Journal articles on the topic 'Sensor RADAR SAR X band'
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1
Mulas, M., M. Petitta, A. Corsini, S. Schneiderbauer, F. V. Mair, and C. Iasio. "Long-term monitoring of a deep-seated, slow-moving landslide by mean of C-band and X-band advanced interferometric products: the Corvara in Badia case study (Dolomites, Italy)." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 29, 2015): 827–29. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-827-2015.
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The availability of data from various Synthetic Aperture Radar (SAR) operating in X-Band and C-Band acquired in the last decades enables to monitor slopes affected by landslides. The ASI-founded project ‘LAWINA’ (2010 – 2012) aimed at the improvement of SAR – based monitoring techniques as well as at the integration of SAR data with data stemming from other sensors. Test case area of LAWINA has been a slow-moving landslide located up-stream of Corvara in Badia village in the Dolomites, Italy. Within the scope of the project different time-series obtained through 35 Envisat2, 40 Radarsat-1 and 46 Cosmo-SkyMed covering this test area have been processed in order to explore the potentials to analyse historical and near real time landslide dynamics. The SAR data are characterized by various geometric and temporal resolutions having been acquired by 3 sensors operating at different bands in different periods between 2003 and 2011. TeleRilevamento Europa (TRE) exploited these data in order to retrive displacement timeseries applying its proprietary SqueeSAR algorithm. After re-projecting Envisat-2 and Radarsat datasets according to the CSK Line Of Sight a comparison of displacements recorded by each sensor has been possible. For this purpose, we have selected areas characterized by the presence of Persistent Scatterers or Diffused Scatterers from at least two datasets. This multi-sensor approach allowed determining the slope displacement tracking during 8 years. Even though the different time series are not formally integrated each other, the result is accurate enough to allow the evaluation of the landslide’s behaviour and trend over several years.
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Wiig, Frances, Michael Harrower, Alexander Braun, Smiti Nathan, Joseph Lehner, Katie Simon, Jennie Sturm, et al. "Mapping a Subsurface Water Channel with X-Band and C-Band Synthetic Aperture Radar at the Iron Age Archaeological Site of ‘Uqdat al-Bakrah (Safah), Oman." Geosciences 8, no. 9 (September 5, 2018): 334. http://dx.doi.org/10.3390/geosciences8090334.
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Subsurface imaging in arid regions is a well-known application of satellite Synthetic Aperture Radar (SAR). Archaeological prospection has often focused on L-band SAR sensors, given the ability of longer wavelengths to penetrate more deeply into sand. In contrast, this study demonstrates capabilities of shorter-wavelength, but higher spatial resolution, C-band and X-band SAR sensors in archaeological subsurface imaging at the site of ‘Uqdat al-Bakrah (Safah), Oman. Despite having varying parameters and acquisitions, both the X-band and C-band images analyzed were able to identify a subsurface paleo-channel that is not visible on the ground surface. This feature was first identified through Ground Penetrating Radar (GPR) survey, then recognized in the SAR imagery and further verified by test excavations. Both the GPR and the excavations reveal the base of the paleo-channel at a depth of 0.6 m–0.7 m. Hence, both X-band and C-band wavelengths are appropriate for subsurface archaeological prospection in suitable (dry silt and sand) conditions with specific acquisition parameters. Moreover, these results offer important new insights into the paleo-environmental context of ancient metal-working at ‘Uqdat al-Bakrah and demonstrate surface water flow roughly contemporary with the site’s occupation.
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Marzano, F. S., S. Mori, M. Chini, L. Pulvirenti, N. Pierdicca, M. Montopoli, and J. A. Weinman. "Potential of high-resolution detection and retrieval of precipitation fields from X-band spaceborne synthetic aperture radar over land." Hydrology and Earth System Sciences 15, no. 3 (March 11, 2011): 859–75. http://dx.doi.org/10.5194/hess-15-859-2011.
