Board Of Satellite Research Articles

Spatial and Temporal Variations of Thermal Contrast in the Planetary Boundary Layer

High-spectral resolution infrared sounders on board satellites can measure atmospheric trace gases confined to the planetary boundary layer (PBL). However, their sensitivity to the PBL depends on the temperature difference between the surface and the atmosphere, the so-called thermal contrast (TC). After reviewing the physical aspects of TC and how it drives measurement sensitivity, we characterize the global and temporal behavior of TC in clear-sky conditions. Combining land surface temperatures from the Copernicus Global Land Services dataset with air temperatures from the European Centre for Medium-Range Weather Forecasts reanalysis v5, we obtain global monthly averages of TC at high spatial (31 km) and temporal (1 h) resolution. TCs are analyzed as a function of time of the day, time of the year, location and land cover. Daytime maxima are observed from 1130 to 1330 local time, from 5–10 K in winter to 10–30 K in summer. A large dependency on land cover type is observed, both in the magnitude of the daily variations, and in the seasonality. For bare soils, shrublands, sparse and herbaceous vegetation, a maximum is seen in summer with daily TC amplitudes over 30 K. In contrast, for forests, wetlands, and croplands, the seasonal maximum occurs in spring, with daily variations below 15 K. Nighttime TCs typically range between −5 and −10 K. Occasionally, very favorable nighttime measurement conditions occur during winter and autumn due to large temperature inversions. Throughout the paper, we illustrate important concepts by means of satellite observations of NH 3 over the Po Valley (Italy).

Applicability of the InSAR technique for slope monitoring

Interferometry is a technique that can be applied to SAR (Synthetic Aperture Radar) images that allow obtaining Digital Elevation Models, displacement measurements and assistance for monitoring large areas and/or engineering constructions. The objective of this paper is to present aspects related to the technique of Interferometry SAR (InSAR) applied to the monitoring of slopes. To do so, a systematic bibliographical review was used, through research platforms in scientific collections, in order to highlight the functioning and method of operation of InSAR. Besides that, the practical experience of the authors contributed to a critical analysis of the remote sensing technique addressed. The results show theoretical aspects related to the operation of SARs on board satellites, highlighting their characteristics, orbital systems, types of imaging geometry, as well as the principles of interferometric processing of SAR images. Practical applications demonstrate the potential of InSAR with an emphasis on slope monitoring, highlighting its ability to acquire topographic information on a millimeter scale, monitoring the long-term temporal evolution of displacements, the possibility of composing a monitoring system allowing directing the implementation of other instruments for evaluating the in situ conditions and some limitations regarding the time interval (satellite revisit time) for the acquisition of the displacement data.

Three-dimensional ionospheric conductivity associated with pulsating auroral patches: reconstruction from ground-based optical observations

Abstract. Pulsating auroras (PsAs) appear over a wide area within the aurora oval in the midnight–morning–noon sector. In previous studies, observations by magnetometers on board satellites have reported the presence of field-aligned currents (FACs) near the edges and interiors of pulsating aurora patches. PsAs are thus a key research target for understanding the magnetosphere–ionosphere coupling process. However, the three-dimensional (3-D) structure of the electric currents has yet to be clarified, since each satellite observation is limited to a single dimension along its orbit. This study's aim was a reconstruction of the 3-D structure of ionospheric conductivity, which is necessary to elucidate the 3-D ionospheric current. Tomographic analysis was used to estimate the 3-D ionospheric conductivity for rapidly changing auroral phenomena such as PsAs. The reconstructed Hall conductivity reached its maximum value of 1.4 × 10−3 S m−1 at 94 km altitude, while the Pedersen conductivity reached its maximum value of 2.6 × 10−4 S m−1 at 116 km altitude. A secondary peak in the Pedersen conductivity, due to electron motion, at 9.9 × 10−5 S m−1 appears at 86 km altitude. The electron Pedersen conductivity maximum value in the D region was approximately 38 % of the ion Pedersen conductivity maximum value in the E region. The FAC, derived under the assumption of a uniform ionospheric electric field, was approximately 70 µA m−2 near the edge of the PsA patch. This FAC value was approximately 10 times that observed by satellites in previous studies. If the conductivity around the patch is underestimated or the assumption of a uniform field distribution is incorrect, the FAC could be overestimated. By contrast, due to sharper boundary structures, the FAC could actually have had such a large FAC.

