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This data set is the first-of-its-kind spatial representation of multi-seasonal, global C-band Synthetic Aperture Radar (SAR) interferometric repeat-pass coherence and backscatter signatures. Coverage comprises land masses and ice sheets from 82° Northern to 79° Southern latitudes. The data set is derived from multi-temporal repeat-pass interferometric processing of about 205,000 Sentinel-1 C-band SAR images acquired in Interferometric Wide-Swath Mode from 1-Dec-2019 to 30-Nov-2020. The data set encompasses three sets of seasonal (December-February, March-May, June-August, September-November) metrics produced with a pixel spacing of three arcseconds: 1) Median 6-, 12-, 18-, 24-, 36-, and 48-days repeat-pass coherence at VV or HH polarizations, 2) Mean radiometrically terrain corrected backscatter (γ0) at VV and VH, or HH and HV polarizations, and 3) Estimated parameters of an exponential coherence decay model. The data set has been produced to obtain global, spatially detailed information on how decorrelation affects interferometric measurements of surface displacement and is rich in spatial and temporal information for a variety of mapping applications. Measurement(s) Seasonal C-band SAR coherence and backscatter from Dec. 2019 to Nov. 2020 Technology Type(s) SAR interferometric and radiometric terrain correction processing of Sentinel-1 SAR data Sample Characteristic - Environment Global land an coastal sea ice surfaces Sample Characteristic - Location Earth from 82 degrees northern to 79 degrees southern latitude [ABSTRACT FROM AUTHOR]

This article demonstrates how 3-D vegetation structure can be approximated by interferometric synthetic aperture radar (InSAR) backscatter-height histograms. Single-look backscatter measurements are plotted against the InSAR phase height and are aggregated spatially over a forest patch to form a 3-D histogram, referred to as InSAR backscatter-height histogram or simply InSAR histogram. InSAR histograms resemble LiDAR waveforms, suggesting that existing algorithms used to retrieve canopy height and ground topography from radar tomograms or LiDAR waveforms can be applied to InSAR histograms. Three algorithms are evaluated to generate maps of digital terrain, surface, and canopy height models: Gaussian decomposition, quantile, and backscatter threshold. Full-polarimetric L-band uninhabited aerial vehicle synthetic aperture radar (UAVSAR) data collected over the Gabonese Lopé National Park during the 2016 AfriSAR campaign are used to illustrate and compare the performance of the algorithms for the HH, HV, VV, HH+VV, and HH−VV polarimetric channels. Results show that radar-derived maps using the InSAR histograms differ by 4 m (top-canopy), 5 m (terrain), and 6 m (forest height) in terms of average root-mean-square errors (RMSEs) from standard maps derived from full-waveform laser, vegetation, and ice sensor (LVIS) LiDAR measurements. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) backscatter is the coherent combination of the scattering from multiple individual scatterers within the radar resolution cell, which results into a 2-D radar image. Tomographic SAR (TomoSAR) takes advantage of multiple SAR acquisitions to provide 3-D vertical structure of the imaged target. Over forests, parameters such as canopy structure, canopy density, leaf area and phenology contribute to different SAR scattering mechanisms along the vertical dimension of the trees. Tomography has been demonstrated in various earlier works for forest bio-physical parameter estimation using airborne data. In this research work, multi-polarimetric space-borne TanDEM-X tomograms are examined for the first time over a multi-species Indian tropical forest. Multiple TanDEM-X acquisitions are focused using Capon beamforming at five polarimetric channels — HH, HV, VV, HH − VV and HH + VV. Four distinct forest species compartments are selected representing different canopy structure and density. The tomograms obtained in different polarizations for these species are analyzed in detail to understand the scattering patterns across different forest species. Field surveys carried out in several forest locations provide in situ observations of forest height and vertical structure. It was observed that canopy gaps and leaf density play a crucial role for X-band SAR signal penetration through ground. For dense canopy species the backscatter contributions are spread through the canopy, while in case of sparse canopy species, the ground scattering is dominant. Vertical profiles obtained at surveyed plot locations are plotted in all polarizations, and provide a good agreement with field observations. Further, the obtained TomoSAR backscatter layers have a good correlation with field measured above-ground biomass (AGB). The AGB is modeled from TomoSAR with the HH-pol TomoSAR backscatter layer at 27 m canopy height leading to an AGB estimation with correlation r = 0.76 and RMSE of 50.4 t/ha. • First space-borne multi-polarimetric tomograms over Indian tropical forests • Analysis of effect of species on X-band TanDEM-X tomograms presented • SAR scattering along height of tropical forest visualized for different species • Potential of single-baseline space-borne SAR systems explored [ABSTRACT FROM AUTHOR]

