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TerraSAR-X (TSX), TanDEM-X (TDX), and PAZ Synthetic Aperture Radar data have been used at over 120 volcanoes to assess surface characteristics and change over time. We examine previous work, adding additional examples to understand where and when these data are most useful for volcanology. We focus on volcanoes as part of the Committee on Earth Observation Satellites (CEOS) Volcano Demonstrator Project. TSX/TDX/PAZ data provide a valuable means of detecting small surface changes from amplitude images and topographic changes from bistatic TSX/TDX data. For short temporal and perpendicular baselines, TDX/TSX/PAZ can also provide useful deformation data, even in presence of vegetation. No global background mission currently acquires TSX/TDX/PAZ data at volcanoes: 70 % high spatial resolution data, limiting their suitability for studying pre-eruptive unrest. Coordinated targeting by SAR constellations of priority volcanoes would provide data and insights valuable for forecasting eruptions and associated hazards.

The study of ancient irrigation is crucial in the archaeological research of arid regions. It covers a wide range of topics, with the Near East being the focus for decades. However, political instability and limited data have posed challenges to these studies. The primary objective is to establish a standardised method applicable to different arid environments using the Google Earth Engine platform, considering local relief of terrain and seasonal differences in vegetation. This study integrates multispectral data from LANDSAT 5, Sentinel-2, SAR imagery from Sentinel 1, and TanDEM-X (12 m and 30 m) DSMs. Using these datasets, calculations of selected vegetation indices such as the SMTVI and NDVSI, spectral decomposition methods such as TCT and PCA, and topography-based methods such as the MSRM contribute to a comprehensive understanding of landscape irrigation. This paper investigates the influence of modern environmental conditions on the visibility of features like levees and palaeo-channels by testing different methods and parameters. This study aims to identify the most effective approach for each case study and explore the possibility of applying a consistent method across all areas. Optimal results are achieved by combining several methods, adjusting seasonal parameters, and conducting a comparative analysis of visible features. [ABSTRACT FROM AUTHOR]

This research conducts a comparative analysis of the vertical accuracies of satellite-derived Digital Elevation Models (DEMs) in the Akure South Local Government Area, Ondo State, Nigeria. The DEMs assessed were obtained from three open-source satellite missions: Sentinel-1A, ALOS PALSAR, and TanDEM-X. The reference Ground Control Points (GCPs) used in the evaluations comprised 133 GCPs of 2nd and 3rd-order accuracy acquired from the Office of the Surveyor General, Ondo State, and through field surveys using the South Galaxy-1 Differential GPS. These obtained GCPs provided extensive coverage of the study area. The evaluation was undertaken based on the land cover, which was established by dividing the study area into open (built-up and bare land) and vegetation-covered (agricultural and forested) areas. The vertical accuracies of the satellite-based DEM collected within the study area were then compared and analyzed. The results revealed that Sentinel-1A and TanDEM-X were the most suitable in both open and vegetation-covered parts of the study area, with Sentinel-1A exhibiting the best performance. The effect of vegetation cover was mostly felt by ALOS PALSAR. Sentinel-1A and TanDEMX displayed commendable vertical accuracies, deemed highly suitable for applications in topographic mapping, as shown by an RMS of less than 2m. This research recommends the open-source satellite DEMs Sentinel-1A and TanDEM-X for planning and engineering applications because they offer the highest accuracy among their counterparts within Akure and its environs. [ABSTRACT FROM AUTHOR]

TanDEM-X bistatic interferometric system successfully generated a high-precision, high-resolution global digital elevation model (DEM). However, in forested areas, two core problems make it difficult to obtain sub-canopy topography: (1) the penetrability of short-wave signals is limited, and the DEM obtained in dense forest areas contains a significant forest signal, that is, the scattering phase center (SPC) height; and (2) the single-baseline and single-polarization TanDEM-X interferometric synthetic aperture radar (InSAR) data cannot provide sufficient observations to make the existing physical model reversible for estimating the real surface phase, whereas the introduction of optical data makes it difficult to ensure data synchronization and availability of cloud-free data. To overcome these problems in accurately estimating sub-canopy topography from TanDEM-X InSAR data, this study proposes a practical method of sub-canopy topography estimation based on the following innovations: (1) An orthogonal polynomial model was established using TanDEM-X interferometric coherence and slope to estimate the SPC height. Interferometric coherence records forest height and dielectric property information from an InSAR perspective and has spatiotemporal consistency with the InSAR-derived DEM. (2) Introduce Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data to provide more observational information and automatically screen ICESat-2 control points with similar forest and slope conditions in the local area to suppress forest spatial heterogeneity. (3) A weighted least squares criterion was used to solve this model to estimate the SPC height. The results were validated at four test sites using high-precision airborne light detection and ranging (LiDAR) data as a reference. Compared to the InSAR-derived DEM, the accuracy of the sub-canopy topography was improved by nearly 60%, on average. Furthermore, we investigated the necessity of local modeling, confirming the potential of the proposed method for estimating sub-canopy topography by relying only on TanDEM-X and ICESat-2 data. [ABSTRACT FROM AUTHOR]

A challenging effect in the retrieval of ocean surface currents from synthetic aperture radar (SAR) is the so-called wave-induced Doppler velocity. This effect arises from the correlation between the modulation of the normalized radar cross section (NRCS) caused by the slopes of the long waves and the modulation of the Doppler shift resulting from their orbital velocities. In this article, we analyzed six acquisitions of high-resolution interferometric SAR images of collocated NRCS and Doppler frequency shift acquired by the along-track interferometric SAR TanDEM-X at two polarizations VV and HH. The unique high-quality wave-resolving interferograms of TanDEM-X allow a detailed investigation of the wave-induced Doppler at scales rarely available from space. These images are acquired in different regions, times, and satellite passes (ascending or descending), thus capture different wave conditions in height and propagation direction. First, it is shown that in cases of range propagating waves, the NRCS and Doppler shift modulations are highly correlated. The sign of the correlation coefficient depends on the direction of propagation of the wave relative to the SAR. Moreover, it is shown that the NRCS-Doppler correlation affects the mean Doppler shift and that the wave-induced Doppler velocity increases with the wave steepness. In cases of azimuth propagating waves, the linear correlation is very low due to large phase difference between the NRCS and the Doppler shift modulations. Consequently, the wave-induced Doppler velocity is negligible. Finally, the wave-induced Doppler, if not taken into account, can bias the ocean current estimation by up to 0.96 m/s.

To address the challenge of retrieving subcanopy topography using single-baseline single-polarization TanDEM-X interferometric synthetic aperture radar (InSAR) data, we propose a novel InSAR processing framework. Our methodology begins by employing the SINC model to estimate the penetration depth (PD). Subsequently, we establish a linear relationship between PD and phase center height (PCH) to generate a wall-to-wall PCH product. To achieve this, spaceborne light detection and ranging (LiDAR) data are employed to capture the elevation bias between actual ground elevation and InSAR-derived elevation. Finally, the subcanopy topography is derived by subtracting the PCH from the conventional InSAR-based digital elevation model (DEM). Moreover, this approach enables the simultaneous estimation of forest height from single-baseline TanDEM-X data by combining the estimated PD and PCH components. The approach has been validated against airborne LiDAR scanning (ALS) data over four diverse sites encompassing different forest types, terrain conditions, and climates. The derived subcanopy topography in the boreal and hemiboreal forest sites (Krycklan and Remningstorp) demonstrated notable improvement in accuracy. Additionally, the winter acquisitions outperformed the summer ones in terms of inversion accuracy. The achieved root-mean-square errors (RMSEs) for the winter scenarios were 2.45 m and 3.83 m, respectively, representing a 50% improvement over the InSAR-based DEMs. The forest heights are also close to the ALS measurements, with RMSEs of 2.70 m and 3.33 m, respectively. For the Yanguas site in Spain, characterized by rugged terrain, subcanopy topography in forest areas was estimated with an accuracy of 4.27 m, a 35% improvement over the original DEM. For the denser tropical forest site, only an average elevation bias could be corrected.