Microvibration simulation of reaction wheel ball bearings

Microvibrations on board satellites are a well-known problem affecting their pointing accuracy by inducing oscillations to the line-of-sight. Fast rotating devices, such as reaction wheel assemblies (RWAs) are a major cause of these onboard mechanical disturbances. Imbalances in the flywheel, as well as imperfections in the ball-bearing components yield low-amplitude vibrations that are transmitted to the satellite structure, adversely affecting the performance of sensitive onboard instruments. This research focuses on the RWA’s ball bearing microvibration frequency and amplitude characterization. This is achieved by introducing specific geometry imperfections on the ball bearing rotating elements. In addition, variables such as the ball bearing preload, the number of balls and their dimensions are altered to understand how the bearing microvibration changes in order to minimize it. Finally, available physical microvibration test data is used to compare and cross-correlate the microvibration waterfall plots obtained from the transient FEA of the parametrized bearing models introduced herein. This work provides a better understanding of the relation between the main structural ball bearing parameters and the microvibration disturbances emitted by the RWA to minimize its mechanical noise.

The Estimation of Water Quality Parameters in Lentic Environments Through Remote Sensing Technologies: a Review of the Past Two Decades

The use of remote sensing technology applied to measure the quality of continental waters has grown exponentially since the turn of the century. Using different sensors on board satellites or airborne platforms, the estimation of water quality parameters has been carried out through both empirical and analytical approaches. This work aims to review the specific scientific production of the last two decades to assess how the evolution of the sensors and platforms have affected the potential and the limitations of remote sensing technologies to estimate water quality parameters in lakes and reservoirs. The study also focuses on the accuracy of remote sensing techniques for the major optically active parameters: chlorophyll-a and phycocyanin, Secchi disk depth and turbidity. The article is subdivided by sections dedicated to each of these parameters. A review of remote sensing platforms and sensors precedes the parameters sections. The past 20 years have brought a large body of articles on how remote sensing data can be used to estimate these parameters. Empirical methods dominate overwhelmingly with a four to one proportion over analytical approaches. Environmental factors such as season, complexity of water and concentration loads appear to exert a strong control over the quality of the results. Recent platforms and sensors have brought noticeable improvements over results achieved in this period.

Hail Climatology in the Mediterranean Basin Using the GPM Constellation (1999–2021)

The impacts of hailstorms on human beings and structures and the associated high economic costs have raised significant interest in studying storm mechanisms and climatology, thus producing a substantial amount of literature in the field. To contribute to this effort, we have explored the hail frequency in the Mediterranean basin during the last two decades (1999–2021) on the basis of hail occurrences derived from the observations of the microwave radiometers on board satellites of the Global Precipitation Measurement Constellation (GPM-C) from 2014 (date of GPM Core Observatory launch) onwards and merging multiple other satellite platforms prior to 2014. According to the MWCC-H method, two hail event categories (hail and super hail) are identified, and their spatiotemporal distributions are evaluated to identify the hail development areas in the Mediterranean and the corresponding monthly climatology of hail occurrences. Our results show that the northern sectors of the domain (France, Alpine Region, Po Valley, and Central-Eastern Europe) tend to be hit by hailstorms from June to August, while the central sectors (from Spain to Turkey) are more affected as autumn approaches. The trend analysis shows that the mean number of hail events over the entire domain tends to substantially increase, showing a higher increment during 2010–2021 than during 1999–2010. This behavior was particularly enhanced over Southern Italy and the Balkans. Our findings point to the existence of “sub-hotspots”, i.e., Mediterranean regions most susceptible to hail events and thus possibly more vulnerable to climate change effects.