The random volume over ground (RVoG) model has been widely applied to estimate forest tree height from polarimetric synthetic aperture radar (SAR) interferometry (PolInSAR) data for the past two decades. Successful application of the RVoG model requires certain assumptions to be valid for the imaged forest and the acquisition scenarios in order to avoid large errors in height estimates. Quantification of errors and uncertainties of RVoG-estimated heights have typically been limited to comparison against external validation data, such as lidar or field measurements. In this paper, we present a straightforward approach to simultaneously estimate height and height uncertainty from PolInSAR data using a Bayesian framework that accounts for errors in the data, as well as errors due to incorrect RVoG modeling assumptions, such as those caused by temporal decorrelation effects and errors in ground phase estimation. We apply our method to synthetic data to study how forest height uncertainty depends on modeling assumptions and PolInSAR acquisition parameters. We also compare our estimated Bayesian uncertainties to PolInSAR-derived and lidar-derived height RMS deviations observed over Gabonese tropical forests during the joint NASA-ESA 2016 AfriSAR campaign. Our results show good correspondence between uncertainties and deviations, as well as a strong correlation between uncertainty and estimated tree height. Furthermore, we demonstrate that we can associate specific areas of high uncertainty to confounding effects, such as temporal decorrelation and noncanopy related scattering. [ABSTRACT FROM AUTHOR]

This paper investigates the benefits of integrating multibaseline polarimetric interferometric SAR (PolInSAR) data with LiDAR measurements using a machine-learning approach in order to obtain improved forest canopy height estimates. Multiple interferometric baselines are required to ensure consistent height retrieval performance across a broad range of tree heights. Previous studies have proposed multibaseline merging strategies using metrics extracted from PolInSAR measurements. Here, we introduce the multibaseline merging using a support vector machine trained by sparse LiDAR samples. The novelty of this method lies in the new way of combining the two datasets. Its advantage is that it does not require a complete LiDAR coverage, but only sparse LiDAR samples distributed over the PolInSAR image. LiDAR samples are not used to obtain the best height among a set of height stacks, but rather to train the retrieval algorithm in selecting the best height using the variables derived through PolInSAR processing. This enables more accurate height estimation for a wider scene covered by the SAR with only partial LiDAR coverage. We test our approach on NASA AfriSAR data acquired over tropical forests by the L-band UAVSAR and the LVIS LiDAR instruments. The estimated height from this approach has a higher accuracy (r2 = 0.81, RMSE = 7.1 m) than previously introduced multibaseline merging approach (r2 = 0.67, RMSE = 9.2 m). This method is beneficial to future spaceborne missions, such as GEDI and BIOMASS, which will provide a wealth of near-contemporaneous LiDAR samples and PolInSAR measurements for mapping forest structure at global scale. [ABSTRACT FROM AUTHOR]