TanDEM-X interferometric synthetic aperture radar (InSAR) data have demonstrated promising advantages and potential in recent years for the inversion of forest height. InSAR coherence becomes the primary input feature when a precise digital terrain model is unavailable, but the relationship between InSAR coherence and forest height remains uncertain because of the complexity of forest scenes. In this article, a method for retrieving canopy height in Mediterranean forests, characterized by short and sparse trees, using a single-pass bistatic TanDEM-X InSAR dataset is proposed. To improve the accuracy of forest height inversion from the uncertain correlation between InSAR coherence and canopy height, we begin by using the established SINC model with two semiempirical parameters and then expand the single curve into a collection of three curves, forming the multi-SINC model. To determine the optimal relationship (curve) between TanDEM-X InSAR coherence and canopy height, the problem is shifted from parameter inversion to classification. To solve the problem, we used optical remote sensing data, a small amount of light detection and ranging (LiDAR) data, and TanDEM-X InSAR data in combination with machine learning for classification. As a proof-of-concept, we conducted forest height retrieval at two study sites in Spain with complex terrain and diverse forest types. The results were verified by comparing them with LiDAR product forest height, which demonstrated improved performance (RMSE = 2.49 m and 1.7 m) compared with the semiempirical SINC model (RMSE = 3.28 m and 2.36 m).

The study conducted aimed to examine the accuracy of Digital Elevation Models (DEMs) in Egypt, specifically the TanDEM-X mission's 30 m and 12 m resolution DEMs and the SRTM DEM with a 30 m resolution. The accuracy of DEMs is essential for various civil engineering and surveying applications, especially in geoscience applications. To ensure the comparison's accuracy, the study used ellipsoidal heights instead of orthometric heights, preventing errors caused by global geopotential models in the conversion process. The evaluation of the three DEMs was carried out using 352 GNSS points. The findings indicate that both TanDEM-X DEMs with 30 m and 12 m resolutions outperform the SRTM 30 m in terms of vertical accuracy, making them ideal for geomatic applications that require higher-resolution DEMs. The TanDEM-X 30 m generates a standard deviation (STD) and a Root Mean Square Error (RMSE) of approximately 3.03 m and 3.45 m, respectively. On the other hand, the TanDEM-X 12 m generates an STD and RMSE of approximately 2.86 m and 3.18 m, respectively. Comparatively, the SRTM 30 m produces an STD and RMSE of approximately 4.67 m and 5.35 m, respectively.

Digital elevation models (DEMs) are crucial for numerous assessments of the Earth's surface, so this research aim to evaluate the quality of several open source DEMs like advanced spaceborne thermal emission and reflection radiometer global DEM (ASTER GDEM), shuttle radar topography mission DEM (SRTM DEM), SENTINEL-1, and TerraSAR-X for digital elevation measurement (TanDEM-X). TanDEM-X is one of the newest DEM models; however, ASTER and SRTM are still the most used. To evaluate the qualities of these DEMs, the vertical accuracies of ASTER, STRM (all 30 m), SRTM, TanDEM-X (90 m), and SENTINEL 1 (range from 5 to 12.5 m) were tested using GPS reference points and The RMSE gave the values of ± 3.56 m ± 57.68 m, ±5.89 m, ±10.21 m and ± 20.25 m for used SRTM1, SENTINEL-1, SRTM3, TanDEM-X, and ASTER DEMs respectively. As is well known, obtaining a high-quality DEM is exceedingly challenging since it is time-consuming, expensive, and often constrained, therefore this study examined how to enhance SRTM1 as it gave the best result and assessed the effects of increasing the number of points on digital elevation model accuracy by using raster calculator and merge model in ArcGIS. The results showed that the merge model method gave the best result and, by increasing the number of reference points, the accuracy increased. The DEM produced by the enhanced SRTM (iSRTM) demonstrated noticeably better outcomes than the baseline SRTM DEM, with around 3.56 m RMSE.In this research, STRM-1 DEM is used to determine watershed basin delineation. The topographic map showed significant variations in the level of the Earth's surface, as the heights of the area ranged between 20 m and 120 m. The number of watersheds extracted in the Benban is 132 with different areas, the large area is about 72.14 km2, while the small area is about 1.13 km2, and between those areas, there are various areas. The largest recorded watershed circumference reached 48.94 km in area (72.14 km2), while the lowest recorded perimeter is 5.40 km for the area (1.13 km2).

The study conducted aimed to examine the accuracy of Digital Elevation Models (DEMs) in Egypt, specifically the TanDEM-X mission's 30 m and 12 m resolution DEMs and the SRTM DEM with a 30 m resolution. The accuracy of DEMs is essential for various civil engineering and surveying applications, especially in geoscience applications. To ensure the comparison's accuracy, the study used ellipsoidal heights instead of orthometric heights, preventing errors caused by global geopotential models in the conversion process. The evaluation of the three DEMs was carried out using 352 GNSS points. The findings indicate that both TanDEM-X DEMs with 30 m and 12 m resolutions outperform the SRTM 30 m in terms of vertical accuracy, making them ideal for geomatic applications that require higher-resolution DEMs. The TanDEM-X 30 m generates a standard deviation (STD) and a Root Mean Square Error (RMSE) of approximately 3.03 m and 3.45 m, respectively. On the other hand, the TanDEM-X 12 m generates an STD and RMSE of approximately 2.86 m and 3.18 m, respectively. Comparatively, the SRTM 30 m produces an STD and RMSE of approximately 4.67 m and 5.35 m, respectively. [ABSTRACT FROM AUTHOR]

Forest canopy height estimation is essential for forest management and biomass estimation. In this study, we aimed to evaluate the capacity of TanDEM-X interferometric synthetic aperture radar (InSAR) data to estimate canopy height with the assistance of an external digital terrain model (DTM). A ground-to-volume ratio estimation model was proposed so that the canopy height could be precisely estimated from the random-volume-over-ground (RVoG) model. We also refined the RVoG inversion process with the relationship between the estimated penetration depth (PD) and the phase center height (PCH). The proposed method was tested by TanDEM-X InSAR data acquired over relatively homogenous coniferous forests (Teruel test site) and coniferous as well as broadleaved forests (La Rioja test site) in Spain. Comparing the TanDEM-X-derived height with the LiDAR-derived height at plots of size 50 m × 50 m, the root-mean-square error (RMSE) was 1.71 m (R2 = 0.88) in coniferous forests of Teruel and 1.97 m (R2 = 0.90) in La Rioja. To demonstrate the advantage of the proposed method, existing methods based on ignoring ground scattering contribution, fixing extinction, and assisting with simulated spaceborne LiDAR data were compared. The impacts of penetration and terrain slope on the RVoG inversion were also evaluated. The results show that when a DTM is available, the proposed method has the optimal performance on forest height estimation. [ABSTRACT FROM AUTHOR]

Precise DEMs at high spatial resolution are indispensable for a variety of scientific studies and applications. Presently, the TanDEM-X mission possesses the capability to collect global-scale InSAR data at high spatial resolution, enabling the generation of a high-resolution global DEM (12 m). Nevertheless, directly utilizing InSAR data poses challenges in detecting sub-canopy topography within forest areas, due to the presence of volume scattering and limited penetration of X-band. Conversely, the upcoming BIOMASS mission operated in P-band will provide an exceptional opportunity for sub-canopy topography extraction, owing to its strong penetration and the capability to collect fully-polarimetric SAR data. However, it is imperative to acknowledge that BIOMASS data do have its own limitation, manifesting as lower resolution (100 m) due to limited bandwidth. To address these challenges and generate high-resolution sub-canopy topography, we propose a new method that leverages the strengths of both TanDEM-X InSAR and BIOMASS PolInSAR datasets through the wavelet transform. We evaluated the performance of our method at two test sites characterized by different forest types and terrain conditions using airborne LiDAR data. Our findings demonstrate a significant improvement in sub-canopy topography accuracy. Specifically, under the boreal coniferous forest scenario, the root mean square error (RMSE) of the resulting sub-canopy topography decreased by 44% compared to the TanDEM-X InSAR DEM. In tropical broadleaf forest scenario, the RMSE reduction reached 64% over the TanDEM-X InSAR DEM. These results indicate the potential of our approach for high-resolution sub-canopy topography mapping by combing data from these two different spaceborne SAR sensors. [ABSTRACT FROM AUTHOR]

