Search Results (41)

³Cat-4 is the fourth member of the CubeSat series of UPC’s NanoSat Lab, and it was selected by the ESA Academy’s Fly Your Satellite! program in 2017. This mission aims at demonstrating the capabilities of nano-satellites, and in particular those based in the 1-Unit CubeSat standard, for challenging Earth Observation (EO) using Global Navigation Satellite System-Reflectometry (GNSS-R) and L-band microwave radiometry, as well as for Automatic Identification Systems (AIS). The following study presents the results of the thermal analysis carried out for this mission, evaluating different scenarios, including the most critical cases at both high and low temperatures. The results consider different albedos and orbital parameters in order to establish the optimal temperatures to achieve the best mission performance within the nominal temperatures, and in all operational modes of the satellite. Simulation results are included considering the thermal performance of other materials, such as Kapton, as well as the redesign of the optical properties of the satellite’s solar panels. The correlation with the thermal model and the TVAC test campaign was conducted at the ESA ESEC-GALAXIA facilities in Belgium. Full article

Real-Time RFI Detection and Flagging (RT-RDF) for microwave radiometers is a versatile new FPGA algorithm designed to detect and flag Radio-Frequency Interference (RFI) in microwave radiometers. This block utilizes computationally-efficient techniques to identify and analyze RF signals, allowing the system to take appropriate measures to mitigate interference and maintain reliable performance. With L-Band microwave radiometry as the main application, this RFI detection algorithm focuses on the Kurtogram and Spectrogram to detect non-Gaussian behavior. To gain further modularity, an FFT-based filter bank is used to divide the receiver’s bandwidth into several sub-bands within the band of interest of the instrument, depending on the application. Multiple blanking strategies can then be applied in each band using the provided detection flags. The algorithm can be re-configured in the field, for example with dynamic integration times to support operation in different environments, or configurable thresholds to account for variable RFI environments. A validation and testing campaign has been performed on multiple scenarios with the ARIEL commercial microwave radiometer, and the results confirm the excellent performance of the system. Full article

The issue of Radio Frequency Interference (RFI) is a widespread problem in most microwave Earth observation missions, and passive instruments are particularly sensitive to RFI. This is the case for SMOS, Soil Moisture and Ocean Salinity, a satellite of the European Space Agency, which operates in the 1400–1427 MHz band, where all emissions are prohibited. Notwithstanding this regulatory framework, SMOS has been affected by RFI all around the world since the beginning of operations in 2010. In the first years of operations, manual detection processes and reporting of RFI to National Regulatory Authorities were in place in order to mitigate the detected sources. After 12 years, a tool called ERMIT (ESA RFI Monitoring and Information Tool) has been developed at ESAC (European Space Astronomy Center). This tool helps the SMOS RFI team in its spectrum monitoring tasks (e.g., RFI monitoring, logging, and reporting) thus allowing it to counteract RFI pollution more efficiently, providing external users with detailed and user-friendly information on the L-band RFI observed by SMOS. The ERMIT tool is now publicly available. This document aims at describing the needs that lead to the development of ERMIT and at presenting the information made available by it. Full article

The 3Cat-4 mission aims at demonstrating the capabilities of a CubeSat to perform Earth Observation (EO) by integrating a combined GNSS-R and Microwave Radiometer payload into a 1-Unit CubeSat. One of the greatest challenges is the design of an antenna that respects the 1-Unit CubeSat envelope while operating at the different frequency bands: Global Positioning System (GPS) L1 and Galileo E1 band (1575 MHz), GPS L2 band (1227 MHz), and the microwave radiometry band (1400–1427 MHz). Moreover, it requires between 8 and 12 dB of directivity depending on the band whilst providing at least 10 dB of front-to-back lobe ratio in L1 and L2 GPS bands. After a trade-off analysis on the type of antenna that could be used, a helix antenna was found to be the most suitable option to comply with the requirements, since it can be stowed during launch and deployed once in orbit. This article presents the antenna design from a radiation performance point of view starting with a theoretical analysis, then presenting the numerical simulations, the measurements in an Engineering Model (EM), and finally the final design and performance of the Flight Model (FM). Full article