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Abstract. X-band Synthetic Aperture Radars (X-SARs), able to image the Earth's surface at metric resolution, may provide a unique opportunity to measure rainfall over land with spatial resolution of about few hundred meters, due to the atmospheric moving-target degradation effects. This capability has become very appealing due to the recent launch of several X-SAR satellites, even though several remote sensing issues are still open. This work is devoted to: (i) explore the potential of X-band high-resolution detection and retrieval of rainfall fields from space using X-SAR signal backscattering amplitude and interferometric phase; (ii) evaluate the effects of spatial resolution degradation by precipitation and inhomogeneous beam filling when comparing to other satellite-based sensors. Our X-SAR analysis of precipitation effects has been carried out using both a TerraSAR-X (TSX) case study of Hurricane "Gustav" in 2008 over Mississippi (USA) and a COSMO-SkyMed (CSK) X-SAR case study of orographic rainfall over Central Italy in 2009. For the TSX case study the near-surface rain rate has been retrieved from the normalized radar cross section by means of a modified regression empirical algorithm (MREA). A relatively simple method to account for the geometric effect of X-SAR observation on estimated rainfall rate and first-order volumetric effects has been developed and applied. The TSX-retrieved rain fields have been compared to those estimated from the Next Generation Weather Radar (NEXRAD) in Mobile (AL, USA). The rainfall detection capability of X-SAR has been tested on the CSK case study using the repeat-pass coherence response and qualitatively comparing its signature with ground-based Mt. Midia C-band radar in central Italy. A numerical simulator to represent the effect of the spatial resolution and the antenna pattern of TRMM satellite Precipitation Radar (PR) and Microwave Imager (TMI), using high-resolution TSX-retrieved rain images, has been also set up in order to evaluate the rainfall beam filling phenomenon. As expected, the spatial average can modify the statistics of the high-resolution precipitation fields, strongly reducing its dynamics in a way non-linearly dependent on the rain rate local average value.
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Marzano, F. S., S. Mori, M. Chini, L. Pulvirenti, N. Pierdicca, M. Montopoli, and J. A. Weinman. "Potential of high-resolution detection and retrieval of precipitation fields from X-band spaceborne Synthetic Aperture Radar over land." Hydrology and Earth System Sciences Discussions 7, no. 5 (September 29, 2010): 7451–84. http://dx.doi.org/10.5194/hessd-7-7451-2010.
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Abstract. X-band Synthetic Aperture Radars (X-SARs), able to image the Earth's surface at metric resolution, may provide a unique opportunity to measure rainfall over land with spatial resolution of about few hundred meters, due to the atmospheric moving-target degradation effects. This capability has become very appealing due to the recent launch of several X-SAR satellites, even though several remote sensing issues are still open. This work is devoted to: (i) explore the potential of X-band high-resolution detection and retrieval of rainfall fields from space using X-SAR signal backscattering amplitude and interferometric phase; (ii) evaluate the effects of spatial resolution degradation by precipitation and inhomogeneous beam filling when comparing to other satellite-based sensors. Our X-SAR analysis of precipitation effects has been carried out using both a TerraSAR-X (TSX) case study of Hurricane "Gustav" in 2008 over Mississippi (USA) and a COSMO-SkyMed (CSK) X-SAR case study of orographic rainfall over Central Italy in 2009. For the TSX case study the near-surface rain rate has been retrieved from the normalized radar cross section by means of a modified regression empirical algorithm (MREA). A relatively simple method to account for the geometric effect of X-SAR observation on estimated rainfall rate and first-order volumetric effects has been developed and applied. The TSX-retrieved rain fields have been compared to those estimated from the Next Generation Weather Radar (NEXRAD) in Mobile (AL, USA). The rainfall detection capability of X-SAR has been tested on the CSK case study using the repeat-pass coherence response and qualitatively comparing its signature with ground-based Mt. Midia C-band radar in central Italy. A numerical simulator to represent the effect of the spatial resolution and the antenna pattern of TRMM satellite Precipitation Radar (PR) and Microwave Imager (TMI), using high-resolution TSX-retrieved rain images, has been also set up in order to evaluate the rainfall beam filling phenomenon. As expected, the spatial average can modify the statistics of the high-resolution precipitation fields, strongly reducing its dynamics in a way non-linearly dependent on the rainrate local average value.