During the last decades, freely available GDEMs, such as ASTER, SRTM, and AW3D30, have been widely used in many applications such as for environmental, spatial analysis, research in geomorphology, hydrology, etc. However, these available GDEMs suffer from various limitations. In order to enhance the quality and accuracy of GDEMs, several GDEMs have been merged or reprocessed using a more rigorous method to develop new GDEMs. The advent of these new improvised GDEMs has advanced their applications. Unfortunately, there are very limited studies that focus on the comprehensive and systematic evaluation of the quality of improvised GDEM. Therefore, this study examines the vertical accuracy of three freely available improvised GDEMs (MERIT, NASA, and EarthEnv GDEMs) over the northern region of Peninsular Malaysia using 7757 GNSS points and two reference model, i.e., TanDEM-X DEM 12m resolution and local airborne IFSAR DEM 5m resolution. The accuracy assessments have been performed over three different land covers (urban, non-forest, and forest areas) to evaluate the impact of different land covers on the GDEM's accuracy. Since SRTM DEM is the primary data input in the improvised GDEM, this GDEM is also considered to identify the performance of the new improvised GDEMs. Comparison with GNSS points shows that the accuracy of MERIT DEMs outperforms SRTM DEM and other GDEMs with RMSE of ±2.668m, followed by NASA (±3.656m), SRTM (±5.666m), and EarthEnv (±5.948m). The vertical accuracy of DEM varies with different land cover conditions. Comparison with TanDEM-X and IFSAR DEM shows that all tested GDEMs' accuracy is high over a nonforest area, followed by urban area, and worse over forest area. Overall, the tested GDEM shows only a slight improvement compared to the SRTM. However, these results will help users in selecting the optimum DEM for any application. [ABSTRACT FROM AUTHOR]

Digital elevation models (DEMs) are crucial for numerous assessments of the Earth's surface, so this research aim to evaluate the quality of several open source DEMs like advanced spaceborne thermal emission and reflection radiometer global DEM (ASTER GDEM), shuttle radar topography mission DEM (SRTM DEM), SENTINEL-1, and TerraSAR-X for digital elevation measurement (TanDEM-X). TanDEM-X is one of the newest DEM models; however, ASTER and SRTM are still the most used. To evaluate the qualities of these DEMs, the vertical accuracies of ASTER, STRM (all 30 m), SRTM, TanDEM-X (90 m), and SENTINEL 1 (range from 5 to 12.5 m) were tested using GPS reference points and The RMSE gave the values of ± 3.56 m ± 57.68 m, ±5.89 m, ±10.21 m and ± 20.25 m for used SRTM1, SENTINEL-1, SRTM3, TanDEM-X, and ASTER DEMs respectively. As is well known, obtaining a high-quality DEM is exceedingly challenging since it is time-consuming, expensive, and often constrained, therefore this study examined how to enhance SRTM1 as it gave the best result and assessed the effects of increasing the number of points on digital elevation model accuracy by using raster calculator and merge model in ArcGIS. The results showed that the merge model method gave the best result and, by increasing the number of reference points, the accuracy increased. The DEM produced by the enhanced SRTM (iSRTM) demonstrated noticeably better outcomes than the baseline SRTM DEM, with around 3.56 m RMSE. In this research, STRM-1 DEM is used to determine watershed basin delineation. The topographic map showed significant variations in the level of the Earth's surface, as the heights of the area ranged between 20 m and 120 m. The number of watersheds extracted in the Benban is 132 with different areas, the large area is about 72.14 km 2 , while the small area is about 1.13 km 2 , and between those areas, there are various areas. The largest recorded watershed circumference reached 48.94 km in area (72.14 km 2), while the lowest recorded perimeter is 5.40 km for the area (1.13 km 2). [ABSTRACT FROM AUTHOR]

This study aims to investigate the potential of TanDEM-X DEM and geoinformation technology for mapping and assessing avalanche hazard along the North-South alternative road. This new 433kilometer long transportation corridor connects the northern part (from the Balykchy city) with the southern part (to the JalalAbad city) of Kyrgyzstan, separated by the Fergana Mountain range. The North-South road under construction should become an strategic automobile route between two parts of the country, an alternative to the existing Bishkek-Osh road, which passes through high mountainous regions. The existing road is subject to congestion and closures in natural emergencies, in cases of territorial disputes of neighbor countries, and affected economic development of the country. The road construction in the high mountainous areas requires a detailed study of all types of natural and man-made hazards and risk assessment. There are many road sections under potential hazards of avalanches, landslides and rock falls. Research and mapping of all types of natural hazards requires the use of modern engineering survey technologies, remote sensing data, geographic information systems and field research. Digital Elevation Models and Digital Surface Models have many advantages providing detailed information about the terrain's topography, which is essential for assessing avalanche hazard and risk. The TanDEM-X DEM gives detailed topographic information, including elevation, surface slope, aspect, and curvature used to identify potential avalanche release areas for assessing avalanche risk and implementing mitigation measures. [ABSTRACT FROM AUTHOR]

The transboundary region of the Iishana system in the western Cuvelai Basin, between southern Angola and northern Namibia, is frequently affected by floods at irregular intervals. As a result, the predominantly rural, subsistence farming population has experienced crop failures, human, and economic losses. To date, very little is known about the generation of floods, flood concentration, and stormwater drainage dynamics in this region. In this study, 2D‐hydrodynamic modeling was applied to reconstruct one of the latest major flood events during the rainy season from November 2008 to March 2009 in order to study the runoff behavior and interconnectivity of the Iishana system. The model focused on the eastern part of the Iishana system, which was most affected by floods and flood damage due to the high population density in and around Oshakati, the regional capital. Two main streams were identified noteworthy because they merge and subsequently affect Oshakati. Regarding the simulated flood event water depths vary from 0.1 m to 14 m, with an average of 0.2 m, while water depths above 5 m were attributed to borrow pits. The inundation area ranged up to 1860 km2 and the amount of water left after the rainy season on March 25th, 2009, was determined between 0.116 and 0.547 km3, depending on the amount of evapotranspiration considered in the model. Thus, in the Angolan part of the Iishana system, significantly larger quantities of water are available for longer periods of time during the subsequent dry season, whereas the system in Namibia stores less water, resulting in a shorter water retention period. [ABSTRACT FROM AUTHOR]

The Arctic is experiencing the greatest increase in air temperature on Earth. This significant climatic change is leading to a significant positive trend of increasing wave heights and greater coastal erosion. This in turn effects local economies and ecosystems. Increasing wave energy is one of the main drivers of this alarming trend. However, the data on spatial and temporal patterns of wave heights in the Arctic are either coarse, interpolated or limited to point measurements. The aim of this study is to overcome this shortcoming by using remote sensing data. In this study, the Synthetic Aperture Radar (SAR) satellite TerraSAR-X (TS-X) and TanDEM-X (TD-X) imagery are used to obtain sea state information with a high spatial resolution in Arctic nearshore waters in the Canadian Beaufort Sea. From the entire archive of the TS-X/TD-X StripMap mode with coverage around 30 km × 50 km acquired between 2009 and 2020 around Herschel Island, Qikiqtaruk (HIQ), all the ice-free scenes were processed. The resulting dataset of 175 collocated scenes was used to map the significant wave height ( H s ) and to link spatial and temporal patterns to local coastal processes. Sea state parameters are estimated in raster format with a 600 m step using the empirical algorithm CWAVE_EX. The statistics of the H s were aggregated according to spatial variability, seasonality and wind conditions. The results show that the spatial wave climate is clearly related to the dominant wind regime and seasonality. For instance, the aggregation of all the scenes recorded in July between 2009 and 2020 results in an average of 0.82 m H s , while in October the average H s is almost 0.40 m higher. The analysis by wind direction shows that fetch length and wind speed are likely the most important variables influencing the spatial variability. A larger fetch under NW conditions results in a mean wave height of 0.92 m, while waves generated under ESE conditions are lower at 0.81 m on average. [ABSTRACT FROM AUTHOR]

