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 scientific community is faced with a need for greatly improved data sharing, analysis, visualization and advanced collaboration based firmly on open science principles. Recent and upcoming launches of new satellite missions with more complex and voluminous data, as well as the ever more urgent need to better understand the global carbon budget and related ecological processes provided the immediate rationale for the ESA-NASA Multi-mission Algorithm and Analysis Platform (MAAP).This highly collaborative joint project of ESA and NASA established a framework between ESA and NASA to share data, science algorithms and compute resources in order to foster and accelerate scientific research conducted by ESA and NASA EO data users. Presented to the public in October 2021 [1], the current version of MAAP provides a common cloud-based platform with computing capabilities co-located with the data, a collaborative coding and analysis environment, and a set of interoperable tools and algorithms developed to support, for example, the estimation and visualization of global above-ground biomass.Data from the Global Ecosystem Dynamics Investigation (GEDI) mission on the International Space Station [2] and the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) [3] have been instrumental pioneer products on MAAP, generating the first comprehensive map of Boreal above-ground biomass [4] and supporting the CEOS Biomass Harmonization Activity [5]. Crucially, the platform is being specifically designed to support the forthcoming ESA Biomass mission [6] and incorporate data from the upcoming NASA-ISRO SAR (NISAR) mission [7]. While these missions and the corresponding research leading up to launch, which includes airborne, field, and calibration/validation data collection and analyses, provide a wealth of information relating to global biomass, they also present data storing, processing and sharing challenges; the NISAR mission alone will produce around 40 petabytes of data per year, presenting a challenge that, without MAAP, would impose several accessibility limits on the scientific community and impact scientific progress.Other challenges being addressed by MAAP include: 1) Enabling researchers to easily discover, process, visualize and analyze large volumes of data from both agencies; 2) Providing a wide variety of data in the same coordinate reference frame to enable comparison, analysis, data evaluation, and data generation; 3) Providing a version-controlled science algorithm development environment that supports tools, co-located data and processing resources; 4) Addressing intellectual property and sharing challenges related to collaborative algorithm development and sharing of data and algorithms. REFERENCES[1] https://www.nasa.gov/feature/nasa-esa-partnership-releases-platform-for-open-source-science-in-the-cloud[2] https://science.nasa.gov/missions/gedi[3] https://icesat-2.gsfc.nasa.gov/[4] https://daac.ornl.gov/ABOVE/guides/Boreal_AGB_Density_ICESat2.html [5] https://iopscience.iop.org/article/10.1088/1748-9326/ad0b60[6] T. Le Toan, S. Quegan, M. Davidson, H. Balzter, P. Paillou, K. Papathanassiou, S. Plummer, F. Rocca, S. Saatchi, H. Shugart and L. Ulander, “The BIOMASS Mission: Mapping global forest biomass to better understand the terrestrial carbon cycle”, Remote Sensing of Environment, Vol. 115, No. 11, pp. 2850-2860, June 2011.[7] P.A. Rosen, S. Hensley, S. Shaffer, L. Veilleux, M. Chakraborty, T. Misra, R. Bhan, V. Raju Sagi and R. Satish, "The NASA-ISRO SAR mission - An international space partnership for science and societal benefit", IEEE Radar Conference (RadarCon), pp. 1610-1613, 10-15 May 2015.

