The Italian Space Agency (ASI) under the "PLATiNO" multi-satellite constellation program have started the PLATiNO-3 and PLATiNO-4 payloads addressing Very High Resolution and Hyperspectral capabilities with small payloads under 100 kg, respectively. Proto-flight models of these instruments will be completed by 2024-2025. Under the same program, PLATiNO-1, a SAR mission in X-band and PLATiNO-2, a collaboration with NASA-JPL for the MAIA payload, will complete the constellation.In 2019 ASI launched the PRISMA instrument and from this baseline is developing a new "best-in-class" compact Hyperspectral payload (HYP-PL) that offers the same performance of its larger predecessor in a size and mass that can be adapted to multiple platforms in the small size range.In this article we present the main characteristics of the PLATiNO-4 payload.The HYP-PL is a <100Kg, single imaging spectrometer devoted to measure the spectral signature across the wavelength range 400-2500 nm by means of a diffraction grating as dispersing element. This spectral range matches many end-user requirements for environmental and commercial applications, such as water quality monitoring, oil spills, forest fire, soil protection, agriculture.The payload, after the successful development of breadboards and development models, is entering the D phase with the MAIT of the PFM.

Distributed SAR imaging from space exploits the distribution of the key system resources, normally concentrated in a single, large and complex satellite, among many small-sized and simpler sensors, thanks to the proper combination of the signals from each single node of the swarm. The simultaneous operation of the satellites represents a Multiple-Input-Multiple-Output SAR system (MIMO-SAR), for which several concepts have been presented in the scientific literature, [1]–[6].Indeed, the theoretical maturity of this technique is significant, however the practical feasibility has not yet been demonstrated. In 2020, the demonstrative Mission SATURN [7] was proposed and is currently being studied with the support of the Italian Space Agency (ASI) being part of ALCOR programme promoting the development of the next generation Italian CubeSats. The main target of the SATURN mission is to demonstrate the key technology "Cooperative MIMO Swarms of SAR MicroSats" for innovative, low cost and versatile Earth Observation applications.In this work, we present the architecture and the implementation of a miniaturized SAR payload, called MiniSAR, suitable for operation in a MIMO SAR system. The paper reports the design solutions, the architecture and the qualification of the SAR Electronics by means of functional, performance and environmental testing in thermal-vacuum chambers and vibration facilities.

In this study a novel architecture of Pulse Coupled Neural Network based on a multilevel topology with interconnected layers is presented. The model is applied to the solution of a segmentation problem of SAR images for the identification of man-made structures on urban landscapes. Thanks to the multilayer architecture, the unsupervised model can deal with dual polarization SAR data, as well as with combination of ascending and descending acquisitions. Such approach mitigates the issues in detecting artificial targets when their orientation with respect the satellite line of sight reduces the object backscattering with respect to the one of background. An example of application to two COSMO-SkyMed STRIPMAP data, acquired by ascending and descending orbits respectively is provided.The proposed approach, owing to their ability of efficiently processing voluminous datasets, can be effectively coupled with machine learning and deep learning to refine or validate their results.

This paper focuses on the timing analysis for a Distributed Synthetic Aperture Radar (DSAR) system exploiting an opportunity illuminator. This is the case of RODiO, which is a new mission concept funded by the Italian Space Agency (ASI) for a Phase A study in the framework of ALCOR program. RODiO’s aim is to match the growing trend towards the miniaturization of satellites and new Synthetic Aperture Radar (SAR) applications. For this reason, RODiO consists in a cluster of four receiving-only CubeSats flying in a close formation and exploiting the independent PLATiNO-1 satellite as a transmitter. Because of the nature of RODiO mission, simultaneous observations with respect to the monostatic illuminator are needed. Through the comparison of monostatic and bistatic chronograms, a timing analysis is performed in the paper in order to assess the effects of DSAR geometry on simultaneous observation opportunities, aiding the design of the system.

It is more than a decade that the Italian Space Agency (ASI) and the Japan Aerospace Exploration Agency (JAXA) are cooperating on the topic of disaster management, by sharing their expertise and satellite assets for the operational use of X-band and L-band SAR data. Over the years, mutual COSMO-SkyMed and ALOS-2 archives were created over Italy and Japan, ad hoc acquisitions were made in response to emergency requests, and data were exchanged to support joint SAR research activities related to Disaster Risk Management. The present paper provides the current status of the cooperation in light of the most recent emergencies, as well as the near future perspectives.

