping has garnered significant attention from researchers due to its effectiveness in ad-dressing the disparity between offered capacities and uneven terrestrial traffic demands. Recognizing that the larger beams offer broader coverage but the smaller ones provide better an-ti-interference capabilities and higher throughput, this paper introduces an adaptive cluster-ing-based approach. It provides large, medium, and small user beams to target ground users. The proposed algorithm aims to minimize total system latency and enhance system throughput. Sim-ulation results show that employing the proposed algorithm in the baseline model results in a 3.44% increase in system throughput and a 35.5% reduction in system latency. Furthermore, simulation results based on alternative models indicate that while the proposed algorithm may lead to a slight decrease in system throughput, it brings significant improvements in system latency.

EVALUATION OF VEGETATION COVER DYNAMICS IN A SUB-TROPICAL CLIMATE USING HIGH-RESOLUTION MULTISPECTRAL DATA FROM UAVS

The study of vegetation status is of great importance as it reveals environmental factors and their relationship with the availability of water resources, components that are relevant in the planning, drafting and execution of numerous civil engineering projects. Detecting changes in vegetation cover often requires data from space-based platforms, which may have limitations in terms of spatial or temporal resolution and high costs for acquiring high-resolution data. For relatively small areas, the use of Unmanned Aerial Vehicles (UAVs) provides a solution. These devices facilitate the acquisition of land cover information with high spatial and temporal resolution through the use of photographic cameras or specialized sensors. This research analyzes the vegetation status within a study area characterized by distinct climatic seasons (dry and humid) within a warm sub-tropical climate. The primary focus lies in detecting changes in vegetation cover and evaluating the overall state of the vegetation. To accomplish this objective, a multirotor UAV equipped with a multispectral camera that captures RED and NIR bands is deployed. Various vegetation indices based on distance and slope are applied to access the vegetation’s condition. The analysis of the results indicates that it is possible to evaluate the vegetation cover dynamics using the very high-resolution data obtained with the VANT approach (10 cm per pixel). Notably, the distance-based vegetation indices demonstrate superior efficacy in delineating the vegetation status and the stress induced by the scarcity of water resources during the transition between dry and humid seasons.

Research on Knowledge Graph Construction and Semantic Representation of Low Earth Orbit Satellite Spectrum Sensing Data

The growth of frequency-usage devices has made the electromagnetic spectrum posture complex, resulting in an urgent demand for frequency-usage posture cognition. However, the sensing of space-based platforms is limited by the transmission capacity of the satellite-to-ground link and the satellite processing capacity, which makes on-satellite data analysis and posture generation lack the efficient means. Facing the above issues, an idea of a knowledge graph construction and semantic representation for low Earth orbit (LEO) satellite spectrum sensing data is designed in this paper. In the designed construction process, technologies such as knowledge extraction, ontology construction, knowledge fusion and knowledge visualization are utilized to efficiently analyze on-satellite sensing data. Moreover, the constructed spectrum knowledge graph can be applied in the analysis and prediction of frequency-usage behavior and intelligent spectrum management, which exhibits the effectiveness of the spectrum knowledge graph. Finally, the further development of the spectrum knowledge graph is foreseen.

Theoretical derivation of aerosol lidar ratio using Mie theory for CALIOP-CALIPSO and OPAC aerosol models

