Principal Component Analysis (PCA) is a method based on statistics and linear algebra techniques, used in hyperspectral satellite imagery for data dimensionality reduction required in order to speed up and increase the performance of subsequent hyperspectral image processing algorithms. This paper introduces the PCA approximation method based on a geometric construction approach (gaPCA) method, an alternative algorithm for computing the principal components based on a geometrical constructed approximation of the standard PCA and presents its application to remote sensing hyperspectral images. gaPCA has the potential of yielding better land classification results by preserving a higher degree of information related to the smaller objects of the scene (or to the rare spectral objects) than the standard PCA, being focused not on maximizing the variance of the data, but the range. The paper validates gaPCA on four distinct datasets and performs comparative evaluations and metrics with the standard PCA method. A comparative land classification benchmark of gaPCA and the standard PCA using statistical-based tools is also described. The results show gaPCA is an effective dimensionality-reduction tool, with performance similar to, and in several cases, even higher than standard PCA on specific image classification tasks. gaPCA was shown to be more suitable for hyperspectral images with small structures or objects that need to be detected or where preponderantly spectral classes or spectrally similar classes are present.

This work shows the potential use of neural networks in the characterization of eruptive events monitored by satellite, through fast and automatic classification of multispectral images. The algorithm has been developed for the MODIS instrument and can easily be extended to other similar sensors. Six classes have been defined paying particular attention to image regions that represent the different surfaces that could possibly be found under volcanic ash clouds. Complex cloudy scenarios composed by images collected during the Icelandic eruptions of the Eyjafjallajökull (2010) and Grimsvötn (2011) volcanoes have been considered as test cases. A sensitivity analysis on the MODIS TIR and VIS channels has been performed to optimize the algorithm. The neural network has been trained with the first image of the dataset, while the remaining data have been considered as independent validation sets. Finally, the neural network classifier’s results have been compared with maps classified with several interactive procedures performed in a consolidated operational framework. This comparison shows that the automatic methodology proposed achieves a very promising performance, showing an overall accuracy greater than 84%, for the Eyjafjalla - jökull event, and equal to 74% for the Grimsvötn event.

In this article, a new technique for features extraction from SAR interferograms is presented. The technique combines the properties of auto-associative neural networks with those of more traditional approaches such as discrete Fourier transform or discrete wavelet transform. The feature extraction is chained to another neural module performing the estimation of the fault parameters characterizing a seismic event. The whole procedure has been validated with the experimental data acquired for the analysis of the dramatic L’Aquila earthquake which occurred in Italy in 2009. The results show the effectiveness of the approach either in terms of dimensionality reduction or in terms retrieval capabilities.

In this work data observed by the geostationary MSG-SEVIRI and the polar NASA-Terra/Aqua-MODIS orbiting satellite instruments, have been used for the proximal and distal monitoring of the 24-30 December 2018 Etna eruption. The combined use of the SEVIRI high repetition time and the MODIS high spatial resolution allows a reliable near real time volcanic characterization from the source to the atmosphere. For the proximal monitoring the parameters estimated are the eruption starts and duration and the volcanic plume top height, while the distal monitoring was inverted relying on the determination of the volcanic cloud altitude and the ash/SO 2 retrievals. Achieved products were validated by comparing these results with those observed remotely by ground based networks.Results obtained in this study show the ability of satellite-based systems to entirely follow eruptive events in near real time, offering a powerful tool to mitigate volcanic risk on both local population and airspace.

We present a proof-of-concept onboard cloud detection system for the PRISMA Second Generation (PSG) mission, which combines a secondary forward-looking RGB camera with deep learning (DL) models on a system-on-a-chip (SoC) field-programmable gate array (FPGA). The proposed system enables real-time cloud coverage assessment, optimizing primary hyperspectral payload data collection and supporting adaptive acquisition scheduling, offering an effective solution for enhancing onboard data processing in Earth Observation (EO) missions. To support efficient model development, we derived the design specifications of the secondary camera and constructed a dedicated dataset using astronaut-captured images from the International Space Station – the CloudISS-RGB dataset – employing a self-training approach to generate high-quality pseudo-labels. Through an extensive architectural design exploration and systematic optimization, we tested two fully convolutional networks: U-Net, offering higher segmentation accuracy, and a lightweight convolutional autoencoder (CAE) designed for lower latency inference. The models were deployed on an AMD/Xilinx Zynq UltraScale+ MPSoC using the deep learning processing unit (DPU) IP core for hardware acceleration. The FPGA-deployed U-Net achieved 98.16% with a false positive rate of 0.9%, providing robust segmentation even in challenging conditions, making it the preferred model for reliable inference onboard PSG. The CAE model maintained 97.02% accuracy while achieving over 2× faster inference (31.81 ms vs. 74.29 ms per image). Both models enable accurate cloud segmentation with real-time inference, meeting the operational constraints derived from the secondary camera design, while operating at an average power consumption of 2.6 W (U-Net) and 2.4 W (CAE), well within the mission power constraint for the HW accelerator. Our results validate the feasibility of integrating DL-based cloud coverage assessment onboard PSG, contributing to the broader effort of advancing artificial intelligence-powered computing for EO missions to enable more autonomous data processing and decision-making.

The development of passive microwave sensors traces back to Robert Dicke’s pioneering experiments in the 1940s. Since then, microwave radiometry has evolved into a key tool for Earth observation, strengthened by data from multiple satellite missions operating across different wavelengths. This paper reviews the state of the art in microwave radiometry for monitoring land surfaces. After introducing the theoretical foundations underpinning current missions, we present an overview of major satellite instruments. We then examine early theoretical advances in retrieving soil moisture and snow properties, two applications that contributed to the future development of satellite microwave radiometry missions for the observation of surface variables. Particular attention is given to radiative transfer theory and its solutions, which model the effects of roughness, vegetation, and snow cover. These approaches form the basis of today’s retrieval algorithms and remain central to future missions. Subsequent sections highlight the use of passive microwave data for estimating a variety of surface variables, the role of passive microwave in data assimilation systems and forthcoming missions dedicated to land monitoring. The review concludes with key achievements, ongoing challenges, and open issues—such as soil moisture retrieval under dense vegetation or snow property retrieval in melting conditions. Addressing these limitations is critical to fully exploiting microwave radiometry in the context of climate research and mitigation strategies.