The German TanDEM-X mission's primary goal is to create a highly accurate and global digital elevation model (DEM) with global accuracies of at least 10 m absolute height error (linear 90% error). This paper provides one-of-a-kind accuracy assessment of the TanDEM-X global DEM. The assessment of the TanDEM-X DEMs over Nallamala Area, Andhra Pradesh is presented through a comparison with SRTM and TanDEM-X DEMs. TanDEM-X has a finer effective spatial (i.e., horizontal) resolution than SRTM, according to analysis, visualization, and elevation scatterplots. As a first step, digital elevation models are compared to the conventional method of GCP (ground control point) elevation. Then, any two DEMs are compared using a computational method such as R programing, which is a statistical technique that selects point locations with equal distance. TanDEM-X lower elevations indicate that the radar signal penetrates deeper. DEMs with differences allowed the identification of issues related to the production methods of the DEMs. A systematic difference in elevations between TanDEM-X 12 and 30 m, and SRTM was observed. The results show a high level of detail and consistency for TanDEM-X data, indicate that the effective horizontal resolution of SRTM is coarser than the nominal 12 and 30 m. These DEMs are then delineated in QSWAT model for stream network generation. QSWAT is a tool used for the validation of digital elevation models (DEMs) in hydrological modeling. It allows for the comparison of the topographic features of the DEM with field survey data to identify discrepancies and improve model accuracy. [ABSTRACT FROM AUTHOR]

Abstract Vegetation canopy height is a relevant proxy for aboveground biomass, carbon stock, and biodiversity. Wall‐to‐wall information of canopy height with high spatial resolution and accuracy is not yet available on large scales. For the globally consistent TanDEM‐X data, simplifications are necessary to estimate canopy height with semi‐empirical models based on polarimetric synthetic aperture radar interferometry (PolInSAR). We trained the semi‐empirical models with sampled GEDI data, because the assumptions behind the application of such simplifications are not always valid for TanDEM‐X. General linear as well as sinc models and empirical parameterizations of these models were applied to estimate the canopy height in tropical landscapes of Sumatra, Indonesia. Airborne laser scanning (ALS) data were consistently used as an independent reference. The general simplified models were compared with the trained empirical versions to assess the potential improvement of the empirical parameterization of the models. The residuals of the different canopy height models were further evaluated in relation to land use and structural information of the vegetation. Our results indicated that the empirical parameters substantially improved the estimation from a root‐mean‐square‐error (RMSE) of 10.3 m (55.8%) to 8.8 m (47.7%), when using the linear model. In contrast, the improvement of the sinc model with empirical parameters was not substantial compared to the general sinc model (7.4 m [40.4%] vs. 6.9 m [37.5%]). A consistent improvement was observed in the linear model, whereas the improvement of the sinc model was dependent on the land‐use type. Structural attributes like the canopy height itself and vegetation cover had a significant effect on the accuracies, with higher and denser vegetation generally resulting in higher residuals. We demonstrate the potential of the combined exploitation of the TanDEM‐X and GEDI missions for a wall‐to‐wall canopy height estimation in a tropical region. This study provides relevant findings for a consistent mapping of vegetation canopy height in tropical landscapes and on large scales with spaceborne laser and SAR data.

The study of ancient irrigation is crucial in the archaeological research of arid regions. It covers a wide range of topics, with the Near East being the focus for decades. However, political instability and limited data have posed challenges to these studies. The primary objective is to establish a standardised method applicable to different arid environments using the Google Earth Engine platform, considering local relief of terrain and seasonal differences in vegetation. This study integrates multispectral data from LANDSAT 5, Sentinel-2, SAR imagery from Sentinel 1, and TanDEM-X (12 m and 30 m) DSMs. Using these datasets, calculations of selected vegetation indices such as the SMTVI and NDVSI, spectral decomposition methods such as TCT and PCA, and topography-based methods such as the MSRM contribute to a comprehensive understanding of landscape irrigation. This paper investigates the influence of modern environmental conditions on the visibility of features like levees and palaeo-channels by testing different methods and parameters. This study aims to identify the most effective approach for each case study and explore the possibility of applying a consistent method across all areas. Optimal results are achieved by combining several methods, adjusting seasonal parameters, and conducting a comparative analysis of visible features.

Google Earth (GE) provides accurate and reliable global high-resolution images and can obtain the coordinates of any point on Earth's surface. A digital elevation model (DEM) is a dataset that quantitatively reflects the elevation of Earth's surface. Although GE and DEMs can be used to obtain the coordinates of any position on Earth, their open-access data can be affected by various factors, thereby inducing undesirable precision variability. Therefore, it is essential to estimate the accuracy of GE and open-source DEMs. In this study, 325 high-precision GPS survey points in 16 regions in China were used to evaluate the horizontal and vertical accuracies of GE and the elevation accuracy of two open-source DEMs. GE had a high horizontal accuracy with a root mean square error (RMSE) of 2.495 m and an error range of 1.090--4.844 m. The elevation accuracy of GE (RMSE = 2.610 m) was lower than those of TanDEM-X (RMSE = 2.055 m) and AW3D30 (RMSE = 2.373 m) DEMs. At the same time, the impacts of slope, aspect, and feature type on the accuracy of these data are studied and analyzed. The results show that the accuracy of control data are closely related to the characteristics of the study area. Overall, these findings indicate that for future studies in China, GE can be used to acquire horizontal data, whereas TanDEM-X and AW3D30 are more suitable for elevation data that have higher precision and provide a reference for relevant research on geographic information. [ABSTRACT FROM AUTHOR]

Site index and stand age are important variables in forestry. Site index describes the growing potential at a given location, expressed as the height that trees can attain at a given age under favorable growing conditions. It is traditionally used to classify forests in terms of future timber yield potential. Stand age is used for the planning of management activities such as thinning and harvest. SI has previously been predicted using remote sensing, but usually relying on either very short time series or repeated ALS acquisitions. In this study, site index and forest stand age were predicted from time series of interferometric TanDEM-X data spanning seven growth seasons in a hemi-boreal forest in Remningstorp, a test site located in southern Sweden. The goal of the study was to see how satellite-based radar time series could be used to estimate site index and stand age. Compared to previous studies, we used a longer time series and applied a penetration depth correction to the phase heights, thereby avoiding the need for calibration using ancillary field or ALS data. The time series consisted of 30 TanDEM-X strip map scenes acquired between 2011 and 2018. Established height development curves were fitted to the time series of TanDEM-X-based top heights. This enabled simultaneous estimation of both age and site index on 91 field plots with a 10 m radius. The RMSE of predicted SI and age were 6.9 m and 38 years for untreated plots when both SI and age were predicted. When predicting SI and the age was known, the RMSE of the predicted SI was 4.0 m. No significant prediction bias was observed for untreated plots, while underestimation of SI and overestimation of age increased with the intensity of treatment. [ABSTRACT FROM AUTHOR]

The estimation of forestry parameters is essential to understanding the three-dimensional structure of forests. In this respect, the potential of X-band synthetic aperture radar (SAR) has been recognized for years. Many studies have been conducted on deriving tree heights with SAR data, but few have paid attention to the effects of the canopy structure. Canopy density plays an important role since it provides information about the vertical distribution of dominant scatterers in the forest. In this study, the position of the scattering phase center (SPC) of interferometric X-band SAR data is investigated with regard to the densest vegetation layer in a deciduous and coniferous forest in Germany by applying a canopy density index from high-resolution airborne laser scanning data. Two different methods defining the densest layer are introduced and compared with the position of the TanDEM-X SPC. The results indicate that the position of the SPC often coincides with the densest layer, with mean differences ranging from −1.6 m to +0.7 m in the deciduous forest and +1.9 m in the coniferous forest. Regarding relative tree heights, the SAR signal on average penetrates up to 15% (3.4 m) of the average tree height in the coniferous forest. In the deciduous forest, the difference increases to 18% (6.2 m) during summer and 24% (8.2 m) during winter. These findings highlight the importance of considering not only tree height but also canopy density when delineating SAR-based forest heights. The vertical structure of the canopy influences the position of the SPC, and incorporating canopy density can improve the accuracy of SAR-derived forest height estimations. [ABSTRACT FROM AUTHOR]

