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 m${}^3/$m${}^3$. 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 m${}^3/$m${}^3$. Finally, a fourth model combines MWR and GNSS-R data and outperforms the previous approaches, with an error of just 0.063 m${}^3/$m${}^3$. These results demonstrate the capabilities of FMPL-2 to provide SM estimates over land with a good agreement with respect to SMOS SM.

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.

Several methods have been developed to provide polar maps of sea ice thickness (SIT) from L-band brightness temperature (T${}_B$) 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 T${}_B$ 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 T${}_B$ 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.

The Federated Satellite Systems/ ³ Cat-5 (FSSCat) mission was the winner of the European Space Agency (ESA) Sentinel Small Satellite (S ³ ) Challenge and overall winner of the 2017 Copernicus Masters competition. It consisted of two six-unit CubeSats. The Earth observation payloads were 1) the Flexible Microwave Payload 2 (FMPL-2) onboard ³ Cat-5/A, an L-band microwave radiometer and GNSS reflectometer (GNSS-R) implemented using a softwaredefined radio (SDR), and 2) the HyperScout-2 onboard ³ Cat-5/B, a hyperspectral camera, with the first experiment using artificial intelligence to discard cloudy images. FSSCat was launched on 3 September 2020 and injected into a 535-km synchronous orbit. ³ Cat-5/A was operated for three months until the payload was probably damaged by a solar flare and coronal mass ejection. During this time, all scientific requirements were met, including the generation of coarse-resolution and downscaled soil moisture (SM) maps, sea ice extent (SIE) maps, concentration and thickness maps, and even wind speed (WS) and sea surface salinity (SSS) maps, which were not originally foreseen. ³ Cat-5/B was operated a few more months until the number of images acquired met the requirements. This article briefly describes the FSSCat mission and the FMPL-2 payload and summarizes the main scientific results.

Earth Observation missions have increasingly demanding requirements, i.e. higher spatial resolutions and SNR, and lower revisit times, etc. High resolution images have to be captured with precise pointing accuracy which is achieved by the satellite’s Attitude Determination and Control System (ADCS). This subsystem embeds multiple sensors and actuators capable of changing the satellite attitude by changing the torque applied to the platform. In small satellites, magnetorquers, and more recently reaction wheels as well, are the most frequently used actuators. These components fulfill their main purpose, but exhibit some drawbacks, notably a non-constant jitter that provokes non-desired oscillations of the spacecraft, which may result in blurred images. This work presents a deblurring methodology that uses data coming from the ADCS sensors to infer a spatially variant point-spread function. Moreover, the impact of having a lower temporal resolution than the ideal on the sensors is analyzed and a method is presented to improve it.

Hyperspectral imaging is a key tool in numerous remote sensing applications. Images with tens or even hundreds of spectral bands contain a wealth of information to retrieve multiple geophysical parameters, to perform target detection, or land classification with unbeatable high accuracies. In addition, hyperspectral sensors are becoming smaller, and today they even fit in CubeSats. However, the amount of data they generate is so large that satellite communication systems have severe limitations to download it, especially in SmallSats. It is therefore becoming urgent to develop efficient automated algorithms that can be executed in the limited capabilities of the onboard computers of these satellites, so as to reduce the amount of data to be stored and downloaded, while keeping as much information as possible for a given scene. In this work, a band selection algorithm has been designed to deal with this problem. The proposed algorithm consists of the sequential selection of the spectral bands ranked using the amount of information provided by each band, and also the correlation of these bands with the previously selected ones. The algorithm performance is assessed by means of a suite of classification tests with hyperspectral datasets from different sensors. Results show comparable or even better performance than other existing band selection algorithms, while outperforming in terms of computational complexity, which makes it more suitable for SmallSats with limited computing resources.