This study investigates ice features of unknown glaciological origin in the ablation zone of southwest Greenland, focusing on the land-terminating Russell Glacier. Using data from an experimental airborne SAR (Synthetic Aperture Radar) campaign of the German Aerospace Center (DLR), the research employs a range of advanced techniques, including SAR polarimetry, interferometry, tomography, and modeling, to characterize these features. The analysis reveals that in low-backscatter areas, surface scattering is dominant with no correlation to topography or surface characteristics. In contrast, surrounding high-backscatter areas are characterized by volume scattering and the presence of subsurface scattering structures. A significant aspect of this study involves comparing the observed ice features with known glaciological phenomena in the ablation zone. However, the combined findings, along with the temporal stability of these features, as seen through annual SAR backscatter analysis, complicate a straightforward glaciological explanation. A first theory involves the presence of a weathering crust causing these low-backscatter features due to residual liquid water content. These findings could improve our understanding of surface and subsurface processes in the ablation zone, contributing to better mass balance assessments.

The main goal of the TerraSAR-X Add-On for Digital Elevation Measurements (TanDEM-X) mission is the generation of a global digital elevation model (DEM) of unprecedented accuracy and coverage. The global TanDEM-X DEM product became available in 2016, surpassed all expectations, and became a reference for a wide range of Earth science, commercial, and geospatial applications. In addition, new information products, such as DEM change maps (DCMs), have been developed and are available to the geoscience and remote sensing community. Beyond the operational products, new science applications have been demonstrated and are summarized in this article, along with experimental data acquisitions. This article also aims to provide an overview of science activities with TanDEM-X data and science data acquisitions planned for the coming years.

NASA’s Global Ecosystem Dynamic Investigation (GEDI) mission provides billions of lidar samples of canopy structure over the Earth’s temperate and pantropical forests. Using the GEDI sample data alone, gridded height and biomass products have been created at a spatial resolution of 1 km or coarser. However, this resolution may be too coarse for some applications. In this study, we present a new method of mapping high spatial resolution forest height across large areas using fusion of data acquired by GEDI and TanDEM-X (TDX) Interferometric Synthetic Aperture Radar (InSAR). Our method utilizes GEDI waveforms to provide vertical profiles of scatterers needed to invert a physically-based InSAR model to solve for canopy height. We then use 2-year GEDI canopy height and dynamic wavenumber (kZ)-based calibration models to reduce errors in the inverted canopy height caused by the limited penetration capability of the X-band signal in dense tropical forests and the impact of terrain. We apply this dynamic method over large areas including Gabon, Mexico, French Guiana and most of the Amazon basin, and generate continuous forest height products at 25m and 100 m. After validating against airborne lidar data, we find that our canopy height products have a bias of 0.31 m and 0.46 m, and a root mean square error (RMSE) of 8.48 m (30.02%) and 6.91 m (24.08%) at 25 m and 100 m respectively, for all sites combined. Compared to existing data products that integrate GEDI with passive optical data using machine learning approaches, our method reduces bias, has a lower RMSE, and does not saturate for tall canopy heights up to 56 m. A key feature of this study is that our canopy height product is complemented with an uncertainty of prediction map which provides information on the predictor’s uncertainty around the actual value —an advancement over the standard error maps used in earlier studies, which provide uncertainty around the expectation of the predicted value. This integration approach enables the first-ever accurate and high-resolution mapping of forest canopy heights at unprecedented large areas using GEDI and TDX InSAR data, serving as an essential foundation for pantropical aboveground biomass mapping.

Recent advances in Foundation Models (FMs) are transforming AI, enabling remarkable generalization and zero-shot learning capabilities. The Helmholtz Foundation Model Initiative is developing the 3D Above and Below Ground Carbon Stocks foundation model (3D-ABC FM), a tool targeting the accurate mapping and quantification of terrestrial above- and below-ground carbon stocks in soils and vegetation at high spatial resolution. Understanding the global carbon budget with its carbon sources and sinks is crucial for guiding emission pathways and climate policies. The 3D-ABC FM aims to provide a seamless understanding of vegetation and soil carbon distribution by integrating multimodal datasets and addressing complex challenges such as multi-dimensionality and multi-resolution in FMs. 3D-ABC FM offers a transformative approach monitoring terrestrial carbon and advancing climate science.