Visibility Study in a Chief-Deputy Formation for CMB Polarization Missions

Scientific instruments on board satellites are becoming increasingly sensitive, making it imperative to submit these instruments to a thorough calibration. In-flight calibration could be largely improved by using an ancillary microsatellite flying in formation with the main satellite and emitting a well-defined and known reference signal. Due to the main satellite attitude motion, the calibration satellite and therefore, its calibration signal, will only enter the instrument FoV (Field of View) at certain instants. It is not intuitive how frequently and during how much time this will happen, or how this depends on the scan strategy. In the present work, the available time for calibration and its characteristics in terms of total, mean, and maximum duration are studied, deriving analytical expressions for these quantities. These expressions are validated numerically and allow us to assess the impact of different scan strategies and to evaluate the most suitable region to locate the calibration satellite. The focal plane of the instrument is also modelled to evaluate the calibration process at detector level, calculating the number of detectors viewed and the direction of the polarized signal that they received. For this last analysis, only numerical methods have been employed. The tools are finally used in a case study in order to show how they can be employed to test, evaluate, and optimize scanning strategies and relative positions. The tools presented in this work can be easily adapted to evaluate more generally the characteristics of the observation of each point in the sky for a given scan strategy and instrument FoV.

Research on Control of Stewart Platform Integrating Small Attitude Maneuver and Vibration Isolation for High-Precision Payloads on Spacecraft

The Stewart platform, a classical mechanism proposed as the parallel operation apparatus of robots, is widely used for vibration isolation in various fields. In this paper, a design integrating both small attitude control and vibration isolation for high-precision payloads on board satellites is proposed. Our design is based on a Stewart platform equipped with voice-coil motors (VCM) to provide control force over the mechanism. The coupling terms in the dynamic equations of the legs are removed as the total disturbance by the linear active disturbance rejection control (LADRC). Attitude maneuver and vibration isolation performance is verified by numerical simulations.

An FPGA-Based Hardware Accelerator for CNNs Inference on Board Satellites: Benchmarking with Myriad 2-Based Solution for the CloudScout Case Study

In recent years, research in the space community has shown a growing interest in Artificial Intelligence (AI), mostly driven by systems miniaturization and commercial competition. In particular, the application of Deep Learning (DL) techniques on board Earth Observation (EO) satellites might lead to numerous advantages in terms of mitigation of downlink bandwidth constraints, costs, and increment of the satellite autonomy. In this framework, the CloudScout project, funded by the European Space Agency (ESA), represents the first time in-orbit demonstration of a Convolutional Neural Network (CNN) applied to hyperspectral images for cloud detection. The first instance of this use case has been done with an INTEL Myriad 2 VPU on board a CubeSat optimized for low cost, size, and power efficiency. Nevertheless, this solution introduces multiple drawbacks due to its design not specifically being for the space environment, thus limiting its applicability to short-lifetime Low Earth Orbit (LEO) applications. The current work provides a benchmark between the Myriad 2 and our custom hardware accelerator designed for Field Programmable Gate Arrays (FPGAs). The metrics used for comparison include inference time, power consumption, space qualification, and components. The obtained results show that the FPGA-based solution is characterized by a reduced inference time, and a higher possibility of customization, but at the cost of greater power consumption and a longer Time to Market. As a conclusion, the proposed approach might extend the potential market of DL-based solutions to long-term LEO or interplanetary exploration missions through deployment on space-qualified FPGAs, with a limited cost in energy efficiency.

Simplified Thermal Model for Absolute Radiometer Simulation

Abstract The study of solar radiation in space has become something necessary, motivating the launch of radiometers on board satellites dedicated to perform total solar irradiance (TSI) measurements and to build a record of their behavior over the years, thus making these data essential for meteorology and climatology. In this study, we propose a simplified model to understand the thermal behavior of absolute radiometers, which are used in this type of measurement. The model considers the heat transfer among parts through conduction and loss only by radiation since the instrument operates in a space environment. The goal is to understand how each component interferes with sensitivity and response time of the instrument depending on its design, material, volume, and thermal contact. The model was applied to data generated by a prototype for validation.

Reduced-Complexity End-to-End Variational Autoencoder for on Board Satellite Image Compression

