Towards Sentinel-1 SAR Analysis-Ready Data: A Best Practices Assessment on Preparing Backscatter Data for the Cube

Truckenbrodt, J; Freemantle, Terri; Williams, C. L.; Jones, T. W.; Small, David; Dubois, Clémence; Thiel, Christian; Rossi, Cristian; Syriou, A.; Giuliani, Gaston

doi:10.3390/data4030093

Cited by 59 publications

(39 citation statements)

References 27 publications

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“…Terrain normalization was better served by high-resolution DEMs (i.e., TDX20, ALS DEM, AW3D at Spanish site), in agreement with prior research [17,29] (SRTM-1arcsec and AW3D outperformed SRTM-3arcsec and TanDEM-X 90m). Higher resolution DEMs have reduced vertical uncertainties (under 5 m over sloping terrain for TDX12.5 and AW3D) when compared to the SRTM DEM (Table 1), which may have contributed to reducing IOR values.…”

Section: Discussionsupporting

confidence: 89%

“…All SAR images were resampled to a pixel size matching the different DEMs used (see Section 3.2). For the Romanian site, 21 images acquired between 2016/12/30 and 2017/02/06 from three relative orbits (7,29,131) were used. For the Spanish site, 18 images acquired between 2018/08/21 and 2018/10/08 from relative orbits 1 and 81 were used.…”

Section: Study Area and Satellite Datamentioning

confidence: 99%

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Investigating the Impact of Digital Elevation Models on Sentinel-1 Backscatter and Coherence Observations

et al. 2020

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Spaceborne remote sensing can track ecosystems changes thanks to continuous and systematic coverage at short revisit intervals. Active remote sensing from synthetic aperture radar (SAR) sensors allows day and night imaging as they are not affected by cloud cover and solar illumination and can capture unique information about its targets. However, SAR observations are affected by the coupled effect of viewing geometry and terrain topography. The study aims to assess the impact of global digital elevation models (DEMs) on the normalization of Sentinel-1 backscattered intensity and interferometric coherence. For each DEM, we analyzed the difference between orbit tracks, the difference with results obtained with a high-resolution local DEM, and the impact on land cover classification. Tests were carried out at two sites located in mountainous regions in Romania and Spain using the SRTM (Shuttle Radar Topography Mission, 30 m), AW3D (ALOS (Advanced Land Observation Satellite) World 3D, 30 m), TanDEM-X (12.5, 30, 90 m), and Spain national ALS (aerial laser scanning) based DEM (5 m resolution). The TanDEM-X DEM was the global DEM most suitable for topographic normalization, since it provided the smallest differences between orbital tracks, up to 3.5 dB smaller than with other DEMs for peak landform, and 1.4–1.9 dB for pit and valley landforms.

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Section: Discussionsupporting

confidence: 89%

Section: Study Area and Satellite Datamentioning

confidence: 99%

Investigating the Impact of Digital Elevation Models on Sentinel-1 Backscatter and Coherence Observations

et al. 2020

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“…A possible alternative to overcome the reduced availability of images would be the combination of the two collections. Another solution would be the use of the Sentinel-1 mission radar, which is equipped with a Synthetic Aperture Radar (SAR) that collects data regardless of the weather conditions [103][104][105] and improves the detection of vegetation change in the presence of high cloud cover. Additionally, a further alternative for cloud and cloud shadow detection and masking in Sentinel-2 images would be the use of machine learning [106] and deep learning approaches [107].…”

Section: Discussionmentioning

confidence: 99%

Monitoring Wildfires in the Northeastern Peruvian Amazon Using Landsat-8 and Sentinel-2 Imagery in the GEE Platform