Selected as European Space Agency’s seventh Earth Explorer in May 2013, the BIOMASS mission will provide crucial information about the state of our forests and how they are changing. This mission is being designed to provide, for the first time from space, P-band Synthetic Aperture Radar measurements to determine the amount of biomass and carbon stored in forests. The data will be used to further our knowledge of the role forests play in the carbon cycle.In this context of an innovative sensor, the concept of Mission Algorithm and Analysis Platform dedicated to the BIOMASS, to the NASA-ISRO SAR (NISAR) mission and to the NASA Global Ecosystem Dynamics Investigation (GEDI) mission mission is proposed. Developed in a collaborative way between ESA and NASA, this Mission Algorithm and Analysis Platform will implement, as part of the payload data ground segment, a virtual open and collaborative environment. The goal is to bring together data centre (Earth Observation and non- Earth Observation data), computing resources and hosted processing, collaborative tools (processing tools, data mining tools, user tools, …), concurrent design and test bench functions, accounting tools to manage resource utilisation, communication tools (social network) and documentation. This platform will give the opportunity, for the first time, to manage the community of users of the BIOMASS mission thanks to this innovative concept.To best ensure that users can collaborate across the platform and to access needed resources, the MAAP requires all data, algorithms, and software to conform to open access and open-source policies. As an example of best collaborative and open-source practices, most of the BIOMASS Processing Suite (BPS) will be made openly available within the MAAP. This Processing Suite contains all elements to generate the BIOMASS upper-level data products and is currently in development under the umbrella of the open-source project called BioPAL. BioPAL is developed in a coherent manner, putting a modular architecture and reproducible software design in place. BioPAL aims to factorize the development and testing of common elements across different BIOMASS processors. The architecture of this scientific software makes lower-level bricks and functionalities available through a well-documented Application Programming Interface (API) to foster the reuse and continuous development of processing algorithms from the BIOMASS user community. This API will greatly simplify the use of the BIOMASS Processing Suite (BPS) on the MAAP.In addition to open satellite data and open-source algorithms, open reference data is needed for Calibration and Validation. GEOTREES is composed of Biomass Reference Measurement sites that are in situ forest measurement sites with a common standard for high-quality data acquisition, transparent measurement protocols, long-term monitoring, and measurements traceable to SI units. GEO-TREES will be established through collaboration with existing international networks of high-quality forest plots that use standard forest monitoring protocols.

Across the broad potential user base for Earth Observation (EO) data, confidence in the quality of the available products is vital, particularly for users requiring quantitative measured outputs they can rely on. Particularly as the commercial EO sector rapidly expands, however, it is an increasing challenge for the user community to discern between the wide variety of product offerings in a reliable manner, especially in terms of product quality. In response to this ESA and NASA, through their Joint Program Planning Group (JPPG) Subgroup, have developed a common EO product Quality Assurance (QA) Framework to provide comprehensive assessments of product quality. The evaluation is primarily aimed at verifying that the data has achieved its claimed performance levels, and, reviews the extent to which the products have been prepared following community best practice in a manner that is “fit for purpose”. A Cal/Val maturity matrix provides a high-level colour-coded a simple summary of the quality assessment results for users. The matrix contains a column for each section of analysis (e.g., metrology), and cells for each subsection of analysis (e.g., sensor calibration). Subsection grades are indicated by the colour of the respective grid cell, which are defined in the key. Both ESA and NASA have on-going activities supporting the procurement of commercial EO data that make use of the joint QA Framework – to ensure decisions on data acquisition are made with confidence. On the ESA side, the Earthnet Data Assessment Project (EDAP) project performs data assessments on EO missions in optical, atmospheric and SAR domains. Similarly, the NASA Earth Science Division (ESD) Commercial Smallsat Data Acquisition (CSDA) Program, completed a pilot study in 2020, and has since entered sustainment use phase for some of the commercial data sets. In this presentation the joint ESA/NASA QA Framework is described, with some examples of its application to commercial EO products.

In recent decades, an important number of regional and global digital elevation models (DEMs) have been released publicly. As a consequence, researchers need to choose between several of these models to perform their studies and to use these DEMs as third-party data to compute derived products (e.g., for orthorectification). However, the comparison of DEMs is not trivial. For most quantitative comparisons, DEMs need to be expressed in the same coordinate reference system (CRS) and sampled over the same grid (i.e., be at the same ground sampling distance with the same pixel-is-area or pixel-is-point convention) with heights relative to the same vertical reference system (VRS). Thankfully, many open tools allow us to perform these transformations precisely and easily. Despite these rigorous transformations, local or global planimetric displacements may still be observed from one DEM to another. These displacements or disparities may lead to significant biases in comparisons of DEM elevations or derived products such as slope, aspect, or curvature. Therefore, before any comparison, the control of DEM planimetric accuracy is certainly a very important task to perform. This paper presents the disparity analysis method enhanced to achieve a sub-pixel accuracy by interpolating the linear regression coefficients computed within an exploration window. This new method is significantly faster than oversampling the input data because it uses the correlation coefficients that have already been computed in the disparity analysis. To demonstrate the robustness of this algorithm, artificial displacements have been introduced through bicubic interpolation in an 11 × 11 grid with a 0.1-pixel step in both directionsThis validation method has been applied in four approximately 10 km × 10 km DEMIX tiles showing different roughness (height distribution). Globally, this new sub-pixel accuracy method is robust. Artificial displacements have been retrieved with typical errors (eb) ranging from 12 to 20% of the pixel size (with the worst case in Croatia). These errors in displacement retrievals are not equally distributed in the 11 × 11 grid, and the overall error Eb depends on the roughness encountered in the different tiles. The second aim of this paper is to assess the impact of the bicubic parameter (slope of the weight function at a distance d = 1 of the interpolated point) on the accuracy of the displacement retrieval. By considering Eb as a quality indicator, tests have been performed in the four DEMIX tiles, making the bicubic parameter vary between −1.5 and 0.0 by a step of 0.1. For each DEMIX tile, the best bicubic (BBC) parameter b* is interpolated from the four Eb minimal values. This BBC parameter b* is low for flat areas (around −0.95) and higher in mountainous areas (around −0.75). The roughness indicator is the standard deviation of the slope norms computed from all the pixels of a tile. A logarithmic regression analysis performed between the roughness indicator and the BBC parameter b* computed in 67 DEMIX tiles shows a high correlation (r = 0.717). The logarithmic regression formula b~σslope estimating the BBC parameter from the roughness indicator is generic and may be applied to estimate the displacements between two different DEMs. This formula may also be used to set up a future Adaptative Best BiCubic (ABBC) that will estimate the local roughness in a sliding window to compute a local BBC b~.