Recently, convolutional neural networks have been successfully applied to lossy image compression. End-to-end optimized autoencoders, possibly variational, are able to dramatically outperform traditional transform coding schemes in terms of rate-distortion trade-off; however, this is at the cost of a higher computational complexity. An intensive training step on huge databases allows autoencoders to learn jointly the image representation and its probability distribution, possibly using a non-parametric density model or a hyperprior auxiliary autoencoder to eliminate the need for prior knowledge. However, in the context of on board satellite compression, time and memory complexities are submitted to strong constraints. The aim of this paper is to design a complexity-reduced variational autoencoder in order to meet these constraints while maintaining the performance. Apart from a network dimension reduction that systematically targets each parameter of the analysis and synthesis transforms, we propose a simplified entropy model that preserves the adaptability to the input image. Indeed, a statistical analysis performed on satellite images shows that the Laplacian distribution fits most features of their representation. A complex non parametric distribution fitting or a cumbersome hyperprior auxiliary autoencoder can thus be replaced by a simple parametric estimation. The proposed complexity-reduced autoencoder outperforms the Consultative Committee for Space Data Systems standard (CCSDS 122.0-B) while maintaining a competitive performance, in terms of rate-distortion trade-off, in comparison with the state-of-the-art learned image compression schemes.

Artificial Neural Networks to Retrieve Land and Sea Skin Temperature from IASI

Surface skin temperature (Tskin) derived from infrared remote sensors mounted on board satellites provides a continuous observation of Earth’s surface and allows the monitoring of global temperature change relevant to climate trends. In this study, we present a fast retrieval method for retrieving Tskin based on an artificial neural network (ANN) from a set of spectral channels selected from the Infrared Atmospheric Sounding Interferometer (IASI) using the information theory/entropy reduction technique. Our IASI Tskin product (i.e., TANN) is evaluated against Tskin from EUMETSAT Level 2 product, ECMWF Reanalysis (ERA5), SEVIRI observations, and ground in situ measurements. Good correlations between IASI TANN and the Tskin from other datasets are shown by their statistic data, such as a mean bias and standard deviation (i.e., [bias, STDE]) of [0.55, 1.86 °C], [0.19, 2.10 °C], [−1.5, 3.56 °C], from EUMETSAT IASI L-2 product, ERA5, and SEVIRI. When compared to ground station data, we found that all datasets did not achieve the needed accuracy at several months of the year, and better results were achieved at nighttime. Therefore, comparison with ground-based measurements should be done with care to achieve the ±2 °C accuracy needed, by choosing, for example, a validation site near the station location. On average, this accuracy is achieved, in particular at night, leading to the ability to construct a robust Tskin dataset suitable for Tskin long-term spatio-temporal variability and trend analysis.

Methane and nitrous oxide from ground-based FTIR at Addis Ababa: observations, error analysis, and comparison with satellite data

Abstract. A ground-based, high-spectral-resolution Fourier transform infrared (FTIR) spectrometer has been operational in Addis Ababa, Ethiopia (9.01∘ N latitude, 38.76∘ E longitude; 2443 m altitude above sea level), since May 2009 to obtain information on column abundances and profiles of various constituents in the atmosphere. Vertical profile and column abundances of methane and nitrous oxide are derived from solar absorption measurements taken by FTIR for a period that covers May 2009 to March 2013 using the retrieval code PROFFIT (V9.5). A detailed error analysis of CH4 and N2O retrieval are performed. Averaging kernels of the target gases shows that the major contribution to the retrieved information comes from the measurement. Thus, average degrees of freedom for signals are found to be 2.1 and 3.4, from the retrieval of CH4 and N2O for the total observed FTIR spectra. Methane and nitrous oxide volume mixing ratio (VMR) profiles and column amounts retrieved from FTIR spectra are compared with data from the reduced spectral resolution Institute of Meteorology and Climate Research/Instituto de Astrofísica de Andalucía (IMK/IAA) MIPAS (Version V5R_CH4_224 and V5R_N2O_224), the Microwave Limb Sounder (MLS) (MLS v3.3 of N2O and CH4 derived from MLS v3.3 products of CO, N2O, and H2O), and the Atmospheric Infrared Sounder (AIRS) sensors on board satellites. The averaged mean relative difference between FTIR methane and the three correlative instruments MIPAS, MLS, and AIRS are 4.2 %, 5.8 %, and 5.3 % in the altitude ranges of 20 to 27 km, respectively. However, the biases below 20 km are negative, which indicates the profile of CH4 from FTIR is less than the profiles derived from correlative instruments by −4.9 %, −1.8 %, and −2.8 %. The averaged positive bias between FTIR nitrous oxide and correlative instrument, MIPAS, in the altitude range of 20 to 27 km is 7.8 %, and a negative bias of −4 % at altitudes below 20 km. An averaged positive bias of 9.3 % in the altitude range of 17 to 27 km is obtained for FTIR N2O with MLS. In all the comparisons of CH4 from FTIR with data from MIPAS, MLS, and AIRS, sensors on board satellites indicate a negative bias below 20 km and a positive bias above 20 km. The mean error between partial-column amounts of methane from MIPAS and the ground-based FTIR is −5.5 %, with a standard deviation of 5 % that shows very good agreement as exhibited by relative differences between vertical profiles. Thus, the retrieved CH4 and N2O VMR and column amounts from Addis Ababa, tropical site, is found to exhibit very good agreement with all coincident satellite observations. Therefore, the bias obtained from the comparison is comparable to the precision of FTIR measurement, which allows the use of data in further scientific studies as it represents a unique environment of tropical Africa, a region poorly investigated in the past.