Satellite remote sensing permits large-scale monitoring of coastal waters through synoptic measurements of water-leaving radiance that can be scaled to relevant water quality metrics and in turn help inform local and regional responses to a variety of stressors. As both the incident and water-leaving radiance are affected by interactions with the intervening atmosphere, the efficacy of atmospheric correction algorithms is essential to derive accurate water-leaving radiometry. Modern ocean color satellite sensors such as the Ocean and Land Colour Instrument (OLCI) onboard the Copernicus Sentinel-3A and -3B satellites are providing unprecedented operational data at the higher spatial, spectral, and temporal resolution that is necessary to resolve optically complex coastal water quality. Validating these satellite-based radiance measurements with vicarious in situ radiometry, especially in optically complex coastal waters, is a critical step in not only evaluating atmospheric correction algorithm performance but ultimately providing accurate water quality metrics for stakeholders. In this study, a regional in situ dataset from the Chesapeake Bay was used to evaluate the performance of four atmospheric correction algorithms applied to OLCI Level-1 data. Images of the Chesapeake Bay are processed through a neural-net based algorithm (C2RCC), a spectral optimization-based algorithm (POLYMER), an iterative two-band bio-optical-based algorithm (L2gen), and compared to the standard Level-2 OLCI data (BAC). Performance was evaluated through a matchup analysis to in situ remote sensing reflectance data. Statistical metrics demonstrated that C2RCC had the best performance, particularly in the longer wavelengths (>560 nm) and POLYMER contained the most clear day coverage (fewest flagged data). This study provides a framework with associated uncertainties and recommendations to utilize OLCI ocean color data to monitor the water quality and biogeochemical dynamics in Chesapeake Bay. Full article

This study assesses the suitability of the two-stream microwave emission model in simulating brightness temperature (T_B^p) and retrieving liquid water content (θ_liq) at L-band in combination with the four-phase dielectric model for both thawed and frozen soil. Both single (SCA) and double (DCA) channel algorithms are adopted using both ground-based ELBARA-III and spaceborne SMAP measurements conducted in a Tibetan grassland site. The ELBARA-III measured T_B^p are smaller than the SMAP measurements in the warm season due to a lower value of average θ_liq presented within the ELBARA-III footprint. The two-stream emission model configured with SMAP vegetation and surface roughness parameterization underestimates both ELBARA-III and SMAP measured T_B^p at horizontal polarization in the cold season, and overestimates the vertical polarized measurements (T_B^V) in the warm season. Implementation of a new surface roughness and vegetation parameterization resolves above deficiency, and the simulations capture better large-scale SMAP measurements in comparison to these performed for the ELBARA-III footprint. The dynamics of in situ θ_liq are better reproduced by retrievals using the SCA based on T_B^V measurements (SCA-V), whereby the SCA-V retrievals using the SMAP ascending overpass measurements shows the best results with an unbiased root-mean-square error (ubRMSE) of 0.035 m³ m⁻³ that outperforms the SMAP mission specification. Full article

A better understanding of the water and energy cycles at climate scale in the Third Pole Environment is essential for assessing and understanding the causes of changes in the cryosphere and hydrosphere in relation to changes of plateau atmosphere in the Asian monsoon system and for predicting the possible changes in water resources in South and East Asia. This paper reports the following results: (1) A platform of in situ observation stations is briefly described for quantifying the interactions in hydrosphere-pedosphere-atmosphere-cryosphere-biosphere over the Tibetan Plateau. (2) A multiyear in situ L-Band microwave radiometry of land surface processes is used to develop a new microwave radiative transfer modeling system. This new system improves the modeling of brightness temperature in both horizontal and vertical polarization. (3) A multiyear (2001–2018) monthly terrestrial actual evapotranspiration and its spatial distribution on the Tibetan Plateau is generated using the surface energy balance system (SEBS) forced by a combination of meteorological and satellite data. (4) A comparison of four large scale soil moisture products to in situ measurements is presented. (5) The trajectory of water vapor transport in the canyon area of Southeast Tibet in different seasons is analyzed, and (6) the vertical water vapor exchange between the upper troposphere and the lower stratosphere in different seasons is presented. Full article

Microwave Imager Combined Active and Passive (MICAP), which is a package of active and passive microwave instruments including L/C/K-band radiometers and L-band scatterometer, has been approved to be taken onbord the Chinese Ocean Salinity Mission. The L-band one-dimensional synthetic aperture radiometer (L-Rad) is the key part of MICAP to measure sea surface salinity (SSS). Since radio frequency interference (RFI) is reported as a serious threat to L-band radiometry, the RFI detection and mitigation techniques must be carefully designed before launch. However, these techniques need to be developed based on the knowledge of how RFI affects complex correlation, visibility function, and reconstructed brightness temperature. This paper presents a time-domain signal modeling method for the simulation of interferometric measurement under RFI’s presences, and a simulation system for L-Rad is established accordingly. Several RFI cases are simulated with different RFI types, parameters, and positions; and the RFI characteristics upon L-Rad’s measurement are discussed. The proposed simulation system will be further dedicated to the design of RFI processing strategy onboard MICAP. Full article