In this study we present some preliminary achievements on our proposed deep learning framework specifically designed to perform Sentinel-1 polarimetric information reconstruction. In particular, we aim at demonstrating our deep learning framework ability to generate missing polarization information from single-polarization datasets, with a view to supporting the final generation of useful quicklooks also starting from single-polarization Sentinel-1 data. After a robust validation, the deep learning framework may prove effective in both qualitative and quantitative assessments based on reconstructed quicklooks, notably for the Sentinel-1 Wave (WV) Mode acquisitions, generally acquired and delivered at a single polarization mode, thus with no default colorization scheme for quicklooks.

The paper provides an overview of the activities performed in the framework of the ESA Earthnet Data Assessment Project. The project focuses on the quality and suitability assessments of candidate missions being considered for the Earthnet Third Party Missions. The paper focuses on SAR missions’ assessment presenting the monitored quality parameters and an overview of the missions assessed so far. The assessment of ICEYE mission is reported here as an example of the performed activities. Finally, an open-source tool for the quality assessment of SAR data is presented.

FoPen (Foliage Penetration) SAR systems, especially those operating at low frequencies, are effective for detecting man-made objects in dense forests. This study leverages a single SAR image to distinguish between the strong volume scattering from foliage and the signals of concealed man-made objects, which are typically non-isotropic along the view angle, unlike the azimuthally invariant forest or vegetation. We propose a two-step strategy: firstly, a sliding sublook analysis maintains the azimuthal bandwidth maximally wide for each sublook, producing SAR images with reduced azimuthal resolution but varied central along-track components of the wave-vector, associated with the target’s ’aspect angle’. Secondly, a homogeneity test—specifically, the coefficient of variation—analyzes the aspect angle dimension of the SAR stack. Preliminary results using P-band datasets from the TropiSAR campaign in Paracou demonstrate the effectiveness of our approach in detecting concealed vehicles and infrastructures.

This paper presents the efforts of the European Space Agency (ESA) to define a harmonised family of SAR Analysis Ready Data (ARD) products for the Sentinel-1, ERS-1/2, ENVISAT, ROSE-L and BIOMASS missions. This family of new SAR products is specifically aimed at users who are interestet in exploring the potential of SAR but may lack the expertise of facilities for SAR processing. It will allow immediate analysis with minimum additional user effort, and interoperability between sets of past and future ESA missions.

The requirement of automated Land Use/Land Cover (LULC) classification has arisen in ecosystem related applications, such as natural hazard assessments, urban and rural area planning, natural resource management, etc. The data source and the classification method used in the production of LULC maps depend on the study area size and the location, and also determined by taking the time and cost into account. Recently, MAXAR Technologies announced a new product, High Definition (HD) with 15 cm resolution, which is obtained by post-processing of images with 30 cm Ground Sampling Distance (GSD). The post-processing employs machine learning methods. On the other side, the effect of HD processing on the image quality, and the usability of such products in various applications are still needed to be investigated. In this study, the influence of HD processing algorithm on LULC classification results was investigated by using 15 cm HD and 30 cm resolution images provided by MAXAR. By using the Random Forest (RF) and Support Vector Machine (SVM) methods in two different study areas, image classification was performed to detect water, vegetation, asphalt road, building, shadow, agriculture and barren land classes. The results show that in HD products, the edges of objects were sharper, whereas the classification noise was higher inside agricultural fields. Considering the overall results, it can be concluded that with the use of HD products in urban areas, improved LULC maps can be obtained.