Polygon Based Topology Formation and Information Gathering in Satellite Based Wireless Sensor Network

The satellite network is one of the major source of information and these days small satellites are gaining lot of focus. The group of small satellites form a distributed network that work collaboratively to accomplish the mission task. These networks are very similar to the terrestrial wireless sensor network in terms of restricted resources and constrained capabilities. Sometimes, network of small satellites is also called as space based wireless sensor network (SBWSN). In any distributed network, topology formation and its control plays a significant role. This is true for SBWSN also. The topology in SBWSN decides the area of coverage (field of view), time of coverage, data gathered and data transmitted to ground station. The proposed topology is a distributed network of small satellites formed by trivalent, toroidal and spherical polyhedron graph forming a fullerene, which is called as polygon based network topology (PBNT). It comprises of both pentagonal (Fp) and hexagonal (Fh) faces with K regular graphs such that K ≥ 3 with genus equal to 1. It also satisfies Eulers formula with n vertices. The fullerene comprises of simple rings and each of this ring forms the cluster. Each cluster is further represented as a triangular grid, that is linearly convex or non-linear, with K-connected graph along with Hamiltonian extendible cycle. The nodes/satellites on the triangular grid represent sensing nodes (low capability nodes/satellites), while the vertices of the ring are sink nodes (higher capability nodes/satellites). In this work, the topology is formed by small satellites (pico or nano satellites). In the proposed topology formation, the network is considered as virtual network with logical neighbours forming the cluster. Each node in the cluster covers a particular swath for a particular time interval based on the mission payload and on the p3 tiling. In the simulation, we consider n small satellites being placed in low earth orbit (LEO), (where n ranges from 3 to 150). The performance enhancements are seen during simulation in the following parameters, (1) Coverage Area: The coverage area increases as multiple satellites have different field of view at different times. (2) Reduces Gaps:The proposed distributed network also minimises uncovered areas as multiple satellites cover the target location at different time stamp which is not possible by a single large satellite. (3) Increase in Data Throughput: Each satellite in the network transmits data, when it is at perigee. The data throughput of the network increases, as data is transmitted by multiple satellites. Therefore, the throughput is increased by n-fold. (4) Continuous Connectivity: The data captured by one satellite in the network is made available to other using multi-hop communication. Thus the proposed topology also increases the continuous connectivity between satellites and also with the ground station. (5) Increases lifetime and Network Reliability: The SBWSN accomplishes its mission task even when one/more satellites encounters functional failure. The satellites in the network can reconfigure themselves and continue the mission task. Thus SBWSN also reduces the risk of mission failure and ensures mission reliability. Due to reconfiguration the lifetime of the network is also increased. The proposed topology is used for small satellites (specially nano and pico satellites) network, which permit the single board satellite weighing less than 10 kg (Pico satellite less than 1 kg and nano satellites less than 10 kg). The advantage of these small satellites network over single large satellite is low cost and reduced development time, as it uses commercially of the shelf (COTS) components. In this paper, we propose network architecture formed by the spherically embedded clusters formed by polyhedron. The vertices of polyhedron have both pentagonal (Fp) and hexagonal (Fh) faces with K regular graphs such that K ≥ 3, with genus equal to 1. The vertices of these polyhedron form the sink nodes and the other nodes are sensing nodes. Here satellites and nodes are interchangeably used. Sensing nodes are used for data gathering (pico/nano), while sink nodes are higher capability nodes which perform computational extensive operations in the network (nano or macro satellites). The sensing and sink nodes transmit data to the ground station when they are at perigee. The main objective of the proposed work is, technology demonstration of low cost, distributed small satellites network for earth observations replacing single huge satellite.