The present study has analyzed dynamics of Gangotri glacier using multiple remote sensing (RS) datasets and ground based observations. Interferometric Synthetic Aperture Radar (InSAR) data pairs from European Remote Sensing satellite (ERS 1/2) tandem pair for spring of 1996, Sentinel-1 SAR pairs and Japanese's Advance Land Observation System (ALOS) PALSAR-2 SAR data for Spring of 2015 were used to derive glacier-surface velocity at seasonal time scale using Differential InSAR (DInSAR) techniques. Bi-static TanDEM-X (Experimental) data was used for the 1st time to estimate glacier surface elevation changes for a period of 22, 44, 88 days during summer of 2012 using InSAR techniques in this study. Annual glacier velocity was also estimated using temporal panchromatic data of LANDSAT-5 (30 m), LANDSAT-7/8 (15 m), Sentinel-2 (10 m) and Indian Remote Sensing Satellite IRS-1C/1D panchromatic (5 m) data during 1998–2019 with feature tracking approach. This study has estimated glacier surface velocity and surface elevation changes for the major parts of Gangotri glacier and its tributary glaciers using medium to high resolution optical and SAR datasets, at annual and seasonal time scale, which is an improvement over earlier studies, wherein snout based glacier recession or only main glacier velocities were reported. The velocity and slope were used to assess glacier-ice thickness distribution using Glabtop-2, slope dependent and laminar flow based methods over the Gangotri group of glaciers. The estimated ice thickness was estimated in the range of 58–550 m for the complete glacier while few small areas in middle & upper regions carry higher thickness of about 607 m. The estimated glacier-ice thickness was found in the range of 58–67 m at the snout region. The estimation was validated using 2014 field measurements from Terrestrial Laser Scanner (TLS) for the first time and correlation was found to be 0.799 at snout of the glacier. [ABSTRACT FROM AUTHOR]

This study compared the vertical accuracy of existing satellite’s Digital Elevation Model (STRM, ASTER) and the latest satellite remote sensing height data set TanDEM-90m. ASTER and STRM have vertical accuracies of ± 20m and ± 16m, respectively. TanDEMX-90m was processed before use, and the RMSE range of 19-20m was confirmed for TANDEM X 90m. The result of the study shows that ASTER DEM performed better than the rest of the global digital elevation models. The SRTM error of 4m between the first and second locations may be due to systematic error due to slightly different versions of SRTM used for processing. The TANDEM X 90m had the same resolution but performed poorer in positional fitness. The study found that digital elevation models have different resolutions and accuracy levels. Tandem-X90m has a resolution of up to 3 meters, while Aster and SRTM have 30 meters. GPS-based field digital terrain models provide higher accuracy, while Tandem-X90m has a 10 cm accuracy. The models cover a limited area, making them suitable for high-resolution elevation data. The data collection methods used by the models, such as SAR sensors, also affect their accuracy and resolution. Based on the above comparative analysis, it can be recommended that the GPS-based field digital terrain model is suitable for high-accuracy and high-resolution elevation data in a limited area. Tandem-X90m is suitable for high-resolution elevation data in a limited area but is expensive. Aster and SRTM are suitable for global-scale studies but have lower accuracy and resolution. [ABSTRACT FROM AUTHOR]

Vegetation canopy height is a relevant proxy for aboveground biomass, carbon stock, and biodiversity. Wall‐to‐wall information of canopy height with high spatial resolution and accuracy is not yet available on large scales. For the globally consistent TanDEM‐X data, simplifications are necessary to estimate canopy height with semi‐empirical models based on polarimetric synthetic aperture radar interferometry (PolInSAR).We trained the semi‐empirical models with sampled GEDI data, because the assumptions behind the application of such simplifications are not always valid for TanDEM‐X. General linear as well as sinc models and empirical parameterizations of these models were applied to estimate the canopy height in tropical landscapes of Sumatra, Indonesia. Airborne laser scanning (ALS) data were consistently used as an independent reference. The general simplified models were compared with the trained empirical versions to assess the potential improvement of the empirical parameterization of the models. The residuals of the different canopy height models were further evaluated in relation to land use and structural information of the vegetation.Our results indicated that the empirical parameters substantially improved the estimation from a root‐mean‐square‐error (RMSE) of 10.3 m (55.8%) to 8.8 m (47.7%), when using the linear model. In contrast, the improvement of the sinc model with empirical parameters was not substantial compared to the general sinc model (7.4 m [40.4%] vs. 6.9 m [37.5%]). A consistent improvement was observed in the linear model, whereas the improvement of the sinc model was dependent on the land‐use type. Structural attributes like the canopy height itself and vegetation cover had a significant effect on the accuracies, with higher and denser vegetation generally resulting in higher residuals.We demonstrate the potential of the combined exploitation of the TanDEM‐X and GEDI missions for a wall‐to‐wall canopy height estimation in a tropical region. This study provides relevant findings for a consistent mapping of vegetation canopy height in tropical landscapes and on large scales with spaceborne laser and SAR data. [ABSTRACT FROM AUTHOR]

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.

TanDEM-X bistatic interferometric system successfully generated a high-precision, high-resolution global digital elevation model (DEM). However, in forested areas, two core problems make it difficult to obtain sub-canopy topography: (1) the penetrability of short-wave signals is limited, and the DEM obtained in dense forest areas contains a significant forest signal, that is, the scattering phase center (SPC) height; and (2) the single-baseline and single-polarization TanDEM-X interferometric synthetic aperture radar (InSAR) data cannot provide sufficient observations to make the existing physical model reversible for estimating the real surface phase, whereas the introduction of optical data makes it difficult to ensure data synchronization and availability of cloud-free data. To overcome these problems in accurately estimating sub-canopy topography from TanDEM-X InSAR data, this study proposes a practical method of sub-canopy topography estimation based on the following innovations: (1) An orthogonal polynomial model was established using TanDEM-X interferometric coherence and slope to estimate the SPC height. Interferometric coherence records forest height and dielectric property information from an InSAR perspective and has spatiotemporal consistency with the InSAR-derived DEM. (2) Introduce Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data to provide more observational information and automatically screen ICESat-2 control points with similar forest and slope conditions in the local area to suppress forest spatial heterogeneity. (3) A weighted least squares criterion was used to solve this model to estimate the SPC height. The results were validated at four test sites using high-precision airborne light detection and ranging (LiDAR) data as a reference. Compared to the InSAR-derived DEM, the accuracy of the sub-canopy topography was improved by nearly 60%, on average. Furthermore, we investigated the necessity of local modeling, confirming the potential of the proposed method for estimating sub-canopy topography by relying only on TanDEM-X and ICESat-2 data.

Declassified CORONA images provide stereo pairs that could be used for preparation of Digital Elevation Models (DEMs) and orthophotos that could be an invaluable resource in accounting historical information. In the present work, KH-9 Hexagon CORONA images of Bilaspur district, Himachal Pradesh, India are used for the generation of DEM and orthophoto using photogrammetric techniques and tools. ASTER DEM and TanDEM-X were used to validate the generated CORONA DEM. The CORONA DEM elevation profiles at three different locations were compared with ASTER DEM and TanDEM-X. These profiles match very closely with each other. The change percentage of elevation is within 1%, between CORONA DEM and ASTER DEM, and TanDEM-X when computed for random 100 points. However, the elevation at the Bhakra dam has submeter level variation when measured using CORONA DEM. According to published reports, the elevation of the Bhakra dam is 487.68 m, while that is estimated to be 487.88 m in CORONA DEM. Furthermore, Landsat-3 MSS (1979) data is used to validate the orthophoto generated from the stereo pairs. The Govind Sagar reservoir in the study area is demarcated using object-based classification and it showed a difference of 75.91 ha between CORONA orthophoto and Landsat-3 satellite images for 1979. The water bodies are extracted in the study area between 1979 and 2010 using the object-based image classification technique. The area of the water reservoir is decreased by − 7.70%, − 4.57%, and − 19.50% between 1979 and 1990, 1990 and 2000, and 2000 and 2010. We found that annual rainfall is decreasing with an increase in temperature which could be the reason for declining reservoir area. This decrease in reservoir area could also be attributed to depleting water resources, enhanced water extraction, and increased sedimentation near the reservoir. The DEM prepared using the CORONA images can be used for the representation of the earlier earth's surface and can be used as a reference for change detection and deformation studies. [ABSTRACT FROM AUTHOR]