Tropical forests are of great ecological and climatological importance. Although they only cover about 6% of Earth’s surface, they are home to approx. 50% of the world’s animal and plant species. Their trees store 50% more carbon than trees outside the tropics. At the same time, they are one of the most endangered ecosystems on Earth: about 6 million of hectares per year are felled for timber or cleared for farming. Compared to the other components of the carbon cycle (i.e. the ocean as a sink and the burning of fossil fuels as a source), the uncertainties in the local land carbon stocks and the carbon fluxes are particularly large. This is especially true for tropical forests: more than 98% of the carbon flux generated by changes in land-use may be due to tropical deforestation, which converts carbon stored as biomass into emissions. In this context, the AfriSAR 2015/16 campaign, supported by ESA, was carried out over four forest sites in Gabon by ONERA (July 2015) during the dry season and by DLR (February 2016) during the wet season. From the data collected the innovative techniques applied to estimate forest height and biomass could be improved significantly and are summarized in a special issue ‘Forest Structure Estimation in Remote Sensing’ of IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. The motivation of the AfriSAR campaign was to acquire demonstration data for the soon to be launched ESA BIOMASS mission, that was selected as the 7th Earth Explorer mission in May 2013 in order to meet the pressing need for information on tropical carbon sinks and sources by providing estimates of forest height and biomass. AfriSAR focused on African tropical and savannah forest types (with biomass in the 100-300 t/ha range) and complements previous ESA campaigns over Indonesian and Amazonian forest types in 2004 (INDREX-II) and 2009 (TropiSAR). The present contribution concerns the GABONX campaign, the ESA supported successor to AfriSAR, which took place in May to July 2023. GABONX aims to detect and quantify changes that have occurred since the DLR acquisitions in February 2016. To this end, DLR’s F-SAR sensor acquired interferometric stacks of fully polarimetric L- and P-Band data over the same forest sites in the same flight geometry as in 2016. The results presented give an overview of campaign activities with particular emphasis on the calibration of the SAR instrument as well as the validation of forest parameters derived from polarimetric interferometry. The SAR sensor calibration is based on an innovative approach that leverages state-of-the-art EM simulation to accurately characterize the 5m trihedral reference target deployed for the campaign in Gabon. The validation of derived forest parameters uses lidar measurements obtained in the time frame of the GABONX campaign by NASA’s LVIS sensor. As an outlook, further collaborative calibration and validation activities will hopefully include the cross-calibration of DLR’s F-SAR and NASA’s UAVSAR, which is set to acquire L- and P-Band data over the GABONX sites in 2024.

Combining spaceborne waveform lidar and InSAR data has been attractive to provide sub-hectare forest height after launch of GEDI mission. In this framework, this letter focuses on the improvement of forest height mapping in the combination of TanDEM-X (bistatic) interferometric coherence and GEDI waveform scheme by addressing variations in the reflectivity profile through geometric configurations. The enhanced height estimation performance can be successfully achieved by segmentation of TanDEM-X data by means of acquisition orbit. The analysis was carried out by processing 364 TanDEM-X scenes and about 15 million GEDI waveforms acquired in Tasmania forested island.

This work focuses on the detection and interpretation of forest structural changes by exploiting jointly polarimetric and interferometric SAR (Pol-InSAR) acquisitions. Using Pol-InSAR measurements and following a two-layer model assumption, the response from the forest canopy can be first decomposed into a ground and a volume layer. Then, a polarimetric change analysis can be applied over the separated ground and volume scattering components acquired at different times. The analysis of the detection and interpretation of the type of forest (structural) changes is carried out by exploiting L- and P-band data acquired by DLR’s F-SAR sensor over the Traunstein forest in the context of the TMPSAR campaign. The results corroborate that the decomposition of the forest into simpler layers eases the interpretation of the changes.

Model-based forest height inversion from single- and multi-baseline polarimetric synthetic aperture radar interferometry (Pol-InSAR) data is today well-established. One of the critical performance points is the parameterization of the vertical reflectivity profile. In this article, the parameterization of the vertical reflectivity profile using a tomographic reconstruction is proposed. To mitigate the limitations in the tomographic reconstruction induced by the limited vertical resolution especially for the ground-scattering component, the ground and volume contributions are separated using the sum of Kronecker products (SKPs) decomposition. For the forest height inversion, the Pol-InSAR line inclination angle is proposed, which, unlike the absolute coherence and the phase center, is invariant to the presence of a residual Dirac-like ground-scattering contribution. The inversion performance is addressed and compared for both single- and multi-baseline cases, in the absence and presence of temporal decorrelation. In all cases, the ground elevation is estimated from the tomographically reconstructed vertical reflectivity profiles and its accuracy directly influences the achieved inversion performance. The proposed methodology establishes a link between interferometric and tomographic measurements and is therefore relevant for missions that allow the implementation of both techniques. ESA's BIOMASS is one such mission, and as such is being used as a test case to discuss different implementation scenarios. The different inversion strategies are applied to P-band campaign data acquired in the frame of the AFRISAR 2016 experiment and validated against reference measurements.