An UAV and Satellite Multispectral Data Approach to Monitor Water Quality in Small Reservoirs

A multi-sensor and multi-scale monitoring tool for the spatially explicit and periodic monitoring of eutrophication in a small drinking water reservoir is presented. The tool was built with freely available satellite and in situ data combined with Unmanned Aerial Vehicle (UAV)-based technology. The goal is to evaluate the performance of a multi-platform approach for the trophic state monitoring with images obtained with MultiSpectral Sensors on board satellites Sentinel 2 (S2A and S2B), Landsat 8 (L8) and UAV. We assessed the performance of three different sensors (MultiSpectral Instrument (MSI), Operational Land Imager (OLI) and Rededge Micasense) for retrieving the pigment chlorophyll-a (chl-a), as a quantitative descriptor of phytoplankton biomass and trophic level. The study was conducted in a waterbody affected by cyanobacterial blooms, one of the most important eutrophication-derived risks for human health. Different empirical models and band indices were evaluated. Spectral band combinations using red and near-infrared (NIR) bands were the most suitable for retrieving chl-a concentration (especially 2 band algorithm (2BDA), the Surface Algal Bloom Index (SABI) and 3 band algorithm (3BDA)) even though blue and green bands were useful to classify UAV images into two chl-a ranges. The results show a moderately good agreement among the three sensors at different spatial resolutions (10 m., 30 m. and 8 cm.), indicating a high potential for the development of a multi-platform and multi-sensor approach for the eutrophication monitoring of small reservoirs.

Use of Sentinel-1 radar observations to evaluate snowmelt dynamics in alpine regions

Abstract. Knowing the timing and the evolution of the snow melting process is very important, since it allows the prediction of (i) the snowmelt onset, (ii) the snow gliding and wet-snow avalanches, (iii) the release of snow contaminants, and (iv) the runoff onset. The snowmelt can be monitored by jointly measuring snowpack parameters such as the snow water equivalent (SWE) or the amount of free liquid water content (LWC). However, continuous measurements of SWE and LWC are rare and difficult to obtain. On the other hand, active microwave sensors such as the synthetic aperture radar (SAR) mounted on board satellites are highly sensitive to LWC of the snowpack and can provide spatially distributed information with a high resolution. Moreover, with the introduction of Sentinel-1, SAR images are regularly acquired every 6 d over several places in the world. In this paper we analyze the correlation between the multitemporal SAR backscattering and the snowmelt dynamics. We compared Sentinel-1 backscattering with snow properties derived from in situ observations and process-based snow modeling simulations for five alpine test sites in Italy, Germany and Switzerland considering 2 hydrological years. We found that the multitemporal SAR measurements allow the identification of the three melting phases that characterize the melting process, i.e., moistening, ripening and runoff. In particular, we found that the C-band SAR backscattering decreases as soon as the snow starts containing water and that the backscattering increases as soon as SWE starts decreasing, which corresponds to the release of meltwater from the snowpack. We discuss the possible reasons of this increase, which are not directly correlated to the SWE decrease but to the different snow conditions, which change the backscattering mechanisms. Finally, we show a spatially distributed application of the identification of the runoff onset from SAR images for a mountain catchment, i.e., the Zugspitze catchment in Germany. Results allow us to better understand the spatial and temporal evolution of melting dynamics in mountain regions. The presented investigation could have relevant applications for monitoring and predicting the snowmelt progress over large regions.

Multiplatform Earth Observation Systems for Monitoring Water Quality in Vulnerable Inland Ecosystems: Maspalomas Water Lagoon

