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 European Space Agency's 7th Earth Explorer mission, BIOMASS, was proposed in 2005 and since then there have been major changes in the scientific and political conditions within which it was conceived, and also within the technology and methodology both of the mission itself and of the complementary systems that BIOMASS will work with. This paper describes some of the most important recent developments in this overall environment of the mission, and how they affect the likely use of data from BIOMASS mission after its launch in 2023 and over its nominal five-year lifetime.

Climate change may result in reduced crop yields and, due to extreme weather events, increase seasonal variations in production. Farmers are under further pressure to manage croplands with more sustainable and cost-efficient practices whilst maintaining high levels of production. Although plant breeding has made good progress in raising genetic yield potential under favourable growing conditions, there is much concern that yields on-farms have either stagnated, or become highly variable between seasons.

The Earth Explorer Biomass mission will provide the scientific community with accurate maps of tropical, temperate and boreal forest biomass, including height and disturbance patterns. This information is urgently needed to improve our understanding of the global carbon cycle and to reduce uncertainties in the calculation of carbon stocks and fluxes associated to the terrestrial biosphere. It is also crucial for approaches to managing climate, such as the UNFCCC initiative known as Reducing Emissions through Degradation and Deforestation (REDD+), aimed at climate change mitigation through conservation and better management of tropical forests The required measurements are forest biomass and forest height at resolution of 200 m, and detection of deforestation at 50 m. Global maps of biomass are required with accuracy of 20% (or l0 t ha-1when above-ground biomass are less than 50 t ha-1). To achieve this Biomass will be implemented as a P-band SAR mission. It will exploit the unique sensitivity of P-band SAR together with advanced retrieval methods including polarimetric interferometry (Pol-InSAR) and SAR tomography to measure biomass, height and disturbances across the entire biomass range every 6 months. The mission will also support important secondary objectives, including sub-surface imaging in arid zones, production of a bare-earth DTM and ice applications.

In this study, vegetation optical depth from ESA's Soil Moisture and Ocean Salinity (SMOS) satellite mission is combined with other existing remote sensing, meteorological and literature data in order to obtain values of gravimetric vegetation water content (Mg). The methodology combines an effective medium model valid at passive microwave frequencies with a vegetation dielectric constant model. The algorithm is calibrated for 11 global vegetation classes. The resulting product consists of temporally dynamic ~25 km global grids of Mg. The first maps clearly show seasonal differences in vegetation water, which vary for the different continental regions due to variations in e.g. latitude, climate and landcover type. This new vegetation water product is unique and offers important complementary information to existing vegetation indices.

The seasonal cycle of tree leaf display in the savannas and woodlands of the seasonally dry tropics is complex, and robust observations are required to illuminate the processes at play. Here, we evaluate three types of data for this purpose, comparing scatterometry (QuikSCAT σ0) and optical/near-infrared MODIS EVI remotely sensed data against field observations. At a site in Mozambique, the seasonal cycles from both space-borne sensors are in close agreement with each other and with estimates of tree plant area index derived from hemispherical photography . This agreement results in similar estimates of the start of the growing season across different data types (range 13 days). Ku-band scatterometry may therefore be a useful complement to vegetation indices such as EVI for estimating the start of the growing season for trees in tropical woodlands. More broadly, across southern tropical Africa there is close agreement between scatterometry and EVI time series in woody ecosystems with 25 % tree cover, but in areas of 25 % tree cover, the two time series diverge and produce markedly different start of season (SoS) dates (difference 50 days). This is due to increases in σ0during the dry season, not matched by increase in EVI. The reasons for these increases are not obvious, but might relate to soil moisture, flowering, fruiting, or grass dynamics. Further observations and modeling of this phenomenon is warranted to understand the causes of these dry season changes in σ0. Finally, three different definitions of the SoS were examined and found to produce only small differences in estimated dates, across all types of data.

This study investigates the temporal behavior of the gravimetric vegetation water content (Mg) derived from SMOS (L-band) optical depth (τ) values. The analysis is done for the year 2010 over a coniferous forest site in the U.S. Resulting values of Mgare compared to values of leaf water potential obtained with the Soil-Plant-Atmosphere model and in situ data. A significant nonlinear correlation is found between the two (R=0.72, pg, based on plant water status, is different from that of existing optical-based vegetation indices, which are mainly based on chlorophyll/biomass changes. Therefore, τ-derived Mgcould provide complementary information for future global-scale dynamic monitoring of vegetation water status.

The BIOMASS mission is designed to provide unique information on the biomass in the world's forests at spatial and temporal resolutions suitable for characterizing their dynamics and their contribution to carbon cycle estimates. To achieve this it combines biomass estimates from direct inversion of polarimetric backscattering coefficients with Pol-InSAR forest height estimates. The mission will also support important secondary objectives, including sub-surface imaging in arid zones, production of a bare-earth DTM and ice applications, and is optimized to be robust against environmental and ionospheric disturbances.

