The European Space Agency's (ESA) BIOMASS mission is a pioneering Earth observation satellite mission launched on April 29, 2025. Utilizing a P-band synthetic aperture radar (SAR), the objective of BIOMASS is to deliver estimates of above-ground forest biomass, forest height (FH), and forest disturbance (FD), with unprecedented accuracy. The mission's primary scientific goal is to quantify the distribution and changes in forest biomass, thereby reducing uncertainties in carbon flux estimates and informing climate models. The satellite's advanced instrumentation and innovative approach allow it to penetrate dense forest canopies, capturing data even in challenging environments. The mission will operate in two distinct phases: the tomographic phase and the interferometric phase, which will support polarimetric interferometric SAR (Pol-InSAR) and tomographic SAR (TomoSAR) processing. Additionally, BIOMASS will provide valuable observational data for ice sheets, deserts, the ionosphere, below canopy topography, and other domains.

The Biomass mission is the 7th Earth Explorer Mission within the frame of the ESA Earth Observation Programme. The primary objective of Biomass is to determine the worldwide distribution of forest above-ground biomass in order to reduce the major uncertainties in calculations of carbon stocks and fluxes associated with the terrestrial biosphere. To meet these objectives Biomass will carry, for the first time in space, a fully polarimetric P-band synthetic aperture radar (SAR) supporting also interferometric acquisitions. This paper provides an overview of the Biomass system design and development status.

Meteosat Third Generation (MTG) is the next generation of European meteorological geostationary satellites, set to be launched in 2021. Besides ensuring continuity with Meteosat Second Generation imagery mission, the new series will feature new instruments, such as the Lightning Imager (LI), a high-speed optical detector providing near real-time lightning detection capabilities over Europe and Africa. The instrument will register events on pixels, where a lightning pulse generates a transient in the acquired radiance. In parallel, signal variations due to a number of unwanted sources, e.g., acquisition noise or jitter movement, are expected to produce false events. The challenge for on-board and on-ground processing is, thus, to discard as many false events as possible while keeping a majority of the true lightning events. This paper discusses a chain of algorithms that can be used by the LI for the detection of lightning and for the filtering of false events. Some of these algorithms have been developed in the framework of internal research and simulations conducted by the MTG team at the European Space Agency on an in-house LI simulator and therefore will not necessarily reflect the ultimate operational processing chain. The application of the chain on a representative scenario shows that 99.5% of the false events can be eliminated while keeping 83.6% of the true events, before generating the LI higher level data products. Machine learning techniques have also been studied as an alternative for on-ground event processing, and preliminary results indicate promising potential.

Reflectometry using Global Navigation Satellite System's signals of opportunity (GNSS-R) was originally conceived for mesoscale altimetry [1], although its applicability to sea state determination, soil moisture, vegetation, snow monitoring... has already been demonstrated. In December 2012 the Phase A studies of ESA's PAssive Reflectometry and Interferometry System In-orbit Demonstration (PARIS IoD) mission ended. In conventional GNSS-R the GNSS signals scattered over the Earth's surface are cross-correlated with a locally generated replica of the transmitted signal shifted in frequency (Δfd) and in delay (Δτ). PARIS is called an interferometric GNSS-R (iGNSS-R) system because the direct and the scattered signals are cross-correlated in order to use the whole signal's bandwidth, and improve the altimetric precision, despite the large bandwidth signals are not publicly available. This work presents a methodology to evaluate the performance of iGNSS-R altimeters. It is then applied to a PARIS IoD-like case, in which the receivers' bandwidths have been optimized in terms of altimetric resolution.