Abstract. The extinction-to-backscattering ratio, popularly known as lidar (light detection and ranging) ratio of atmospheric aerosols is an important optical property, which is essential to retrieve the extinction profiles of atmospheric aerosols. Lidar satellite observations can provide the global coverage of atmospheric aerosols along with their vertical extent. NASA's Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite is the only space-based platform available, so far, that provides the vertical profiles of extinction due to atmospheric aerosols. A physics-based theoretical approach is presented in the present paper that estimates lidar ratio values for CALIPSO aerosol models, which can be used as inputs to determine the extinction profiles of aerosols using CALIPSO data. The developed methodology was also qualified by comparing it with the lidar ratio values derived using AERONET (AErosol RObotic NETwork) datasets. Lidar ratio values for CALIPSO aerosol models were estimated in the range of 38.72 to 85.98 sr at 532 nm, whereas at 1064 nm lidar ratio varied between 20.11 to 71.11 sr depending upon the aerosol type and their size distributions. Aerosols are compositions of various particles; thus, the presence of water vapour in the atmosphere can affect the optical properties of the aerosols. Thus, the effect of relative humidity on lidar ratio was studied using Optical Properties of Aerosols and Clouds (OPAC) aerosol models, which are the standard aerosol models against the cluster-classified AERONET and CALIPSO aerosol models. Water-soluble particles contribute substantially in clean continental, clean marine, tropical marine and desert aerosol models and are hygroscopic in nature. Hygroscopic sulfate particles dominate the Antarctic aerosols during summertime. In the presence of relative humidity between 0 %–80 %, the lidar ratio values were observed to decrease from 53.59 to 47.13, from 53.66 to 47.15, from 53.70 to 47.16, and from 55.32 to 48.78 sr at 532 nm for clean continental, clean marine, tropical marine, and desert aerosols, respectively, whereas lidar ratio gradually increased from 47.13 to 51, from 47.15 to 51, from 47.16 to 51, and from 48.78 to 51.68 sr, respectively, for these aerosol models when relative humidity was between 80 %–99 %, due to constituent hygroscopic particles. In the case of Antarctic aerosols, the lidar ratio was observed to increase from 57.73 to 97.64 sr due to hygroscopic sulfate particles that backscattered heavily in the presence of water vapour at 532 nm. The soot particles dominate the polluted continental and polluted marine particles, causing an increase in lidar ratio over its corresponding clean counterpart. Similar results were observed at 1064 nm for OPAC aerosol models.

Extended Space Deterrence: Providing Security Assurance in Space

Many U.S. allies are increasingly dependent on space-based platforms for their military and economic activities. At the same time, the counterspace threats from U.S. adversaries such as China and Russia are rapidly intensifying. The United States has provided extended deterrence to its allies through its overwhelming nuclear and conventional capabilities for decades. The question arises as to whether the time-tested logic of extended deterrence is applicable in the space domain. This research argues that U.S. extended deterrence in space—relying on the traditional mechanism of deterrence-by-punishment—is ineffective due to the unique nature of outer space.

Spectral Energy Distributions for 258 Local Volume Galaxies

We present model spectral energy distribution (SED) fits to ultraviolet/optical/infrared observations for the 258 nearby galaxies in the Local Volume Legacy survey, a sample dominated by lower-luminosity dwarf irregular systems. The data for each galaxy include up to 26 spatially-integrated broadband and narrowband fluxes from the Galaxy Evolution Explorer, Spitzer Space Telescope, and Infrared Astronomical Satellite space-based platforms, and from the Sloan Digital Sky Survey, Two Micron All Sky Survey, and other ground-based efforts. The CIGALE SED fitting package is employed using a delayed star-formation history with an optional late burst or quenching episode to constrain 11 different free parameters that characterize the properties of each galaxy’s stellar and dust emission, with the overriding constraint that the ultraviolet/optical emission absorbed by interstellar dust grains is emitted in equal energy portions at infrared wavelengths. The main results are: (i) 94% of the SED fits yield reduced χ 2 values less than 3; (ii) the modeled stellar masses agree with those derived from 3.6 μm-based measures with a scatter of 0.07 dex; (iii) for a typical galaxy in the sample the SED-derived star-formation rate averaged over the past 100 Myr is about 88% of the value derived from standard hybrid indicators on similar timescales; and (iv) there is a statistically significant inverse relation between the stellar mass fraction appearing in the late burst and the total stellar mass. These results build upon prior SED modeling efforts in the local volume and lay the groundwork for future studies of more distant low-metallicity galaxies with JWST.