Earth Observation (EO) data characterised by a spatial resolution in the range of 10-30 m (e.g., Sentinel 1 – S1, Sentinel 2 – S2, Landsat series), systematically acquired and freely distributed by national and international space agencies/institutions (e.g., ESA, EU, NASA), are a valuable tool for analysing shoreline evolution trends. These data can be used for supporting coastal erosion hazard and risk management strategies (Cenci et al., 2018). However, the accuracy of such trends is not often quantified because of the difficulties in finding systematic and freely available EO data at Very High Resolution (VHR) concurrently acquired over the same target areas to use as reference. Within this context, this work was conceived for taking advantage of the Copernicus VHR optical datasets (spatial resolution: 2-4 m) to use as reference data to validate the shoreline evolution trends obtained by exploiting S1 and S2 images. The abovementioned analysis was carried out for a short-term scenario (i.e., 3 years: from 2015 to 2018) in an exemplifying littoral of the Mediterranean Sea characterised by both urbanised and natural coastal areas: i.e., Lido di Ostia (Rome, Italy). Importantly, the shoreline extraction method used in this case study was based on a methodological approach that allowed to map the shoreline positions with sub-pixel precision (Bishop-Taylor et al., 2019; Cenci et al., 2021). Preliminary results showed that the shoreline evolution trends based on the S2 Visible Near-InfraRed (VNIR) spectral bands (spatial resolution: 10 m) retain an accuracy of 4.5 m (in term of Root Mean Squared Error - RMSE), if compared against the corresponding trends acquired by using Copernicus VHR data with a spatial resolution of 2 m. At the conference, the results of the analysis based on S1 data will be also presented, as well as a thorough interpretation and discussion of the S1 and S2 -based results that take into account the characteristics of the coastal area under assessment (e.g., presence or absence of defence structures) and the relationship between the magnitude of the shoreline advance/retreat trends and the corresponding accuracy. The overall objective of this work is to show the potentialities of the Copernicus EO data for the management of the coastal erosion hazard/risk in the Mediterranean area. References: * Bishop-Taylor R., Sagar S., Lymburner L., Alam I. and Sixsmith J. Sub-Pixel Waterline Extraction: Characterising Accuracy and Sensitivity to Indices and Spectra. Remote Sensing. 2019; 11(24):2984. https://doi.org/10.3390/rs11242984 * Cenci L., Disperati L., Persichillo M.G., Oliveira E.R., Alves F.L. and Michael Phillips. Integrating remote sensing and GIS techniques for monitoring and modeling shoreline evolution to support coastal risk management. GIScience & Remote Sensing. 2018. 55(3), pp. 355-375.https://doi.org/10.1080/15481603.2017.1376370 * Cenci L., Pampanoni V., Laneve G., Santella C. and Boccia V. Evaluating the Potentialities of Copernicus Very High Resolution (VHR) Optical Datasets for Assessing the Shoreline Erosion Hazard in Microtidal Environments. AIT Series: Trends in earth observation. 2021. Volume 2, pp. 81-84. ISSN: 2612-7148. ISBN: 978-88-944687-0-0. Published on behalf of the Associazione Italiana di Telerilevamento (AIT) https://aitonline.org/wp-content/uploads/2021/10/PlanetCarefromSpace.pdf DOI: 10.978.88944687/00

Cal/Val activities within the Earthnet Data Assessment Pilot (EDAP) Project of the European Space Agency (ESA) cover several Earth Observation (EO) satellite sensors, including Third-Party Missions (TPMs). As part of the validation studies of very-high-resolution (VHR) sensor data, the geometric and radiometric quality of the images and the mission compliance of the SkySat satellites owned by Planet were evaluated in this study. The SkySat constellation provides optical images with a nominal spatial resolution of 50 cm, and has the capacity for multiple visits of any place on Earth each day. The evaluations performed over several test sites for the purpose of the EDAP Maturity Matrix generation show that the high resolution requirement is fulfilled with high geometric accuracy, although various systematic and random errors could be observed. The 2D and 3D information extracted from SkySat data conform to the quality expectations for the given resolution, although improvements to the vendor-provided rational polynomial coefficients (RPCs) are essential. The results show that the SkySat constellation is compliant with the specifications and the accuracy results are within the ranges claimed by the vendor. The signal-to-noise ratio assessments revealed that the quality is high, but variations occur between the different sensors.