Castillo

Cayo

Almeida

et al. 2020

IJGI

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During the latest decades, the Amazon has experienced a great loss of vegetation cover, in many cases as a direct consequence of wildfires, which became a problem at local, national, and global scales, leading to economic, social, and environmental impacts. Hence, this study is committed to developing a routine for monitoring fires in the vegetation cover relying on recent multitemporal data (2017–2019) of Landsat-8 and Sentinel-2 imagery using the cloud-based Google Earth Engine (GEE) platform. In order to assess the burnt areas (BA), spectral indices were employed, such as the Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2), and Mid-Infrared Burn Index (MIRBI). All these indices were applied for BA assessment according to appropriate thresholds. Additionally, to reduce confusion between burnt areas and other land cover classes, further indices were used, like those considering the temporal differences between pre and post-fire conditions: differential Mid-Infrared Burn Index (dMIRBI), differential Normalized Burn Ratio (dNBR), differential Normalized Burn Ratio 2 (dNBR2), and differential Near-Infrared (dNIR). The calculated BA by Sentinel-2 was larger during the three-year investigation span (16.55, 78.50, and 67.19 km2) and of greater detail (detected small areas) than the BA extracted by Landsat-8 (16.39, 6.24, and 32.93 km2). The routine for monitoring wildfires presented in this work is based on a sequence of decision rules. This enables the detection and monitoring of burnt vegetation cover and has been originally applied to an experiment in the northeastern Peruvian Amazon. The results obtained by the two satellites imagery are compared in terms of accuracy metrics and level of detail (size of BA patches). The accuracy for Landsat-8 and Sentinel-2 in 2017, 2018, and 2019 varied from 82.7–91.4% to 94.5–98.5%, respectively.

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“…It is implemented by the Sentinel Toolbox and allows a subsequent processing in Python and JavaScript languages [7]. Truckenbrodt et al [8] analysed the capability of possible software solutions like the Sentinel Application Platform (SNAP) and GAMMA for the automatic production of Sentinel-1 ground range detected ARD products. He concluded that SNAP is a convenient and user-friendly graphical user interface and additionally, it is open source.…”

Section: Necessity For Analysis Ready Datamentioning

confidence: 99%

Multi-Source and Multi-Temporal Image Fusion on Hypercomplex Bases

et al. 2020

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This article spanned a new, consistent framework for production, archiving, and provision of analysis ready data (ARD) from multi-source and multi-temporal satellite acquisitions and an subsequent image fusion. The core of the image fusion was an orthogonal transform of the reflectance channels from optical sensors on hypercomplex bases delivered in Kennaugh-like elements, which are well-known from polarimetric radar. In this way, SAR and Optics could be fused to one image data set sharing the characteristics of both: the sharpness of Optics and the texture of SAR. The special properties of Kennaugh elements regarding their scaling—linear, logarithmic, normalized—applied likewise to the new elements and guaranteed their robustness towards noise, radiometric sub-sampling, and therewith data compression. This study combined Sentinel-1 and Sentinel-2 on an Octonion basis as well as Sentinel-2 and ALOS-PALSAR-2 on a Sedenion basis. The validation using signatures of typical land cover classes showed that the efficient archiving in 4 bit images still guaranteed an accuracy over 90% in the class assignment. Due to the stability of the resulting class signatures, the fuzziness to be caught by Machine Learning Algorithms was minimized at the same time. Thus, this methodology was predestined to act as new standard for ARD remote sensing data with an subsequent image fusion processed in so-called data cubes.

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Towards Sentinel-1 SAR Analysis-Ready Data: A Best Practices Assessment on Preparing Backscatter Data for the Cube

Cited by 59 publications

References 27 publications

Investigating the Impact of Digital Elevation Models on Sentinel-1 Backscatter and Coherence Observations

Investigating the Impact of Digital Elevation Models on Sentinel-1 Backscatter and Coherence Observations

Monitoring Wildfires in the Northeastern Peruvian Amazon Using Landsat-8 and Sentinel-2 Imagery in the GEE Platform

Multi-Source and Multi-Temporal Image Fusion on Hypercomplex Bases

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