Cross-Viewpoint Template Matching Based on Heterogeneous Feature Alignment and Pixel-Wise Consensus for Air- and Space-Based Platforms

Template matching is the fundamental task in remote sensing image processing of air- and space-based platforms. Due to the heterogeneous image sources, different scales and different viewpoints, the realization of a general end-to-end matching model is still a challenging task. Considering the abovementioned problems, we propose a cross-view remote sensing image matching method. Firstly, a spatial attention map was proposed to solve the problem of the domain gap. It is produced by two-dimensional Gaussian distribution and eliminates the distance between the distributed heterogeneous features. Secondly, in order to perform matching at different flight altitudes, a multi-scale matching method was proposed to perform matching on three down-sampling scales in turn and confirm the optimal result. Thirdly, to improve the adaptability of the viewpoint changes, a pixel-wise consensus method based on a correlation layer was applied. Finally, we trained the proposed model based on weakly supervised learning, which does not require extensive annotation but only labels one pair of feature points of the template image and search image. The robustness and effectiveness of the proposed methods were demonstrated by evaluation on various datasets. Our method accommodates three types of template matching with different viewpoints, including SAR to RGB, infrared to RGB, and RGB to RGB.

Demonstration of a quantum advantage by a joint detection receiver for optical communication using quantum belief propagation on a trapped-ion device

Demonstrations of quantum advantage have largely focused on computational speedups and on quantum simulation of many-body physics, limited by fidelity and capability of current devices. Discriminating laser-pulse-modulated classical-communication codewords at the minimum allowable probability of error using universal-quantum processing presents a promising parallel direction, one that is of both fundamental importance in quantum state discrimination, as well as of technological relevance in deep-space laser communications. Here we present an experimental realization of a quantum joint detection receiver for binary phase shift keying modulated codewords of a 3-bit linear tree code using a recently-proposed quantum algorithm: belief propagation with quantum messages. The receiver, translated to a quantum circuit, was experimentally implemented on a trapped-ion device -- the recently released Honeywell LT-1.0 system using ${}^{171}Yb+ $ ions, which possesses all-to-all connectivity and mid-circuit measurement capabilities that are essential to this demonstration. We conclusively realize a previously postulated but hitherto not-demonstrated joint quantum detection scheme, and provide an experimental framework that surpasses the quantum limit on the minimum average decoding error probability associated with pulse-by-pulse detection in the low mean photon number limit. The full joint-detection scheme bridges across photonic and trapped-ion based quantum information science, mapping the photonic coherent states of the modulation alphabet onto inner product-preserving states of single-ion qubits. Looking ahead, our work opens new avenues in hybrid realizations of quantum-enhanced receivers with applications in astronomy and emerging space-based platforms.

Falcon Neuro: an event-based sensor on the International Space Station

We report on the Falcon neuro event-based sensor (EBS) instrument that is designed to acquire data from lightning and sprite phenomena and is currently operating on the International Space Station. The instrument consists of two independent, identical EBS cameras pointing in two fixed directions, toward the nominal forward direction of flight and toward the nominal Nadir direction. The payload employs stock DAVIS 240C focal plane arrays along with custom-built control and readout electronics to remotely interface with the cameras. To predict the sensor’s ability to effectively record sprites and lightning, we explore temporal response characteristics of the DAVIS 240C and use lab measurements along with reported limitations to model the expected response to a characteristic sprite illumination time-series. These simulations indicate that with appropriate camera settings the instrument will be capable of capturing these transient luminous events when they occur. Finally, we include initial results from the instrument, representing the first reported EBS recordings successfully collected aboard a space-based platform and demonstrating proof of concept that a neuromorphic camera is capable of operating in the space environment.

Space-based infrared aerial target detection method via interframe registration and spatial local contrast

Infrared aerial target detection on a space-based platform is of great importance in the field of aerospace. However, the intensities of clutter may be much stronger than those of aerial targets in space-based infrared images, which causes issues in accurate aerial target detection. Existing detection methods cannot adapt well to such a situation, and often receive results with missed detections and false alarms since they are prone to enhance clutter rather than the target. To tackle such limitations, we propose a two-stage Interframe Registration and Spatial Local Contrast (IFR-SLC) based method for space-based infrared aerial target detection. In the first stage, the interframe registration model is constructed to suppress strong clutter and highlight the target. In the second stage, the spatial local contrast model is introduced to further suppress the residual background and enhance the target, which eventually generates the saliency map with only the aerial target remaining. Target extraction is conducted by setting an adaptive threshold. Five space-based sequences with different backgrounds were selected for evaluation, and experimental results indicated better performances of IFR-SLC compared with those of space-based or state-of-the-art detection methods.