Emerging spaceborne missions, like ESA’s BIOMASS, capable of capturing interferometric polarimetric and tomographic data, demand holistic model integration. The utilization of TomoSAR-modeled profiles offers the advantage of reducing the dimensionality of the forest height inversion using volumetric interferometric coherence. In dual baseline scenario, this approach can aid in inverting the ground-to-volume ratio and compensating for temporal decorrelation, especially advantageous for repeat-pass systems. Until now, the methods addressed in literature, tackle the problem of forest height estimation together with the estimation of the ground-to-volume and temporal decorrelations. In this paper, we derive a relation which allows to cancel the temporal decorrelation and polarization dependent parameter from the multi-parameter equation. It reduces the intrinsically 4-parameter estimation problem into 1-parameter, and allows to integrate the multi-baseline approach for structural parameters estimation.

The objective of this work is to investigate the possibility of separating, quantifying and characterizing individual structural / dielectric change contributions occurring in forest stands provided by tomographic SAR acquisitions at different times. This analysis is carried out by processing real multi-temporal L-band tomographic data over a temperate forest in the south of Germany. Different types of structural changes are firstly identified by means of high resolution lidar acquisitions. Their mapping into both tomographic reflectivity reconstructions and related structure indices is then derived. Reasons for ambiguities are discussed.

Nowadays, remote sensing methods have emerged as an important resource for monitoring polar regions. Microwaves penetrate into dry snow and ice, rendering SAR measurements sensitive to the underlying structure of glaciers and ice sheets. Based on the analysis of an experimental airborne dataset in the ablation zone of the Russel glacier in Greenland, an interesting and complex scattering pattern with an unknown glaciological origin is investigated. Polarimetric, interferometric and tomographic analyses of the SAR data are combined to gain an understanding of this scattering pattern. In the long term, an improved understanding of the glaciology of the research area combined with a relationship to its scattering properties in SAR data could lead to better observations and modeling of ice dynamics and mass balances.

Tomographic Synthetic Aperture Radar (SAR) allows the reconstruction of the 3D radar reflectivity of forests from a large(r) number of multi-angular acquisitions. However, in most practical implementations it suffers from limited vertical resolution and/or reconstruction artefacts as the result of non-ideal acquisition setups. Polarisation Coherence Tomography (PCT) offers an alternative to traditional tomographic techniques that allow the reconstruction of the low-frequency 3D radar reflectivity components from a small(er) number of multi-angular SAR acquisitions. PCT formulates the tomographic reconstruction problem as a series expansion on a given function basis. The expansion coefficients are estimated from interferometric coherence measurements between acquisitions. In its original form, PCT uses the Legendre polynomial basis for the reconstruction of the 3D radar reflectivity. This paper investigates the use of new basis functions for the reconstruction of X-band 3D radar reflectivity of forests derived from available lidar waveforms. This approach enables an improved 3D radar reflectivity reconstruction with enhanced vertical resolution, tailored to individual forest conditions. It also allows the translation from sparse lidar waveform vertical reflectivity information into continuous vertical reflectivity estimates when combined with interferometric SAR measurements. This is especially relevant for exploring the synergy of actual missions such as GEDI and TanDEM-X. The quality of the reconstructed 3D radar reflectivity is assessed by comparing simulated InSAR coherences derived from the reconstructed 3D radar reflectivity against measured coherences at different spatial baselines. The assessment is performed and discussed for interferometric TanDEM-X acquisitions performed over two tropical Gabonese rainforest sites: Mondah and Lopé. The results demonstrate that the lidar-derived basis provides more physically realistic vertical reflectivity profiles, which also produce a smaller bias in the simulated coherence validation, compared to the conventional Legendre polynomial basis.

This paper presents a new technique to retrieve low resolution vertical reflectivity profiles of semitransparent media by means of single-baseline synthetic aperture radar (SAR) interferometric acquisitions. The approach explores the dependency of the vertical wavenumber on the wavelength across the system bandwidth. By performing an inversion as a function of the vertical wavenumber, a low resolution vertical tomogram can be obtained. The paper expounds the mathematical framework of the technique, and the approach is demonstrated using simulations.

Knowledge about the vertical structure of forests, such as forest height, above-ground biomass (AGB), and the vertical biomass distribution is important for understanding carbon allocation, structural diversity, and succession and degradation dynamics in forest ecosystems. While the use of lidar (light detection and ranging) observations is well established to investigate the vertical structure of forests, the sensitivity of P-band synthetic aperture radar tomography (TomoSAR) observations to biomass and vertical forest structure is not yet well understood. Here we use lidar observations from NASA's Global Ecosystem Dynamics Investigation (GEDI) to analyse the sensitivity of airborne P-band SAR tomography backscatter to forest height and AGB at two tropical forests in Lop & eacute; and Mondah, Gabon, Africa. We use GEDI observations to parametrize an empirical model for estimating forest height and we use a random forest model for estimating AGB from TomoSAR profiles. The validation with Land, Vegetation, and Ice Sensor (LVIS) airborne lidar data shows moderate performance for estimating forest height (RMSE = 8.2 m in Lop & eacute; and 9.8 m in Mondah) and moderate to good performance for total AGB (RMSE = 115.3 Mg/ha in Lop & eacute; and 117.8 Mg/ha in Mondah). We also estimated the vertical distribution of AGB using the corrected TomoSAR backscatter and compared it with AGB profiles derived from field observations in Mondah, which indicates potential to use TomoSAR observations for estimating vertical AGB distribution over tropical forests. However, our results demonstrate the need for targeted field observations of vertical biomass profiles in order to make full use of P-band TomoSAR to map the vertical structure of tropical forests.