In the TOPSAR acquisition mode, Sentinel-1 sensors acquire some data in passive mode, with a periodicity of approximately one second. These data provide valuable information about scene brightness and Radio Frequency pollution. This paper reviews the method to extract these passively sensed data and identify Radio Frequency Interferences (RFI). Two sets of measurements are generated by back-projecting passively sensed data on the ground, generating Earth Brightness Temperature (BT) and RFI equivalent temperature. A proper calibration has been implemented thanks to AMSR-2 data. The paper shows the result of one year of data collection from Sentinel 1 A and B regarding RFI and BT. We discuss the stability of the systems and the accuracy of the measurements by cross-comparing results from the two sensors, and we show how this information can be exploited for precise SAR data denoising and RFI mitigation.

This work was conceived to analyze the impact of the different instances of the Copernicus DEM (CopDEM) dataset (named: EEA-10, GLO-30, GLO-90) on the orthorectification of Very High Resolution (VHR) optical data (spatial resolution: 2–4 m). Indeed, the CopDEM instances are characterized by different pixel sizes/spatial coverages/licenses (EEA-10: 10&12 m/European/restricted; GLO-30: 30 m/global/public; GLO-90: 90 m/global/public). Findings showed that all the CopDEM instances provided valuable topographic information that allows reaching similar values of geolocation accuracy. The latter is only slightly influenced by the different pixel sizes of the data. Moreover, this research provided valuable insights related to the existing relationships between the geolocation accuracy and: i) the spatial resolution of the VHR input products; ii) the orography and land cover of the target areas. The analysis also highlighted the importance of the GLO-30 instance for orthorectification purposes, as it is freely and globally available.

BIOMASS will provide unprecedented information about forests, thanks to the first spaceborne P-band SAR sensor, full polarimetry, interferometry-tomography. The challenges posed by this new technology required extensive investigations, leading to the Level-2 prototype processor for the retrieval of forest information. Some open points remain before launch though, with limited data and tools to address them. BioPAL was started to help bridging these gaps, reflecting latest scientific updates from BIOMASS studies in the processor and making algorithms available to other potential contributors. In this paper we present BioPAL software library current status, main components and framework.

Radio Frequency Interferences (RFI) are affecting more and more spaceborne SAR missions due to the increasing number of ground (or even space) emitters transmitting in the frequency band allocated for Earth Observation. Operative L-band SAR missions such ALOS and SAOCOM already foresee RFI mitigation strategies at processing level. Many cases of RFI contamination have been observed by Sentinel-1 users as well. For this reason, the Sentinel-1 operational processor (IPF) was evolved with the capability of RFI detection and mitigation. This paper describes the strategy implemented in the Sentinel-1 IPF, including examples and statistics collected during the first months of operational RFI mitigation.

This work was conceived for assessing the potential of the Copernicus DEM (CopDEM) dataset to identify (flash) flood-prone areas by using a geomorphological approach. This dataset is distributed in different instances (named: EEA-10, GLO-30, GLO-90) characterized by different pixel sizes/spatial coverages/licenses (EEA-10: 10&12 m/European/restricted; GLO-30: 30 m/global/public; GLO- 90: 90 m/global/public). The analysis compared the performances of the different instances for the abovementioned application. An exemplifying Mediterranean catchment located in Italy was used as test area. Results showed that the CopDEM dataset was successfully capable of identifying (flash) flood-prone areas. The best results were obtained by the EEA-10 and GLO-30 instances. The analysis also highlighted the importance of the GLO-30 instance to map flood prone-areas, as it is freely and globally available. Moreover, the GLO-30 instance outperformed other freely available DEMs distributed with a pixel size of 30 m (i.e., ALOS AW3D30, ASTER GDEM, SRTM) that were analyzed for comparative purposes.

In the recent years, the Earth observation (EO) capacity from space has grown with the multiplication of both institutional and commercial missions; in particular the domain of high-resolution optical sensors and SAR (Synthetic Aperture Radar) has dramatically increased. The “NewSpace players” are considered in full in the evolution of the EO international strategy. In this context, ESA and NASA put in place several activities that aim at assessing the data coming from these new missions, like the ESA’ s Earthnet Data Assessment Pilot (EDAP) project and the NASA's Commercial Smallsat Data Acquisition (CSDA) Program. Given the initial success of both of these activities and the multiplication of new missions in the context of the NewSpace, ESA and NASA are proposing to extend these activities with a coordinated approach and the definition of joint guidelines for data quality assessment.