The Federated Satellite System mission (FSSCat), winner of the 2017 Copernicus Masters Competition and the first ESA third-party mission based on CubeSats, aimed to provide coarse-resolution soil moisture estimations and sea ice concentration maps by means of the passive microwave measurements collected by the Flexible Microwave Payload-2 (FMPL-2). The mission was successfully launched on 3 September 2020. In addition to the primary scientific objectives, FMPL-2 data are used in this study to estimate sea surface salinity (SSS), correcting for the sea surface roughness using a wind speed estimate from the L-band microwave radiometer and GNSS-R data themselves. FMPL-2 was executed over the Arctic and Antarctic oceans on a weekly schedule. Different artificial neural network algorithms have been implemented, combining FMPL-2 data with the sea surface temperature, showing a root-mean-square error (RMSE) down to 1.68 m/s in the case of the wind speed (WS) retrieval algorithms, and RMSE down to 0.43 psu for the sea surface salinity algorithm in one single pass. Full article

Several methods have been developed to provide polar maps of sea ice thickness (SIT) from L-band brightness temperature (TB) and altimetry data. Current process-based inversion methods to yield SIT fail to address the complex surface characteristics because sea ice is subject to strong seasonal dynamics and ice-physical properties are often non-linearly related. Neural networks can be trained to find hidden links among large datasets and often perform better on convoluted problems for which traditional approaches miss out important relationships between the observations. The FSSCat mission launched on 3 September 2020, carries the Flexible Microwave Payload-2 (FMPL-2), which contains the first Reflected Global Navigation Satellite System (GNSS-R) and L-band radiometer on board a CubeSat—designed to provide TB data on global coverage for soil moisture retrieval, and sea ice applications. This work investigates a predictive regression neural network approach with the goal to infer SIT using FMPL-2 TB and ancillary data (sea ice concentration, surface temperature, and sea ice freeboard). Two models—covering thin ice up to 0.6 m and full-range thickness—were separately trained on Arctic data in a two-month period from mid-October to the beginning of December 2020, while using ground truth data derived from the Soil Moisture and Ocean Salinity (SMOS) and Cryosat-2 missions. The thin ice and the full-range models resulted in a mean absolute error of 6.5 cm and 23 cm, respectively. Both of the models allowed for one to produce weekly composites of Arctic maps, and monthly composites of Antarctic SIT were predicted based on the Arctic full-range model. This work presents the first results of the FSSCat mission over the polar regions. It reveals the benefits of neural networks for sea ice retrievals and demonstrates that moderate-cost CubeSat missions can provide valuable data for applications in Earth observation. Full article

CubeSat-based Earth Observation missions have emerged in recent times, achieving scientifically valuable data at a moderate cost. FSSCat is a two 6U CubeSats mission, winner of the ESA S³ challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched in September 2020. The first satellite, ³Cat-5/A, carries the FMPL-2 instrument, an L-band microwave radiometer and a GNSS-Reflectometer. This work presents a neural network approach for retrieving sea ice concentration and sea ice extent maps on the Arctic and the Antarctic oceans using FMPL-2 data. The results from the first months of operations are presented and analyzed, and the quality of the retrieved maps is assessed by comparing them with other existing sea ice concentration maps. As compared to OSI SAF products, the overall accuracy for the sea ice extent maps is greater than 97% using MWR data, and up to 99% when using combined GNSS-R and MWR data. In the case of Sea ice concentration, the absolute errors are lower than 5%, with MWR and lower than 3% combining it with the GNSS-R. The total extent area computed using this methodology is close, with 2.5% difference, to those computed by other well consolidated algorithms, such as OSI SAF or NSIDC. The approach presented for estimating sea ice extent and concentration maps is a cost-effective alternative, and using a constellation of CubeSats, it can be further improved. Full article

The Federated Satellite System mission (FSSCat) was the winner of the 2017 Copernicus Masters Competition and the first Copernicus third-party mission based on CubeSats. One of FSSCat’s objectives is to provide coarse Soil Moisture (SM) estimations by means of passive microwave measurements collected by Flexible Microwave Payload-2 (FMPL-2). This payload is a novel CubeSat based instrument combining an L1/E1 Global Navigation Satellite Systems-Reflectometer (GNSS-R) and an L-band Microwave Radiometer (MWR) using software-defined radio. This work presents the first results over land of the first two months of operations after the commissioning phase, from 1 October to 4 December 2020. Four neural network algorithms are implemented and analyzed in terms of different sets of input features to yield maps of SM content over the Northern Hemisphere (latitudes above 45° N). The first algorithm uses the surface skin temperature from the European Centre of Medium-Range Weather Forecast (ECMWF) in conjunction with the 16 day averaged Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate SM and to use it as a comparison dataset for evaluating the additional models. A second approach is implemented to retrieve SM, which complements the first model using FMPL-2 L-band MWR antenna temperature measurements, showing a better performance than in the first case. The error standard deviation of this model referred to the Soil Moisture and Ocean Salinity (SMOS) SM product gridded at 36 km is 0.074 m3/m3. The third algorithm proposes a new approach to retrieve SM using FMPL-2 GNSS-R data. The mean and standard deviation of the GNSS-R reflectivity are obtained by averaging consecutive observations based on a sliding window and are further included as additional input features to the network. The model output shows an accurate SM estimation compared to a 9 km SMOS SM product, with an error of 0.087 m3/m3. Finally, a fourth model combines MWR and GNSS-R data and outperforms the previous approaches, with an error of just 0.063 m3/m3. These results demonstrate the capabilities of FMPL-2 to provide SM estimates over land with a good agreement with respect to SMOS SM. Full article