CubeSat constellations provide enhanced crop phenology and digital agricultural insights using daily leaf area index retrievals

Satellite remote sensing has great potential to deliver on the promise of a data-driven agricultural revolution, with emerging space-based platforms providing spatiotemporal insights into precision-level attributes such as crop water use, vegetation health and condition and crop response to management practices. Using a harmonized collection of high-resolution Planet CubeSat, Sentinel-2, Landsat-8 and additional coarser resolution imagery from MODIS and VIIRS, we exploit a multi-satellite data fusion and machine learning approach to deliver a radiometrically calibrated and gap-filled time-series of daily leaf area index (LAI) at an unprecedented spatial resolution of 3 m. The insights available from such high-resolution CubeSat-based LAI data are demonstrated through tracking the growth cycle of a maize crop and identifying observable within-field spatial and temporal variations across key phenological stages. Daily LAI retrievals peaked at the tasseling stage, demonstrating their value for fertilizer and irrigation scheduling. An evaluation of satellite-based retrievals against field-measured LAI data collected from both rain-fed and irrigated fields shows high correlation and captures the spatiotemporal development of intra- and inter-field variations. Novel agricultural insights related to individual vegetative and reproductive growth stages were obtained, showcasing the capacity for new high-resolution CubeSat platforms to deliver actionable intelligence for precision agricultural and related applications.

Accuracy Assessment, Comparative Performance, and Enhancement of Public Domain Digital Elevation Models (ASTER 30 m, SRTM 30 m, CARTOSAT 30 m, SRTM 90 m, MERIT 90 m, and TanDEM-X 90 m) Using DGPS

Publicly available Digital Elevation Models (DEM) derived from various space-based platforms (Satellite/Space Shuttle Endeavour) have had a tremendous impact on the quantification of landscape characteristics, and the related processes and products. The accuracy of elevation data from six major public domain satellite-derived Digital Elevation Models (a 30 m grid size—ASTER GDEM version 3 (Ast30), SRTM version 3 (Srt30), CartoDEM version V3R1 (Crt30)—and 90 m grid size—SRTM version 4.1 (Srt90), MERIT (MRT90), and TanDEM-X (TDX90)), as well as the improvement in accuracy achieved by applying a correction (linear fit) using Differential Global Positioning System (DGPS) estimates at Ground Control Points (GCPs) is examined in detail. The study area is a hard rock terrain that overall is flat-like with undulating and uneven surfaces (IIT (ISM) Campus and its environs) where the statistical analysis (corrected and uncorrected DEMs), correlation statistics and statistical tests (for elevation and slope), the impact of resampling methods, and the optimum number of GCPs for reduction of error in order to use it in further applications have been presented in detail. As the application of DGPS data at GCPs helps in the substantial reduction of bias by the removal of systematic error, it is recommended that DEMs may be corrected using DGPS before being used in any scientific studies.

RMCSat: An F10.7 Solar Flux Index CubeSat Mission

The F10.7 solar flux index is a measure of microwave solar emissions at a wavelength of 10.7 cm or 2800 MHz. It is widely used in thermosphere and ionosphere models as an indicator of solar activity and is recorded at only one terrestrial observatory in Penticton, Canada during daylight hours. The lack of geographical and temporal coverage of F10.7 measurements and no external redundancy to the existing system has led to the development of the RMCSat mission, which seeks to demonstrate the feasibility of collecting microwave solar flux emissions from a space-based platform. RMCSat is the first CubeSat mission by the Royal Military College of Canada. It offers a training environment for personnel in space mission analysis and design, satellite assembly, integration and testing, and satellite operations. This paper introduces the mission concept and preliminary design of a space-based solution that captures solar density flux measurements during each orbit as the Sun passes through the boresight of the primary payload antenna. In addition to two channels recording the 2800 MHz frequency (2785 MHz and 2815 MHz), a third channel records 2695 MHz using the same calibration standard to determine if the United States Radio Solar Telescope Network (RSTN) could be leveraged to supplement the existing F10.7 solar flux measurements and improve solar flux approximations. The RMCSat mission, satellite design, and system budgets are demonstrated here as being viable. Future design considerations pertain to the payload antennas and achievable launch orbits.

Earth-Based Research Analogs to Investigate Space-Based Health Risks.