This article addresses the implementation of an above ground biomass (AGB) estimation scheme relying on the height-to-biomass allometry at stand level in the context of the synergistic use of continuous TanDEM-X (bistatic) interferometric synthetic aperture radar acquisitions and spatial discrete GEDI waveform lidar measurements. The estimation of forest height and horizontal forest structure from TanDEM-X data in the absence of a digital terrain model (DTM) is discussed. The possibility of estimating (top) canopy height variations independent of topographic height variations is discussed using wavelet-based scale analysis. This understanding is then exploited to define a structure index expressing the (top) canopy-only height variations in the absence of a DTM. The potential of using the derived structure information to account for the spatial variability of height-to-biomass allometry derived from the GEDI measurements is addressed. The performance of the conventional height-to-biomass allometry and the one achieved by the locally adapted implementation are compared against reference lidar measurements and discussed. The analysis is carried out using GEDI and TanDEM-X interferometric measurements and validated by using LVIS lidar measurements over the Lopé National Park, a diverse tropical forest test site in Gabon.

This paper provides an overview of the state of the art and an outlook on future developments of spaceborne Synthetic Aperture Radar (SAR) systems with multi-baseline imaging capability, such as 3D differential SAR interferometry (3D-DinSAR), polarimetric SAR interferometry (Pol-InSAR), tomography (TomoSAR), and holography (HoloSAR). The goal is to fill the multidimensional data space with additional information from images with different spatial and/or temporal baselines.

The present study addresses the development, implementation, and validation of a forest height mapping scheme based on the combination of TanDEM-X interferometric coherence and GEDI waveform measurements. The very general case where only a single polarisation TanDEM-X interferogram, a set of spatially discrete GEDI waveform measurements, and no DTM are available is assumed. The use of GEDI waveforms to invert the TanDEM-X interferometric measurements is described together with a set of performance criteria implemented to ensure a certain performance quality. The emphasis is set on developing a methodology able to invert forest height at large scales. Combining 595 TanDEM-X scenes and about 15 million GEDI waveforms, a spatially continuous 25-m resolution forest height map covering the whole of Tasmania Island is achieved. The derived forest height map is validated against an airborne lidar-derived canopy height map available across the whole island.

ESA Forest Carbon Monitoring project (FCM) is developing Earth Observation based, user-centric approaches for forest carbon monitoring. Forest carbon accounting based on forest inventory requires precise and timely estimation of forest variables at various spatial levels accompanied by verifiable uncertainty information. In this paper, we present the algorithm trade-off and selection approach and preliminary results of the algorithm intercomparison exercise in the FCM project. The studies were performed over 7 European test sites located in Finland, Ireland, Romania, Spain and Switzerland, and one tropical forest site in Peru. EO datasets were represented by Sentinel-1, Sentinel-2, TanDEM-X and ALOS-2 PALSAR-2 imagery. Examined approaches include popular parametric and SAR/InSAR scattering physics based approaches, and nonparametric and machine learning approaches such as k-NN, random forests, support vector regression.

The combination of TanDEM-X interferometric measurements with GEDI lidar full waveform measurements can provide continuous high-resolution forest height maps at global scale with sufficient accuracy without using external information about the underlyingtopography. In previous studies, the GEDI lidar full waveforms have been used to provide an approximation of the TanDEM-X X-band (radar) vertical reflectivity function in the height inversion of an entire TanDEM-X scene. This framework has been applied to the whole the whole Brazilian Amazon, and the obtained results are presented and analyzed in this paper. More than 12,000 TanDEM-X scenes and 250 millions GEDI lidar measurements have been processed.have.

Polarimetric SAR Interferometry (Pol-InSAR) is a SAR remote sensing discipline with unique and powerful applications related to the vertical structure of natural and man-made volume scatterers. The coherent combination of single- or multi -baseline interferograms acquired at different polarisations provides sensitivity to the vertical distribution of scattering processes and allows their characterisation by using the associated (volume) interferometric coherences [1] [2] [3] [4] [5].