This paper presents a new damage-mapping algorithm based on coherence images estimated from multitemporal polarimetric-interferometric synthetic aperture radar (SAR) data. The interferometric coherence has been restricted in the conventional damage-mapping approaches because the decorrelation sources are too complicated to interpret accurately and temporal decorrelation effects caused by slowly occurring natural changes and disaster events are often coupled together. To overcome these limitations, we formulate a coherence model that accounts for temporal decorrelation in two simplified layers, ground and volume layers, for long-temporal repeat-pass scenarios with zero spatial baseline. The model parameters include: 1) ground-to-volume ratio, a factor to determine the relative scattering contribution of ground and volume layers; 2) temporally correlated change, which captures the exponentially decaying behavior of coherence with time; and 3) temporally uncorrelated change, which is associated with random temporal changes. We estimate the model parameters in three steps: coherence optimization, interferometric pair-invariant parameter estimation, and interferometric pair-variant parameter estimation. To isolate the effects of disaster events from background natural changes, we calculate the probability density functions of historical change pixel by pixel and produce a probability map of damage. We tested the algorithm with uninhabited aerial vehicle data acquired from 2009 to 2015 for mapping the area damaged by the 2015 Lake Fire in California. Based on performance evaluation using receiver operating characteristic curves for optimized coherences and averaged probability maps, the proposed method reduced the false alarm from 0.25 to 0.07 when the probability of detection was 0.85 compared to coherence products alone. [ABSTRACT FROM AUTHOR]

Detection of changes caused by major events—such as earthquakes, volcanic eruptions, and floods—from interferometric synthetic aperture radar (SAR) data is challenging because of the coupled effects with temporal decorrelation caused by natural phenomena, including rain, snow, wind, and seasonal changes. The coupled effect of major events and natural phenomena sometimes leads to misinterpretation of interferometric coherence maps and often degrades the performance of change detection algorithms. To differentiate decorrelation sources caused by natural changes from those caused by an event of interest, we formulated a temporal decorrelation model that accounts for the random motion of canopy elements, temporally correlated dielectric changes, and temporally uncorrelated dielectric changes of canopy and ground. The model parameters are extracted from the interferometric pairs associated with natural changes in canopy and ground using the proposed temporal decorrelation model. In addition, the cumulative distribution functions of the temporally uncorrelated model parameters, which are associated with natural changes in canopy and ground, are estimated from interferometric pairs acquired before the event. Model parameters are also extracted from interferometric SAR data acquired across the event and compared with the cumulative probabilities of natural changes in order to calculate the probability of a major event. Subsequently, pixels with cumulative probabilities greater than 75% are marked as changed due to the event. A case study for detecting volcanic ash during the eruption of the Shinmoedake volcano in January 2011 was carried out using L-band Advanced Land Observation Satellite PALSAR data. [ABSTRACT FROM AUTHOR]

This paper addresses the important yet unresolved problem of estimating forest properties from polarimetric-interferometric radar images affected by temporal decorrelation. We approach the problem by formulating a physical model of the polarimetric-interferometric coherence that incorporates both volumetric and temporal decorrelation effects. The model is termed random-motion-over-ground (RMoG) model, as it combines the random-volume-over-ground (RVoG) model with a Gaussian-statistic motion model of the canopy elements. Key features of the RMoG model are: 1) temporal decorrelation depends on the vertical structure of forests; 2) volumetric and temporal coherences are not separable as simple multiplicative factors; and 3) temporal decorrelation is complex-valued and changes with wave polarization. This third feature is particularly important as it allows compensating for unknown levels of temporal decorrelation using multiple polarimetric channels. To estimate model parameters such as tree height and canopy motion, we propose an algorithm that minimizes the least square distance between model predictions and complex coherence observations. The algorithm was applied to L-band NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar data acquired over the Harvard Forest (Massachussetts, USA). We found that the RMS difference at stand level between estimated RMoG-model tree height and NASA's lidar Laser Vegetation and Ice Sensor tree height was within 12% of the lidar-derived height, which improved significantly the RMS difference of 37% obtained using the RVoG model and ignoring temporal decorrelation. This result contributes to our ability of estimating forest biomass using in-orbit and forthcoming polarimetric-interferometric radar missions. [ABSTRACT FROM PUBLISHER]