During spaceflight, astronauts are exposed to a variety of unique hazards, including altered gravity fields, long periods of isolation and confinement, living in a closed environment at increasing distances from Earth, and exposure to higher levels of hazardous ionizing radiation. Preserving human health and performance in the face of these relentless hazards becomes progressively more difficult as missions increase in length and extend beyond low Earth orbit. Finding solutions is a significant challenge that is further complicated by logistical issues associated with studying these unique hazards. Although research studies using space-based platforms are the gold standard, these are not without limitations. Factors such as the small sample size of the available astronaut crew, high expense, and time constraints all add to the logistical challenge. To overcome these limitations, a wide variety of Earth-based analogs, from polar research outposts to an undersea laboratory, are available to augment space-based studies. Each analog simulates unique physiological and behavioral effects associated with spaceflight and, therefore, for any given study, the choice of an appropriate platform is closely linked to the phenomena under investigation as well as the characteristics of the analog. There are pros and cons to each type of analog and each actual facility, but overall they provide a reasonable means to overcome the barriers associated with conducting experimental research in space. Analogs, by definition, will never be perfect, but they are a useful component of an integrated effort to understand the human risks of living and working in space. They are a necessary resource for pushing the frontier of human spaceflight, both for astronauts and for commercial space activities. In this review, we describe the use of analogs here on Earth to replicate specific aspects of the spaceflight environment and highlight how analog studies support future human endeavors in space.

Optical design of visual and infrared imaging system based on space-based platform

The predecessor of China Optics was China Optics and Applied Optics Digest, which was founded in 1985. At that time, it was the only retrieval journal in the field of optics in China. At the end of 2008, China Optics and Applied Optics Digest was renamed

Event-Based Object Detection and Tracking for Space Situational Awareness

In this work, we present an optical space imaging dataset using a range of event-based neuromorphic vision sensors. The unique method of operation of event-based sensors makes them ideal for space situational awareness (SSA) applications due to the sparseness inherent in space imaging data. These sensors offer significantly lower bandwidth and power requirements making them particularly well suited for use in remote locations and space-based platforms. We present the first publicly-accessible event-based space imaging dataset including recordings using sensors from multiple providers, greatly lowering the barrier to entry for other researchers given the scarcity of such sensors and the expertise required to operate them for SSA applications. The dataset contains both day time and night time recordings, including simultaneous co-collections from different event-based sensors. Recorded at a remote site, and containing 572 labeled targets with a wide range of sizes, trajectories, and signal-to-noise ratios, this real-world event-based dataset represents a challenging detection and tracking task that is not readily solved using previously proposed methods. We propose a highly optimized and robust feature-based detection and tracking method, designed specifically for SSA applications, and implemented via a cascade of increasingly selective event filters. These filters rapidly isolate events associated with space objects, maintaining the high temporal resolution of the sensors. The results from this simple yet highly optimized algorithm on the space imaging dataset demonstrate robust high-speed event-based detection and tracking which can readily be implemented on sensor platforms in space as well as terrestrial environments.

Characterization of chlorophyll fluorescence, absorbed photosynthetically active radiation, and reflectance-based vegetation index spectroradiometer measurements

ABSTRACT Spectroscopy plays a key role in Earth observations, especially for studies involving vegetation function and structure. These measurements are critical in the context of carbon cycle monitoring from leaf to global scales. Reflectance-based vegetation indices (RIs) have been used extensively in remote sensing studies from the unpiloted aerial vehicle, aerial, and space-based platforms to model quantities related to productivity, such as gross primary production (GPP), while more recently chlorophyll fluorescence (CF) measurements are increasingly exploited to track GPP. CF and RI measurements vary in magnitude, depend on different portions of the spectrum, and are derived from unique equations; thus, instrument uncertainty manifests distinctly for these measurements. Although this is well known, it is often unexamined in experiments and analyses. We use a portable spectroradiometer to make measurements of reflectance-based vegetation indices (RIs) and chlorophyll fluorescence (CF) in order to characterize how measurements of RIs and CF compare to one another. In particular, we examine fluorescence (F) and fluorescence yield (F Yield) under a light-emitting diode grow light (LED), solar-induced fluorescence (SIF), solar-induced fluorescence yield (SIFYield), absorbed photosynthetically active radiation (APAR), and reflectance-based vegetation indices (the normalized difference vegetation index (NDVI), the chlorophyll/carotenoid index (CCI), and the photochemical reflectance index (PRI)) and include maximized propagated uncertainty of the spectroradiometer for each measurement. We show that RIs have a significantly lower propagated error relative to the mean (0.01% to 0.28%) than CF measurements (0.01% to 1.28%) and that while fine resolution spectrometer CF measurements are outside the noise of the instrument and have potential to provide relative measurements of productivity, show why this instrument having fine spectral resolution and sampling is more effective for measurements of APAR and RIs. We also demonstrate that F and F Yield measurements have low propagated uncertainty and propose that future studies of plant function using this spectrometer/LED technique and the full range of spectra be undertaken. Finally, measurements of SIF, F, and APAR can provide estimates of SIFYieldand F Yield in the same order of magnitude, but further examination is required to determine how these measurements compare under a range of illumination and environmental conditions and how they might compare to PRI.

Improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques

Abstract. Vegetation has a tremendous influence on snow processes and snowpack dynamics, yet remote sensing techniques to resolve the spatial variability of sub-canopy snow depth are not always available and are difficult from space-based platforms. Unmanned aerial vehicles (UAVs) have had recent widespread application to capture high-resolution information on snow processes and are herein applied to the sub-canopy snow depth challenge. Previous demonstrations of snow depth mapping with UAV structure from motion (SfM) and airborne lidar have focussed on non-vegetated surfaces or reported large errors in the presence of vegetation. In contrast, UAV-lidar systems have high-density point clouds and measure returns from a wide range of scan angles, increasing the likelihood of successfully sensing the sub-canopy snow depth. The effectiveness of UAV lidar and UAV SfM in mapping snow depth in both open and forested terrain was tested in a 2019 field campaign at the Canadian Rockies Hydrological Observatory, Alberta, and at Canadian prairie sites near Saskatoon, Saskatchewan, Canada. Only UAV lidar could successfully measure the sub-canopy snow surface with reliable sub-canopy point coverage and consistent error metrics (root mean square error (RMSE) <0.17 m and bias −0.03 to −0.13 m). Relative to UAV lidar, UAV SfM did not consistently sense the sub-canopy snow surface, the interpolation needed to account for point cloud gaps introduced interpolation artefacts, and error metrics demonstrated relatively large variability (RMSE<0.33 m and bias 0.08 to −0.14 m). With the demonstration of sub-canopy snow depth mapping capabilities, a number of early applications are presented to showcase the ability of UAV lidar to effectively quantify the many multiscale snow processes defining snowpack dynamics in mountain and prairie environments.

Physical and Optical Properties of Ultra-black Nickel–Phosphorus for a Total Solar Irradiance Measurement

Abstract The total solar irradiance at the top of the atmosphere is the primary source of energy of the Earth’s highly coupled atmosphere–land–ocean system. Small fluctuations of the solar flux density in scales from years to millennia could impact the energy balance of this system due to nonlinear effects. The quantification of this variability depends on absolute radiometers on board of space-based platforms. Although there has been significant improvement in the design and calibration of absolute radiometers during the last decades, the uncertainties in the measurements have not allowed us to untangle the natural and anthropogenic drivers of the observed changes of the climatic patterns appropriately. One of the critical components of the absolute radiometers is the coating of the sensor elements, which should absorb the radiation efficiently. Here we discuss the optical characteristics of ultra-black Nickel–Phosphorus (Ni–P) and its relations with the surface morphology. The ultra-black Ni–P has important unique properties such as low reflectance and uniformity of deposition in complex geometries. Ni–P multilayer was deposited by electroless on aluminum substrates. The surface was etched by oxidizing acid to produce ultra-black Ni–P. Characterization techniques were used to describe the properties of the material. We describe the directional reflectance employing the bidirectional reflectance distribution function. Additionally, we used reflectance maps to show the influence of the pores on the reflectance. Ultra-black Ni–P exhibited a high absorptance and dependence with the light incidence angle. Based on the results, the material demonstrated the opportunity of many terrestrial and space applications as a black coating absorber.