Structural diversity is recognized as a complementary aspect of biological diversity and plays a fundamental role in forest management, conservation, and restoration. Hence, the assessment of structural diversity has become a major effort in the primary international processes, dealing with biodiversity and sustainable forest management. Because of prohibitive costs associated with the ground measurements of forest structure, despite their high accuracy, space-borne polarization coherence tomography (PCT) can introduce an alternative approach given its ability to provide a vertical reflectivity profile and spatiotemporal resolutions related to detecting forest structural changes. In this study, for the first time ever, the potential of space-borne PCT was evaluated in a broad-leaved Hyrcanian forest of Iran over 308 circular sample plots with an area of 0.1 ha. Two aspects of horizontal structure diversity, including standard deviation of diameter at breast height ( σ d b h ) and the number of trees (N), were predicted as important characteristics in wood production and biomass estimation. In addition, the performance of prediction algorithms, including multiple linear regression (MLR), k-nearest neighbors (k-NN), random forest (RF), and support vector regression (SVR) were compared. We addressed the issue of temporal decorrelation in space-borne PCT utilizing the single-pass TanDEM-X interferometer. The data were acquired in standard DEM mode with single polarization of HH. Consequently, airborne laser scanning (ALS) was used to estimate initial values of height h v and ground phase φ 0 . The Fourier–Legendre series was used to approximate the relative reflectivity profile of each pixel. To link the relative reflectivity profile averaged within each plot with corresponding ground measurements of σ d b h and N , thirteen geometrical and physical parameters were defined ( P 1 − P 13 ). Leave-one-out cross validation (LOOCV) showed a better performance of k-NN than the other algorithms in predicting σ d b h and N. It resulted in a relative root mean square error (rRMSE) of 32.80%, mean absolute error (MAE) of 4.69 cm, and R2* of 0.25 for σ d b h , whereas only 22% of the variation in N was explained using the PCT algorithm with an rRMSE of 41.56%. This study revealed promising results utilizing TanDEM-X data even though the accuracy is still limited. Hence, an entire assessment of the used framework in characterizing the reflectivity profile and the possible effect of the scale is necessary for future studies. [ABSTRACT FROM AUTHOR]

Forest canopy height estimation is essential for forest management and biomass estimation. In this study, we aimed to evaluate the capacity of TanDEM-X interferometric synthetic aperture radar (InSAR) data to estimate canopy height with the assistance of an external digital terrain model (DTM). A ground-to-volume ratio estimation model was proposed so that the canopy height could be precisely estimated from the random-volume-over-ground (RVoG) model. We also refined the RVoG inversion process with the relationship between the estimated penetration depth (PD) and the phase center height (PCH). The proposed method was tested by TanDEM-X InSAR data acquired over relatively homogenous coniferous forests (Teruel test site) and coniferous as well as broadleaved forests (La Rioja test site) in Spain. Comparing the TanDEM-X-derived height with the LiDAR-derived height at plots of size 50 m × 50 m, the root-mean-square error (RMSE) was 1.71 m (R2 = 0.88) in coniferous forests of Teruel and 1.97 m (R2 = 0.90) in La Rioja. To demonstrate the advantage of the proposed method, existing methods based on ignoring ground scattering contribution, fixing extinction, and assisting with simulated spaceborne LiDAR data were compared. The impacts of penetration and terrain slope on the RVoG inversion were also evaluated. The results show that when a DTM is available, the proposed method has the optimal performance on forest height estimation.

The Nullarbor Plain is a ~200,000 km2 planar karst surface in southern Australia, underlain by Cenozoic shallow‐water limestones. During the Miocene the area was uplifted, and although the plain is generally considered extremely flat, locally, the geomorphology of the Nullarbor Plain retains evidence of earth surface processes across a long, middle Miocene‐to‐present time span. The accessibility of the recent 0.4 arc‐second TanDEM‐X digital elevation model (DEM) by the German Aerospace Centre motivated the search for other possible fine‐scale landforms that would previously have been unresolvable. The analysis of DEM images revealed an enigmatic annular landform with an outer diameter between 1200 m and 1300 m. It consists of a circular elevated rim and a central dome. Its morphology is distinct from other landforms observed on the plain, and cannot be readily explained as a part of known active or inferred/expected processes on the plain (e.g., fluvial, aeolian, karst, tectonic or extra‐terrestrial impact processes). More recent karst processes (dayas formation) overprinting the landform indicates the presence of the annular structure prior to dayas formation. A unique microbial boundstone facies sampled from the bed‐rock of the annular structure supports an interpretation of long‐lived, at least partial retention of a primary depositional structure. Differential carbonate deposition, especially bioherm growth, was a likely origin of the annular topographical expression of the present‐day landform and is comparable to biogenic structures in the modern day deeper Great Barrier Reef. [ABSTRACT FROM AUTHOR]

The study presents the results of interferometric processing and analysis of ALOS-1/2 and Sentinel-1 SAR data acquired over the landslide territory of the Bureya river bank in 2006–2018. The novelty of the study is that, for the first time, the joint analysis of interferometric measurements of the landslide movement with meteorological information and time series of SRTM-X, ALOS-1, and TanDEM-X digital elevation models were performed. The results allowed us to make a conclusion that the Bureya landslide was evolving within the frame of the depression formed by a relict landslide which was stable at least at the beginning of the 21st century. The Bureya reservoir filling in 2003–2009 with a 60 m rise of the water and subsequent seasonal water level oscillations supposedly triggered the landslide activity. The landslide movement in the first decade may be characterized by a stable rate of movement all year around. A steady increase in precipitation level in the second decade, with heavy rainfalls in 2013, 2016, and 2018 led to a prominent acceleration of the landslide rate of movement, especially in warm seasons, with a final landslide in December 2018. [ABSTRACT FROM AUTHOR]

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This study extracted the data of Digital Elevation Model (DEM), tidal channel, and tidal channel density, slope based on TanDEM-X satellite of Ganghwa tidal flat. Monitoring and analysis of the decrease in the area of tidal flats in Korea are of great importance, and by judging the efficiency and accuracy in time and space, satellite data were obtained according to the analysis topic of the tidal flats. Since the west coast occupies a large proportion of domestic tidal flats, Ganghwa-do tidal flats were designated as the scope of the study. The produced materials are provided in the form of GEOTIFF(.tif) or Shape(.shp) files. To utilize the tidal flat data constructed in this way, it can be downloaded from the Environmental Big Data website (www.bigdata-environment.kr), an environmental business big data platform.

The bistatic interferometric coherence is affected by different sources of error, among which volume decorrelation, which quantifies the amount of noise caused by volume scattering mechanisms. This represents a key quantity not only for the performance assessment of interferometric synthetic aperture radar (SAR) products, but also for a large variety of scientific applications, ranging from land cover classification to physical parameters estimation, such as ice structure, forest height, and biomass retrieval. The magnitude of volume decorrelation can be derived from the total interferometric coherence by properly compensating for all other decorrelation sources. Considering that temporal decorrelation can be neglected for a bistatic system such as TanDEM-X, the remaining decorrelation components can be estimated from the SAR scene characteristics and the system parameters. In the scientific community, it is a common practice to approximate the volume decorrelation with the coherence or to compute it by compensating for the signal-to-noise ratio (SNR) decorrelation only, which typically represents the predominant decorrelation component. The aim of this work is to assess the impact of different decorrelation sources in detail and to provide the readers with a practical procedure for a precise computation of the volume decorrelation from TanDEM-X bistatic data. In particular, we concentrate on the two most relevant decorrelation components: the SNR and the quantization components. Regarding the former, we estimate the noise equivalent sigma naught directly from real SAR data and we provide the users with a set of polynomial coefficients for the retrieval of the system noise floor for each operational TanDEM-X StripMap beam used for the generation of the global digital elevation model. These values are then combined with the backscatter for the retrieval of the scene-based SNR and of the corresponding decorrelation. Concerning the latter, we analyze its dependence on the backscatter local statistics and quantization rate and we provide the reader with a set of empirical lookup tables for quantifying its impact on the coherence. Finally, we provide reasonable assumptions for all other remaining decorrelation sources, discussing two application scenarios in the fields of forest mapping and forest height estimation, which demonstrate the added value of the proposed methodology.