In this letter we propose a three-baseline approach to the extraction of forest tree height from synthetic aperture radar data. Three polarimetric–interferometric pairs are used to constrain a physical model that relates forest parameters to multiple repeat-pass coherence observations. The observations may be performed by a dual, compact or full polarimetric radar, and may be affected by distinct levels of temporal decorrelation. Here, we present the theoretical framework based on the random-motion-over-ground model, and describe an algorithm to extract tree height from the data. The performance of the algorithm is illustrated with L-band airborne data collected by the German Aerospace Center in the frame of the BIOSAR2008 campaign. The proposed method provides height estimates in good agreement with lidar measurements and can be applied to data to be collected by forthcoming polarimetric–interferometric spaceborne missions. [ABSTRACT FROM PUBLISHER]

Forest above-ground biomass (AGB) estimation from SAR backscatter is affected by varying imaging and environmental conditions. This paper quantifies and compares the performance of forest biomass estimation from L-band SAR backscatter measured selectively under dry and wet conditions during the 2019 AM-PM NASA airborne campaign. Seven Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) images acquired between June and October 2019 over a temperate deciduous forest in Southeastern United States with varying moisture and precipitation conditions are examined in conjunction with LIDAR and field measurements. Biomass is estimated by fitting a 3-parameter modified Water Cloud Model (WCM) to radiometric terrain corrected SAR backscatter. Our experiment is designed to quantify the biomass estimation errors when biomass models are calibrated and validated on varying acquisition conditions (dry or wet). Multi-temporal estimation strategies are also evaluated and compared with single-acquisition estimation approaches. As an outcome, the experiment shows that the WCM model calibrated and validated on single acquisitions adapts to different soil moisture conditions with RMSD up to 18.7 Mg/ha. The AGB estimation performance, however, decreases with RMSD upwards of 30 Mg/ha when the model is cross-validated on moisture and precipitation conditions different than the calibration conditions. Results confirm that calibrating the model over the multi-temporal data using averaged backscatter or weighted combinations of individual AGB estimates, improves the biomass estimation accuracy up to about 20% at L-band. This study helps design biomass cal/val procedures and biomass estimation algorithms for dense time-series to be collected by low-frequency radar missions such as NASA-ISRO SAR (NISAR) and BIOMASS. [ABSTRACT FROM AUTHOR]

Developing and enhancing strategies to characterize actual forests structure is a timely challenge, particularly for tropical forests. P-band synthetic aperture radar (SAR) tomography (TomoSAR) has previously been demonstrated as a powerful tool for characterizing the 3-D vertical structure of tropical forests, and its capability and potential to retrieve tropical forest structure has been discussed and assessed. On the other hand, the abilities of L-band TomoSAR are still in the early stages of development. Here, we aim to provide a better understanding of L-band TomoSAR capabilities for retrieving the 3-D structure of tropical forests and estimating the top height in dense forests. We carried out tomographic analysis using L-band UAVSAR data from the AfriSAR campaign conducted over Gabon Lopé Park in February 2016. First, it was found that L-band TomoSAR was able to penetrate into and through the canopy down to the ground, and thus the canopy and ground layers were detected correctly. The resulting TomoSAR vertical profiles were validated with a digital terrain model and canopy height model extracted from small-footprint Lidar (SFL) data. Second, there was a strong correlation between the L-band Capon beam forming profile in HH and HV polarizations with Land Vegetation Ice Sensor (LVIS) Level 1B waveform Lidar over different kinds of forest in Gabon Lopé National Park. Finally, forest top height from the L-band data was estimated and validated with SFL data, resulting in a root mean square error of 3 m and coefficient of determination of 0.92. The results demonstrate that L-band TomoSAR is capable of characterizing 3-D structure of tropical forests. [ABSTRACT FROM AUTHOR]