The National Aeronautics and Space Administration’s (NASA’s) Soil Moisture Active–Passive (SMAP) radiometer has been providing geolocated power moments measured within a 24 MHz band in the protected portion of L-band, i.e., 1400–1424 MHz, with 1.2 ms and 1.5 MHz time and frequency resolutions, as its Level 1A data. This paper presents important spectral and temporal properties of the radio frequency interference (RFI) in the protected portion of L-band using SMAP Level 1A data. Maximum and average bandwidth and duration of RFI signals, average RFI-free spectrum availability, and variations in such properties between ascending and descending satellite orbits have been reported across the world. The average bandwidth and duration of individual RFI sources have been found to be usually less than 4.5 MHz and 4.8 ms; and the average RFI-free spectrum is larger than 20 MHz in most regions with exceptions over the Middle East and Central and Eastern Asia. It has also been shown that, the bandwidth and duration of RFI signals can vary as much as 10 MHz and 10 ms, respectively, between ascending and descending orbits over certain locations. Furthermore, to identify frequencies susceptible to RFI contamination in the protected portion of L-band, observed RFI signals have been assigned to individual 1.5 MHz SMAP channels according to their frequencies. It has been demonstrated that, contrary to common perception, the center of the protected portion can be as RFI contaminated as its edges. Finally, there have been no significant correlations noted among different RFI properties such as amplitude, bandwidth, and duration within the 1400–1424 MHz band. Full article

The Flexible Microwave Payload-2 is the GNSS-R and L-band Microwave Radiometer Payload on board ${}^3$Cat-5/A, one of the two 6-unit CubeSats of the FSSCat mission, which were successfully launched on 3 September 2020 on Vega flight VV16. The instrument occupies nearly a single unit of the CubeSat, and its goal is to provide sea-ice extension and thickness over the poles, and soil moisture maps at low-moderate resolution over land, which will be downscaled using data from Cosine Hyperscout-2 on board ${}^3$Cat-5/B. The spacecrafts are in a 97.5${}^{°}$ inclination Sun-synchronous orbit, and both the reflectometer and the radiometer have been successfully executed and validated over both the North and the South poles. This manuscript presents the results and validation of the first data sets collected by the instrument during the first two months of the mission. The results of the validation are showing a radiometric accuracy better than 2 K, and a sensitivity lower than the Kelvin. For the reflectometer, the results are showing that the sea-ice transition can be estimated even at short integration times (40 ms). The presented results shows the potential for Earth Observation missions based on CubeSats, which temporal and spatial resolution can be further increased by means of CubeSat constellations. Full article

L-band brightness temperature ($T_b$) is one of the key remotely-sensed variables that provides information regarding surface soil moisture conditions. In order to harness the information in $T_b$ observations, a radiative transfer model (RTM) is investigated for eventual inclusion into a data assimilation framework. In this study, $T_b$ estimates from the RTM implemented in the NASA Goddard Earth Observing System (GEOS) were evaluated against the nearly four-year record of daily $T_b$ observations collected by L-band radiometers onboard the Aquarius satellite. Statistics between the modeled and observed $T_b$ were computed over North America as a function of soil hydraulic properties and vegetation types. Overall, statistics showed good agreement between the modeled and observed $T_b$ with a relatively low, domain-average bias (0.79 K (ascending) and −2.79 K (descending)), root mean squared error (11.0 K (ascending) and 11.7 K (descending)), and unbiased root mean squared error (8.14 K (ascending) and 8.28 K (descending)). In terms of soil hydraulic parameters, large porosity and large wilting point both lead to high uncertainty in modeled $T_b$ due to the large variability in dielectric constant and surface roughness used by the RTM. The performance of the RTM as a function of vegetation type suggests better agreement in regions with broadleaf deciduous and needleleaf forests while grassland regions exhibited the worst accuracy amongst the five different vegetation types. Full article