Tidal flats are internationally protected areas because of their environmental, geological, and economic value. Nevertheless, these are areas where casualties are common among visitors. To ensure the safety of visitors, efforts must be made to understand the geomorphologic characteristics of tidal flats. It is necessary to use three-dimensional (3-D) topographic data to clearly determine when an exposed tidal flat rapidly disappears as a result of periodic tidal fluctuations. Digital elevation models (DEMs) created using unmanned aerial vehicle (UAV) imaging and commercial photogrammetric software to measure tidal flats are produced using ground control points (GCPs). It is difficult to conduct field surveys and readings of image coordinates that correspond to these because tidal flat areas are difficult to access. The distribution of GCPs affects the accuracy of a DEM because the entire test region cannot be covered during a survey. Without GCPs, the DEM generated with the UAV images and photogrammetric software using the structure from the motion technique probably has nonlinear distortion such as a bowl shape. This article proposes a practical method of least-squares 3-D surface matching with a polynomial model (LS3D-PM) to correct a distorted DEM. For this LS3D-PM matching, a global TanDEM-X DEM was used instead of a field survey to collect the GCPs in a tidal flat. Practical testing was conducted using the proposed method. The root-mean-square error with respect to the height decreased from approximately 1.0 to 0.1 m, and the bowl-effect error was eliminated. With the DEM corrected using the proposed method and the classified orthoimages, the exposed tidal flat changes due to seawater invasion were estimated at different times during flood tide. This method has nontrivial technical values for quick and relatively precise DEM generations at a low cost compared to that of other techniques, particularly in emergency situations such as tsunamis, landslides, and earthquakes.

This article presents the first direct comparison of the sea surface radial velocity (RVL) derived from the two satellite SAR systems Sentinel-1 and TanDEM-X, operating at different frequencies and imaging modes. The RVL is derived from the Doppler centroid (Dc) provided in the Sentinel-1 OCN product and from the along-track interferometric phase of the TanDEM-X. The comparison is carried out using unique opportunistic acquisitions, collocated in space and time, over three different sites located in the Iceland Sea, the Pentland Firth, and the Kattegat Sea. First, it is observed that the RVL derived from both satellites is biased, thus calibration is applied using the land as a reference. The comparison shows that the correlation and the mean bias between the two datasets depend on the differences in acquisition time, incidence angle, and azimuth angle, and on wind and surface velocities. It is found that, given a time difference of $\lesssim$ 20 min, the spatial correlation coefficient is relatively high (between 0.70 and 0.93), which indicates that the two SAR systems observe similar sea surface current fields. The spatial correlation degrades primarily due to increasing time difference and decreasing velocity magnitudes. It is also found that the mean RVL bias increases primarily with the radial wind speed, which suggests that the bias is mainly due to the wave-induced Doppler shift. This article shows that under certain conditions, i.e., similar acquisition geometry and short time delay, a good agreement between the two independently derived RVL is achieved, both in the spatial variation and absolute mean value. This encourages a synergistic use of the sea surface velocity estimated from different C- and X-band SAR systems.

Accurate large-area Digital Terrain Models (DTMs) are crucial for many science applications. Spaceborne Synthetic Aperture Radar (SAR) platforms are often used to create these DTMs as they provide an effective tool to collect surface elevations across a wide extent. However, SAR-derived digital elevation models (DEMs) cannot accurately measure ground elevations in the presence of forests. This work demonstrates an approach to estimate terrain elevations from 12 m TanDEM-X by using a convolutional neural network (CNN) trained with ground elevations from ICESat-2 – a spaceborne laser altimeter. This approach demonstrated the ability to estimate terrain elevations from TanDEM-X DEMs for the greater North Carolina area. The CNN estimated terrain saw an improvement in RMSE from 11.28 m to 4.42 m within the entire area of interest, and a focused improvement in RMSE from 12.78 m to 4.95 m in forested areas when compared to ICESat-2. The CNN model outperformed linear, random forest, and gradient boosted regression models using comparable model inputs. This work combines 12-m TanDEM-X data with ICESat-2 profiles, resulting in a new DTM product with accuracy approaching that of reference elevations obtained from satellite laser altimetry in the southeastern United States. • Investigates use of CNN to create DTM with TanDEM-X and ICESat-2 in North Carolina. • CNN outperformed linear, random forest, and gradient boosted regression models tested. • CNN reduced RMSE from 11.28 m to 4.42 m with convolutional neural network. • Establishes possible framework for creating global DTMs. [ABSTRACT FROM AUTHOR]

Operational canopy height mapping at high resolution remains a challenging task at country-level. Most of the existing state-of-the-art inversion methods propose physically-based schemes which are specifically tuned for local scales. Only few approaches in the literature have attempted to produce country or global scale estimates, mostly by means of data-driven approaches and multi-spectral data sources. In this paper, we propose a robust deep learning approach that exploits single-pass interferometric TanDEM-X data to generate accurate forest height estimates from a single interferometric bistatic acquisition. The model development is driven by considerations on both the final performance and the trustworthiness of the model for large-scale deployment in the context of tropical forests. We train and test our model over the five tropical sites of the AfriSAR 2016 campaign, situated in the West Central state of Gabon, performing spatial cross-validation experiments to test its generalization capability. We define a specific training dataset and input predictors to develop a robust model for country-scale inference, by finding an optimal trade-off between the model performance and the large-scale reliability. The proposed model achieves an overall estimation bias of 0.12 m, a mean absolute error of 3.90 m, a root mean squared error of 5.08 m and a coefficient of determination of 0.77. Finally, we generate a time-tagged country-scale canopy height map of Gabon at 25 m resolution, discussing the potential and challenges of these kinds of products for their application in different scenarios and for the monitoring of forest changes. • Country-scale mapping of canopy height over Gabon, Africa. • Bistatic TanDEM-X InSAR features essential for bio-physical parameter estimation. • State-of-the-art canopy height estimation performance with deep learning. • Generation of calibrated estimates independent of SAR and InSAR geometries. • Novel reliability estimation approach of the country-scale product. [ABSTRACT FROM AUTHOR]

The estimation of forestry parameters is essential to understanding the three-dimensional structure of forests. In this respect, the potential of X-band synthetic aperture radar (SAR) has been recognized for years. Many studies have been conducted on deriving tree heights with SAR data, but few have paid attention to the effects of the canopy structure. Canopy density plays an important role since it provides information about the vertical distribution of dominant scatterers in the forest. In this study, the position of the scattering phase center (SPC) of interferometric X-band SAR data is investigated with regard to the densest vegetation layer in a deciduous and coniferous forest in Germany by applying a canopy density index from high-resolution airborne laser scanning data. Two different methods defining the densest layer are introduced and compared with the position of the TanDEM-X SPC. The results indicate that the position of the SPC often coincides with the densest layer, with mean differences ranging from −1.6 m to +0.7 m in the deciduous forest and +1.9 m in the coniferous forest. Regarding relative tree heights, the SAR signal on average penetrates up to 15% (3.4 m) of the average tree height in the coniferous forest. In the deciduous forest, the difference increases to 18% (6.2 m) during summer and 24% (8.2 m) during winter. These findings highlight the importance of considering not only tree height but also canopy density when delineating SAR-based forest heights. The vertical structure of the canopy influences the position of the SPC, and incorporating canopy density can improve the accuracy of SAR-derived forest height estimations.

This study provides mapping of glacial and fluvial geomorphology in the Shaluli Shan region on the eastern margin of the south-eastern Tibetan Plateau. Based on TanDEM-X 12 m elevation data and GoogleEarth imagery, glacial valleys, ice-marginal moraines, glacial lineations, scoured terrain and fluvial terraces were mapped. Covering around 11,000 km2, this map is the first for this region to display geomorphology at a spatial resolution of 0.4 arcsec (= c. 11 m) and to include fluvial terraces. Its glacial landform distribution is largely consistent with previous mapping. The substantially higher level of detail in this study is reflected in an approximately tenfold number and smaller median sizes of individual landforms such as moraines and glacial lineations. These results underscore the importance of high-resolution DEM data such as TanDEM-X for the identification of glacial and fluvial geomorphology. The map presented here will be used for detailed paleoglacial reconstructions and landscape evolution studies combining both glacial and fluvial landforms.