Forest height is an important forest biophysical parameter which is used to derive important information about forest ecosystems, such as forest above ground biomass. In this paper, the potential of combining Polarimetric Synthetic Aperture Radar (PolSAR) variables with LiDAR measurements for forest height estimation is investigated. This will be conducted using different machine learning algorithms including Random Forest (RFs), Rotation Forest (RoFs), Canonical Correlation Forest (CCFs) and Support Vector Machine (SVMs). Various PolSAR parameters are required as input variables to ensure a successful height retrieval across different forest heights ranges. The algorithms are trained with 5000 LiDAR samples (less than 1% of the full scene) and different polarimetric variables. To examine the dependency of the algorithm on input training samples, three different subsets are identified which each includes different features: subset 1 is quiet diverse and includes non-vegetated region, short/sparse vegetation (0–20 m), vegetation with mid-range height (20–40 m) to tall/dense ones (40–60 m); subset 2 covers mostly the dense vegetated area with height ranges 40–60 m; and subset 3 mostly covers the non-vegetated to short/sparse vegetation (0–20 m).The trained algorithms were used to estimate the height for the areas outside the identified subset. The results were validated with independent samples of LiDAR-derived height showing high accuracy (with the average R2 = 0.70 and RMSE = 10 m between all the algorithms and different training samples). The results confirm that it is possible to estimate forest canopy height using PolSAR parameters together with a small coverage of LiDAR height as training data. [ABSTRACT FROM AUTHOR]

In order to help scientists in the above ground biomass community and to support the science behind the upcoming BIOMASS, NISAR, and GEDI satellite missions, ESA and NASA are collaborating on the Multi-Mission Algorithm and Analysis Platform (MAAP). The MAAP is a jointly developed and implemented platform that will include not only data (satellite, airborne, in situ data, and products), but also computing capabilities and sets of tools and algorithms developed to support this specific field of research. To best ensure that users are able to 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. In addition to aiding researchers, the MAAP exercise is establishing a collaboration framework between ESA and NASA that focuses on sharing data, science algorithms, and computable resources in order to foster and accelerate scientific research. [ABSTRACT FROM AUTHOR]

In 2015 and 2016, the AfriSAR campaign was carried out as a collaborative effort among international space and National Park agencies (ESA, NASA, ONERA, DLR, ANPN and AGEOS) in support of the upcoming ESA BIOMASS, NASA-ISRO Synthetic Aperture Radar (NISAR) and NASA Global Ecosystem Dynamics Initiative (GEDI) missions. The NASA contribution to the campaign was conducted in 2016 with the NASA LVIS (Land Vegetation and Ice Sensor) Lidar, the NASA L-band UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar). A central motivation for the AfriSAR deployment was the common AGBD estimation requirement for the three future spaceborne missions, the lack of sufficient airborne and ground calibration data covering the full range of ABGD in tropical forest systems, and the intercomparison and fusion of the technologies. During the campaign, over 7000 km2 of waveform Lidar data from LVIS and 30,000 km2 of UAVSAR data were collected over 10 key sites and transects. In addition, field measurements of forest structure and biomass were collected in sixteen 1-hectare sized plots. The campaign produced gridded Lidar canopy structure products, gridded aboveground biomass and associated uncertainties, Lidar based vegetation canopy cover profile products, Polarimetric Interferometric SAR and Tomographic SAR products and field measurements. Our results showcase the types of data products and scientific results expected from the spaceborne Lidar and SAR missions; we also expect that the AfriSAR campaign data will facilitate further analysis and use of waveform lidar and multiple baseline polarimetric SAR datasets for carbon cycle, biodiversity, water resources and more applications by the greater scientific community. • In 2016, the international collaborative air and field campaign AfriSAR took place • The data provides calibration and validation for NASA GEDI, NISAR and ESA BIOMASS • NASA collected coincident L-band SAR (UAVSAR) and waveform Lidar (LVIS) data • We produced SAR- and Lidar derived canopy height, biomass and vertical profile datasets • We provide an overview of the airborne mission, data products and applications [ABSTRACT FROM AUTHOR]