Tandem-L: Main results of the phase a feasibility study

Krieger, Gerhard; Moreira, Alberto; Zink, Manfred; Hajnsek, Irena; Huber, Sigurd; Villano, Michelangelo; Papathanassiou, Kostas; Younis, Marwan; López-Dekker, Paco; Pardini, Matteo; Schulze, Daniel; Bachmann, Markus; Tridon, D. Borla; Reimann, Jens; Bräutigam, Benjamin; Steinbrecher, Ulrich; Tienda, C.; Ferrer, M. Sanjuan; Zonno, Mariantonietta; Eineder, Michael; Zan, Francesco De; Parizzi, Alessandro; Fritz, Thomas; Diedrich, Erhard; Maurer, Edith; Münzenmayer, R.; Grafmuller, B.; Wolters, Reinhard; Hennepe, F. te; Ernst, Robert R.; Bewick, Charlotte

doi:10.1109/igarss.2016.7729546

Cited by 10 publications

(13 citation statements)

References 9 publications

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“…Recently, tomographic acquisitions, which will be seen as an extension of multi-baseline interferometric acquisitions, have been used to reconstruct the three-dimensional radar reflectivity of forests [17][18][19][20][21][22][23]. The promising outcomes accomplished initiated the execution of TomoSAR acquisition modes in future spaceborne SAR missions, like Biomass [24] or Tandem-L [25], for mapping structural forest parameters and to enhance the performance of biomass estimators at a global scale.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Tropical Forest Structure Using SAR Tomography at L- and P-Band

et al. 2019

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Our study aims to provide a comparison of the P- and L-band TomoSAR profiles, Land Vegetation and Ice Sensor (LVIS), and discrete return LiDAR to assess the ability for TomoSAR to monitor and estimate the tropical forest structure parameters for enhanced forest management and to support biomass missions. The comparison relies on the unique UAVSAR Jet propulsion Laboratory (JPL)/NASA L-band data, P-band data acquired by ONERA airborne system (SETHI), Small Footprint LiDAR (SFL), and NASA Land, Vegetation and Ice Sensor (LVIS) LiDAR datasets acquired in 2015 and 2016 in the frame of the AfriSAR campaign. Prior to multi-baseline data processing, a phase residual correction methodology based on phase calibration via phase center double localization has been implemented to improve the phase measurements and compensate for the phase perturbations, and disturbances originated from uncertainties in allocating flight trajectories. First, the vertical structure was estimated from L- and P-band corrected Tomography SAR data measurements, then compared with the canopy height model from SFL data. After that, the SAR and LiDAR three-dimensional (3D) datasets are compared and discussed at a qualitative basis at the region of interest. The L- and P-band’s performance for canopy penetration was assessed to determine the underlying ground locations. Additionally, the 3D records for each configuration were compared with their ability to derive forest vertical structure. Finally, the vertical structure extracted from the 3D radar reflectivity from L- and P-band are compared with SFL data, resulting in a root mean square error of 3.02 m and 3.68 m, where the coefficient of determination shows a value of 0.95 and 0.93 for P- and L-band, respectively. The results demonstrate that TomoSAR holds promise for a scientific basis in forest management activities.

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Section: Introductionmentioning

confidence: 99%

Monitoring Tropical Forest Structure Using SAR Tomography at L- and P-Band

et al. 2019

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“…This section provides a short overview of future RS technologies as well as existing data products, especially with respect to the Tandem-L mission ̶ a mission proposal suggesting two Lband (~24 cm wavelength) SAR satellites in helical formation flight [46] (see also Table 6). This tandem formation enables single-pass interferometry and thereby 3D imaging of the land surface.…”

Section: A Summary Of Future Rs Technologies and Existing Data Producmentioning

confidence: 99%

“…However, with Tandem-L a global high-resolution DEM will be produced every year as opposed to only twice in the mission's life time as is the case for TanDEM-X. This is enabled by cutting-edge SAR acquisition technology including digital feed arrays combined with a mesh reflector as well as signal recoding using digital beamforming [46]. The application of L-band waves, instead of X-band (~3 cm wavelength) makes transmission through vegetation possible.…”

Section: A Summary Of Future Rs Technologies and Existing Data Producmentioning

confidence: 99%

“…and erosion on land and in coastal waters [42,43]. Recent RS technologies such as the satellite-based light detection and ranging (LiDAR), global ecosystem dynamics investigation (GEDI) [44,45], as well as upcoming radio direction and ranging (RADAR) technologies such as the Tandem-L [46,47], NISAR (NASA-ISRO Synthetic Aperture RADAR) or even Rose-L (Copernicus High Priority Candidate Mission), alone and in combination with imaging spectroscopy [48] and thermal infrared (TIR) sensor technology such as the Copernicus Hyperspectral Imaging Mission (CHIME) [49], the Hyperspectral Infrared Imager Mission (HyspIRI, [50]) and Environmental Mapping and Analysis Program (EnMAP, [51]), open up new opportunities for a global monitoring of geo-and biodiversity and their interactions [3,[52][53][54].…”

Section: Introductionmentioning

confidence: 99%

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Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces

Lausch

Schaepman

Skidmore³

et al. 2020

Remote Sensing

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The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring.

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“…Staggered SAR is currently the baseline acquisition mode of the Tandem-L concept [16]. Tandem-L's goal is to acquire single/dual-polarimetric data over a 350 km ground-swath and fully polarimetric data over a 175 km ground-swath, with an azimuth resolution of around 7.5 m. Optimum oversampling factors are calculated considering the ambiguity level requirements and the designed PRI sequence, and amount to 2.3 and 1.9 for the dual-pol and quad-pol modes.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Low-Oversampled Staggered SAR Data

Pinheiro

Prats-Iraola

Rodríguez-Cassolà³

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The combination of SCan-On-REceive with continuous variation of the pulse repetition interval during transmission (staggered operation) is a viable option for the acquisition of highresolution synthetic aperture radar (SAR) data over wide areas. Since the acquired data are not uniformly sampled and contain gaps within the synthetic aperture mainly due to the interruption of reception during transmission (i.e., due to blockage), proper reconstruction strategies must be considered in order to minimize artifacts. Conventionally, large oversampling rates are required to ensure low ambiguity levels, but this is not always feasible due to data-rate constrains. This article presents a detailed analysis of the blockage recovery in the low-oversampling case. Moreover, we propose a data adaptive strategy to optimally perform this step, ensuring the best performance for point-like targets and avoiding degradation for distributed scatters. This paper also presents simulation results that show the impact of staggered data for interferometric applications.

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Tandem-L: Main results of the phase a feasibility study

Cited by 10 publications

References 9 publications

Monitoring Tropical Forest Structure Using SAR Tomography at L- and P-Band

Monitoring Tropical Forest Structure Using SAR Tomography at L- and P-Band

Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces

Analysis of Low-Oversampled Staggered SAR Data

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