The TanDEM-X synthetic aperture radar (SAR) system allows for the recording of bistatic interferometric SAR (InSAR) acquisitions, which provide additional information to the common amplitude images acquired by monostatic SAR systems. More concretely, the volume decorrelation factor, which can be derived from the bistatic interferometric coherence, is a reliable indicator of the presence of vegetation and it was used as main input feature for the generation of the global TanDEM-X forest/non-forest map, by means of a clustering algorithm. In this work, we investigate the capabilities of deep Convolutional Neural Networks (CNNs) for mapping tropical forests at large-scale using TanDEM-X InSAR data. For this purpose, we rely on a U-Net architecture, which takes as input a set of feature maps selected on the basis of previous preparatory works. Moreover, we design an ad hoc training strategy, aimed at developing a robust model for global mapping purposes, which has to properly manage the large variety of different acquisition geometries characterizing the TanDEM-X global data set. In addition to detecting forest/non-forest areas, the CNN has also been trained to detect water surfaces, which are typically characterized by low values of coherence. By applying the proposed method on single TanDEM-X images, we achieved a significant performance improvement with respect to the baseline clustering approach, with an average F-score increase of 0.13. We then applied such a model for mapping the entire Amazon rainforest, as well as the other tropical forests in Central Africa and South-East Asia, in order to test its robustness and generalization capabilities, and we observed that forests are typically well detected as contour closed regions and that water classification is reliable, too. Finally, the generated maps show a great potential for mapping temporal changes occurring over forested areas and can be used for generating large-scale maps of deforestation. [ABSTRACT FROM AUTHOR]

Glaciers and glacial lakes in the Bosula Mountain Range need special attention, because their instability may cause disastrous consequences to the downstream settlements and the Sichuan-Tibet Road. The latter is a pivotal traffic line in the Southeast Tibetan Plateau. In order to investigate the state of glaciers and glacial lakes in the Bosula Mountain Range, we estimated the changes in glacier/glacial lake boundaries, glacier surface elevation, and glacier flow velocity between 2000 and 2021 based on multisource remote sensing data. Our results showed that, from the period 2000–2013 to the period 2013–2021, the average shrinking rate of glacier area increased from 0.99 km2/a to 1.74 km2/a, and the average expanding rate of glacial lake area increased from 0.04 km2/a to 0.06 km2/a. From the period 1990–2011 to the period 2015–2019, the average thinning rate of glaciers increased from 0.83 m/a to 1.58 m/a. These results indicate the Bosula Mountain Range is one of the fastest melting glacierized regions in the High Mountain Asia, and the factors that account for this may include quick temperature rise, abundant summer rainfall, and thin debris cover. In spite of strong ice melting, the observed changes in glacier boundaries, surface elevation, and flow velocity show no sign of surge activity, and the frequency of glacier lake outburst has not increased since 1989. Currently, three proglacial lakes that expanded quickly during 2000–2021 are now prominent hazards. They are directly threatened by accidental ice calving and ice avalanche, and their outburst could cause considerable damage to the downstream settlements and the Sichuan-Tibet Road. [ABSTRACT FROM AUTHOR]

The TanDEM-X DEM is a valuable data source for estimating glacier mass balance. However, the accuracy of TanDEM-X elevation over glaciers can be affected by microwave penetration and phase decorrelation. To investigate the bias of TanDEM-X DEMs of glaciers on the Tibetan Plateau, these DEMs were subtracted from SPOT-6 DEMs obtained around the same time at two study sites. The average bias over the studied glacier areas in West Kunlun (175.0 km2) was 2.106 ± 0.012 m in April 2014, and it was 1.523 ± 0.011 m in Geladandong (228.8 km2) in October 2013. By combining backscatter coefficients and interferometric coherence maps, we found surface decorrelation and baseline decorrelation can cause obvious bias in addition to microwave penetration. If the optical/laser data and winter TanDEM-X data were used as new and historic elevation sources for mass-balance measurements over an arbitrary observation period of 10 years, the glacier mass loss rates in West Kunlun and Geladandong would be potentially underestimated by 0.218 ± 0.016 and 0.158 ± 0.011 m w.e. a−1, respectively. The impact is therefore significant, and users should carefully treat the bias of TanDEM-X DEMs when retrieving a geodetic glacier mass balance.

Landslides pose a threat to property both in the populated and cultivated areas of the Gerecse Hills (Hungary). The currently available landslide inventory database holds the records from many sites in the area, but the database is out-of-date. Here we address the problem of revising the National Landslides Cadastre landslide inventory database by creating a landslide susceptibility map with a multivariate model based on likelihood ratio functions. The model is applied to the TanDEM-X DEM (0.4″ res.), the current landslide inventory of the area, and data acquired from geological maps. By comparing the distributions of four variables in the landslide and non-landslide area with grid computation methods, the model yields landslide susceptibility estimates for the study area. The estimations show to what extent a certain area is similar to the sample areas, therefore, its likelihood to be affected by landslides in the future. The accuracy of the model predictions was checked in the field and compared to the results of our previous study using the SRTM-1 DEM for a similar analysis. The model gave accurate estimates when certain correction measures were applied to the input datasets. The limitations of the model, the input datasets, and the suggested correction measures are also discussed.

Synthetic aperture radar (SAR) backscatter based above-ground biomass (AGB) estimates are limited by the saturation of the backscatter-AGB curve. This work explores the potential of combining backscatter with polarimetric SAR interferometry (PolInSAR) estimated forest stand height for improved AGB estimation. The models combining L-band backscatter and TanDEM-X height are compared with established backscatter based models. The models are also temporally cross-validated, i.e., trained on one acquisition date and validated for other dates. It is observed that with the input of height, the combined models perform significantly better than backscatter based models, with an improvement in root mean square error (RMSE) between 19% and 46%. The model utilizing HV-polarized backscatter and TanDEM-X PolInSAR height provide the best case AGB inversion with an R2 = 0.78 and an RMSE of 27.1 Mg/ha or 22% of mean AGB. The results demonstrate the potential of the synergistic combination of L-band PolSAR (backscatter) and X-band PolInSAR (height) products for AGB mapping over a tropical forest range in India.

The hybrid interferometric synthetic aperture radar system is a combination of an along-track configuration and cross-track configuration. Based on linear ocean wave theory, an ocean wave inversion algorithm for a hybrid interferometric synthetic aperture radar system is proposed in this work. Using the interferometric synthetic aperture radar images acquired by the TerraSAR-X and TanDEM-X satellites and the interferometric synthetic aperture radar images acquired by an airborne interferometric radar altimeter with a certain degree of squint, the profile of ocean waves and the corresponding orbital velocities were retrieved by combining the new inversion algorithm with the cross-spectra. Meanwhile, key parameters of ocean waves, such as the significant wave height, significant wave orbital velocity, propagation direction, and wavelength of the dominant waves, were also extracted from the ocean wave spectra retrieved in this study. In order to evaluate the reliability of the new inversion algorithm, the retrieved significant wave heights were compared with those provided by the European Centre for Medium-Range Weather Forecasts and measured by a Global Navigation Satellite System buoy. The results showed that for the ocean waves retrieved from the spaceborne hybrid interferometric synthetic aperture radar images, the differences between the retrieved significant wave heights of the four subareas selected in this paper and those provided by European Centre for Medium-Range Weather Forecasts were approximately 0.01, –0.17, –0.55, and –0.37 m, respectively, and for the ocean waves retrieved from the airborne interferometric radar altimeter images, the differences between the retrieved significant wave heights corresponding to the M920 and M3120 images used in this paper and those measured by the Global Navigation Satellite System buoy were approximately –0.05 and –0.09, respectively. Therefore, the method proposed in this work could retrieve the ocean wave spectra well when the velocity bunching had a small influence; however, as the nonlinear influence of the velocity bunching increased, the difference between the significant wave heights retrieved using this method and provided by the European Centre for Medium-Range Weather Forecasts also increased. [ABSTRACT FROM AUTHOR]