The accurate monitoring of water quality indicators, bathymetry and distribution of benthic habitats in vulnerable ecosystems is key to assessing the effects of climate change, the quality of natural areas and to guide appropriate biodiversity, tourism or fisheries policies. Coastal and inland water ecosystems are very complex but crucial due to their richness and primary production. In this context, remote sensing can be a reliable way to monitor these areas, mainly thanks to satellite sensors’ improved spatial and spectral capabilities and airborne or drone instruments. In general, mapping bodies of water is challenging due to low signal-to-noise (SNR) at sensor level, due to the very low reflectance of water surfaces as well as atmospheric effects. Therefore, the main objective of this work is to provide a robust processing framework to estimate water quality parameters in inland shallow waters using multiplatform data. More specifically, we measured chlorophyll concentrations (Chl-a) from multispectral and hyperspectral sensors on board satellites, aircrafts and drones. The Natural Reserve of Maspalomas, Canary Island (Spain), was chosen for the study because of its complexity as well as being an inner lagoon with considerable organic and inorganic matter and chlorophyll concentration. This area can also be considered a well-known coastal-dune ecosystem attracting a large amount of tourists. The water quality parameter estimated by the remote sensing platforms has been validated using co-temporal in situ measurements collected during field campaigns, and quite satisfactory results have been achieved for this complex ecosystem. In particular, for the drone hyperspectral instrument, the root mean square error, computed to quantify the differences between the estimated and in situ chlorophyll-a concentrations, was 3.45 with a bias of 2.96.

Excitation of VLF perturbations in the F-region of the ionosphere by beating HF O-mode waves

Action of two HF O-mode pump waves with different frequencies on the ionosphere is suggested as a source of artificial quasielectrostatic VLF perturbations in the F-layer. Such perturbations with the beat frequency are excited in the vicinity of small-scale artificial plasma irregularities (striations) by upper hybrid resonance (UHR) oscillations. While UHR oscillations are trapped inside striations, the generated VLF perturbations are not trapped in such small-scale plasma depletions. These VLF oscillations propagate at small angles with respect to the background magnetic field. Estimates show that the excited VLF electric fields are strong enough, while magnetic perturbations are much weaker. The scattering of quasielectrostatic VLF perturbations on small-scale plasma irregularities in the F-layer provides the generation of the whistler mode waves that can be detected on board satellites.

Plasma Wave Observation in Space Via Scientific Satellites

Space plasmas are essentially collisionless. Kinetic energies of plasmas are transferred through wave-particle interactions. Since plasmas are dispersive media, plasma waves show various features depending on how and which plasma wave modes interact with particles. Plasma wave observations via scientific satellites have a key role in knowing physical processes in space. Plasma wave investigation systems on board satellites are dedicated to the observations of electric field and magnetic field components of plasma waves. They consist of sensors, receivers and an onboard digital processing unit. While dipole antennas are used for electric field sensors, search coil magnetometers or loop antennas are used for magnetic field sensors. Plasma wave receivers are a kind of sophisticated radio receivers. The recent plasma wave receiver is designed as a waveform receiver that can save its mass and size. In addition to waveforms, the waveform receiver provides frequency spectra which are calculated in the digital processing unit from the observed waveforms. The present paper introduces plasma wave observation by scientific satellites on the basis of the latest design of plasma wave investigation systems.

Alteration of Particle Drift Resonance Dynamics Near Poloidal Mode Field Line Resonance Structures

AbstractWe examine the effect on drift‐resonant particle dynamics of a strongly peaked internally driven poloidal mode field line resonance (FLR; with specified frequency ω in the Pc5 range and azimuthal mode number m≫1). Using an analytic magneto‐hydrodynamic model in a dipole field to describe the ultra low frequency wave mode, we use the bounce‐averaged formalism of Northrop (1963) to obtain equations of motion for charged particles in the wave frame and find an analytic solution for the case of a temporally constant ultra low frequency wave amplitude profile. Focusing on equatorially mirroring electrons in this study, we demonstrate that, for sufficiently peaked radial profiles, multiple drift resonances appear that are associated with the FLR peak. These are in addition to the well‐known zeroth‐order drift resonance location, occurring when the unperturbed drift speed satisfies the resonance condition ( ). The additional resonances arise because the strongly peaked FLR wave field components provide sufficiently strong perturbations to the azimuthal drift speed to cause multiple zero crossings in the resonance condition. These additional resonances have trapping periods much lower than that of the zeroth‐order resonance and considerably complicate the electron dynamics. Further properties of these resonances and their measurable effect on electron dynamics are discussed. For example, their effect on observations of energetic electron flux on board satellites in the vicinity of an FLR is calculated and shown to significantly distort the typical signature associated with drift resonance (modulations in electron flux at the wave frequency with a 180° phase change across the resonant energy).