We investigated the use of phenological information extracted from satellite imagery in accurate crop classification. Vegetation indices (VI) extracted from Sentinel-2 imagery are capable to track the vegetation development through the year and from them the phenological profile can be retrieved and introduced into a multi-temporal automatic classification process to detect crop fields and to discriminate among different crop species. The matching and discrimination between phenology was evaluated by means of cross-correlation. Our case study is the Narok county located within the Great Rift Valley of Kenya.

Agricultural activities conducted in the Great Rift Valley of Kenya, show a significant decline of productivity levels. In this study, a remote and automatic agricultural monitoring system is presented as an effective alternative to the most traditional in situ measurements and observations. We investigated the use of phenological variables and metrics extracted from satellite in accurate crop classification and monitoring. Vegetation indices extracted from Landsat 8 imagery are capable to track the vegetation development through the year and from them the phenological profile can be retrieved and implemented into a multi-temporal automatic classification process to detect agricultural vegetated areas and to discriminate among different crop species. The phenological profiles extracted by satellite images were compared with crop calendar data, compiled by FAO for the area of interest.

Geostationary satellites like MSG allows to detect and monitor thermal anomalies (wild fires, volcanic eruption) with a refresh frequency ranging from 5 to 15 min. Such a frequency meets the requirements of the institutions involved in contrasting the fire events and could provide information on the temporal behavior of the fire (through Fire Radiative Power, FRP) and the spatial distribution of the events with the related hazard for the population and infrastructure when more occurrences are simultaneously present. A limitation to the operational applicability of this tool is represented by the present low spatial resolution of the MSG/SEVIRI sensor ranging from 3 km at the equator to 4.5 km at Mediterranean latitudes. Whereas the limitations related to the sensitivity of the geostationary sensor to fire sizes has been, at least in part, overcome by introducing specific algorithms, the reduced accuracy in the geographic localization of the fire, which can, in principle, occupy any position in an area of about 16 km2(at Mediterranean latitude) makes this information not very much interesting for the institutions involved in fire fighting. This paper is focused on the analysis of the feasibility of improving the localization of the thermal anomalies (hot spots) based on geostationary sensors by combining images acquired simultaneously from different MSG satellites located at different longitudes. In particular, we combine the images acquired by MSG-9 (RSS) located at long. 9.0° and MSG-8 (IODC) located at long. 40.5°. The results seem to confirm the possibility to improve the accuracy of the detection by exploiting the observation of the events from different position in the space.

Agricultural activities conducted in the Great Rift Valley of Kenya, show a significant decline of productivity levels. This phenomenon is mainly related to the limited water resources availability, the lack of supporting irrigation and the harvesting techniques ineffectiveness. The production risks reduction is closely related with a better use of water resources and a better understanding of the effects resulting from the multiple interactions between climate, agricultural vegetation, soil type and crops management techniques. In this study, a remote and automatic agricultural monitoring system is presented as an effective alternative to the most traditional in situ measurements and observations.

SBAM (Satellite Based Agricultural Monitoring) is a project funded by Italian Space Agency in the framework of Italian-Kenya cooperation. The project has four main objectives: a) to produce an updated map of the agricultural areas for Kenya based on Landsat 8 and Sentinel 2 imagery; b) to develop an automatic monitoring system able to classify agricultural areas and detect land use changes; c) to develop and deliver to the Kenyan partner of the project a system capable to download and process automatically Landsat8, Sentinel2, MODIS and MSG/SEVIRI images by providing standard products (vegetation indices, statistics, temporal analysis, etc.); d) to provide a tool for assessing changes in the agricultural area stability and crop yield. and study the feasibility of a tool capable to forecast crop yields. The paper is devoted to describe the activity carried out in the field of forecasting crop yield by using biomass estimate based on SAR images. The results obtained by using images acquired by X-band (Cosmo-Skymed), C-Band (Sentinel-1) and L-band (PALSAR) systems on a study area devoted to sugarcane will be described.

We investigated the use of phenological information extracted from satellite imagery and supported by agro-ecological zoning (AEZ) in accurate crop classification and monitoring. Vegetation indices extracted from Landsat 8 imagery are capable to track the vegetation development through the year and from them the phenological profile can be extrapolated and implemented into a multi-temporal automatic classification process to detect agricultural vegetated areas and to discriminate among different crop species. Our case study is the Nakuru district located within the Great Rift Valley of Kenya.

The territory knowledge plays a key role in the proper management and planning of many human activities. The relevance of land monitoring and mapping, that finally leads to quantify changes in land cover, is widely recognized as a key element in the study of global changes. Vegetation indices derived from satellite imagery are well correlated with those parameters that defines the crop yield's status; as a consequence remote sensed earth observation data are really notable for monitoring cultivated areas and crop yields and to provide information concerning food security and famine early warning.