The European Space Agency (ESA) released a Call for Proposals for the next Earth Explorer Core Mission in March 2005, with the aim to select the 7thEarth Explorer (EE-7) mission for launch in the next decade. Twenty-four proposals were received and subject to scientific and technical assessment. Six candidate missions were selected and further investigated in the preliminary feasibility studies (Phase 0). One of these missions is BIOMASS, which has recently been selected to proceed to Phase-A. BIOMASS is a response to the urgent need for greatly improved mapping of global biomass and the lack of any current space systems capable of addressing this need.

This paper explores the potential to improve spatial estimates of key carbon fluxes by combining Earth Observation data with a simple ecosystem model. Spatial estimates of Leaf Area Index from MODIS at the kilometre scale over a coniferous forest site in Oregon are assimilated into an ecosystem model with an Ensemble Kalman filter. Results show that assimilating EO data improves the magnitude of estimates of Net Ecosystem Productivity relative to running the model alone, however the uncertainty is not significantly constrained. Spatially there is an underestimate in modelled carbon fluxes. This is attributed to error in the EO data which induces an underestimate in model stock estimates, as well as inadequacies in the model parameterisation.

This paper details recent progress in the assimilation of top of canopy reflectance data into an ecosystem model to constrain estimates of net carbon flux. Previous work has demonstrated the feasibility of such an approach but its application at wide spatial scales remains relatively un explored. Two aspects of this are examined in this paper: the requirement to provide spatialised reflectance model parameters and the restating of an ecosystem model to optimise it for assimilating EO data.

In this study we investigated the relationships between landscape features such as terrain elevation and slope with two variables that drives forest productivity, soil texture and leaf area index (LAI). The study was carried out at the Tapajos region in Para State, eastern Amazonia. Twenty-four 0.25 ha plots were sampled along a ~150 km north-south transect in October 2002. Soil samples were collected (0-10 cm) in three random points in each plot for texture analysis. LAI was measured at 25 points regularly distributed in each plot. The geomorphologic attributes for each plot were extracted from the Shuttle Radar Topography Mission (SRTM) data linearly resample to 10 m spatial resolution. Terrain slope was linear and negatively related to the soil clay content (r2=0.73). Soil sand content had an expected opposite pattern (r2=0.72). The soil content of clay and sand along the elevation gradient can be strongly explained by a cubic polynomial curve (r2=0.82 and 0.81, respectively). LAI showed to be a logarithmic function of slope (r2=0.61), excluding plots located in the Valley regions. Moreover, LAI showed a linear and positive relationship with soil clay content (r2=0.52). Similarly to the relationships found between terrain elevation and soil texture, the 3rd order polynomial could explain 64% of the LAI variability over the Tapajos. Therefore, we concluded that topography is a major driver of the patterns of soil texture at the landscape scale and the combined effect of topography and soil can largely explain the patterns of LAI over the Tapajos. The combination of SRTM data and field-based information has the potential to increase the accuracy of ecosystem scale estimations of forest productivity in the Amazonia.

This paper reports on the validation of the Collection 4 MODIS leaf area index (LAI) product over the Tapajo/spl acute/s region, eastern Amazonia. The validation site is enclosed in tile h12v09 of the MODIS LAI product. The methodology to assess MODIS LAI accuracy included two main steps: (1) a multiple regression analysis for the generation of LAI surfaces, based on the relationships between field data and remote sensing information from the Enhanced Thematic Mapper Plus sensor, and between field data and topographic information from a digital elevation model; (2) the direct comparison of these LAI surfaces with the MODIS LAI surfaces. The analysis indicated that MODIS LAI is significantly overestimated for the Tapajo/spl acute/s region by a factor of 1.18. No relationships between MODIS LAI and the validation surfaces were found. These results are indicative of a predominance of LAI retrievals by the backup algorithm, which is overcompensating LAI values at the saturation domain. The overgeneralization of the land cover layer (MOD12Q1) can be a source of uncertainties for the lookup table parameterization. Further validation efforts must be carried out over Amazonia for a quantitative quality assessment of the MODIS LAI surfaces in order to improve its accuracy.

The general aim of this study was to produce a continuous field of LAI to evaluate the LAI surface (MODIS product) derived from the moderate resolution imaging spectroradiometer (MODIS), for the Tapajos region, eastern Amazonia. Our method consisted in generating regression models combining spectral data derived from Enhanced Thematic Mapper Plus (ETM+) sensor (07/30/2001) and terrain slope and altimetry information extracted from a digital terrain model. The spectral variables considered for this study are reflectance, vegetation indices (NDVI and SR) and fraction images. Using a multiple comparison test, we compared the mean LAI estimated by the three models generated in this study (270 m spatial resolution) with the 8 days LAI composition (08/13/2001) derived from MODIS sensor (1 km spatial resolution) and also with field data. The MODIS LAI surface for the Tapajos region showed a more homogenous surface and little information about land cover and land use when we visually compared with our estimations. This fact occurs due to the 1 km resolution from de MODIS LAI against the 270 m resolution used in our approach. The statistics indicated that the mean LAI derived from MODIS sensor is significantly overestimated (P<0.05) in relation to both field and modeled data. We conclude that the approach employed here is promising for generating LAI surfaces, based on field data, for MODIS LAI validation purposes