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Arnieri, Emilio, Luigi Boccia, Giandomenico Amendola, Srdjan Glisic, Chun-Xu Mao, Steven (Shichang) Gao, Tobias Rommel, et al. "Channel characterization of a dual-band dual-polarized SAR with digital beamforming." International Journal of Microwave and Wireless Technologies 12, no. 6 (June 1, 2020): 477–86. http://dx.doi.org/10.1017/s175907872000063x.
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AbstractThis paper presents the integration and channel characterization of a highly integrated dual-band digital beamforming space-borne synthetic aperture radar (SAR) receiver. The proposed SAR sensor is a low-cost, lightweight, low-power consumption, and dual-band (X/Ka) dual-polarized module ready for the next-generation space-borne SAR missions. In previous works, by the authors, the design and experimental characterization of each sub-system was already presented and discussed. This work expands upon the previous characterization by providing an exhaustive experimental assessment of the fully integrated system. As it will be shown, the proposed tests were used to validate all the instrument channels in a set-up where the SAR sensor was illuminated by an external source minim the ground reflected waves. Test results demonstrate how the system channels are properly operating allowing the reception of the input signals and their processing in the digital domain. The possibility to easily implement a calibration procedure has also been validated to equalize, in the digital domain, the unavoidable amplitude differences between the different channels.
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Darmawan, Soni, Ita Carolita, Rika Hernawati, Dede Dirgahayu, Agustan, Didin Agustian Permadi, Dewi Kania Sari, Widya Suryadini, Dhimas Wiratmoko, and Yohanes Kunto. "The Potential Scattering Model for Oil Palm Phenology Based on Spaceborne X-, C-, and L-Band Polarimetric SAR Imaging." Journal of Sensors 2021 (March 6, 2021): 1–14. http://dx.doi.org/10.1155/2021/6625774.
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Information about oil palm phenology is required for oil palm plantation management, but using spaceborne polarimetric radar imagery remains challenging. However, spaceborne polarimetric radar on X-, C-, and L-band is promising on structure vegetation and cloud area. This study investigates the scattering model of oil palm phenology based on spaceborne X-, C-, and L-band polarimetric Synthetic Aperture Radar (SAR) imaging. The X-, C-, and L-band polarimetric SAR are derived from spaceborne of TerraSAR-X, Sentinel-1A, and ALOS PALSAR 2. Study area is located in oil palm plantations, Asahan District, North Sumatra, Indonesia. The methodology includes data collection, preprocessing, radiometric calibration, speckle filtering, terrain correction, extraction of scattering value, and development of scattering model of oil palm phenology. The results showed different scattering characteristics for the X-, C-, and L-band polarimetric SAR of oil palm for age and found the potential of the scattering model for oil palm phenology based on the X-band on HH polarization that showed a nonlinear model with R 2 = 0.65 . The C-band on VH and VV polarization showed a nonlinear model with R 2 = 0.56 and R 2 = 0.89 . The L-band on HV and HH polarization showed a logarithmic model with R 2 = 0.50 and R 2 = 0.51 . In this case, the most potential of the scattering model of oil palm phenology based on R 2 is using C-band on VV polarization. However, the scattering model based on X-, C-, and L-band is potentially to be used and applied to identify the phenology of oil palm in Indonesia, which is the main parameter in yield estimation. For the future phenology model needs to improve accuracy by integrating multisensors, including different wavelengths on optical and microwave sensors and more in situ data.
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Petrushevsky, Naomi, Andrea Monti Guarnieri, Marco Manzoni, Claudio Prati, and Stefano Tebaldini. "An Operational Processing Framework for Spaceborne SAR Formations." Remote Sensing 15, no. 6 (March 18, 2023): 1644. http://dx.doi.org/10.3390/rs15061644.
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The paper proposes a flexible and efficient wavenumber domain processing scheme suited for close formations of low earth orbiting (LEO) synthetic aperture radar (SAR) sensors hosted on micro-satellites or CubeSats. Such systems aim to generate a high-resolution image by combining data acquired by each sensor with a low pulse repetition frequency (PRF). This is usually performed by first merging the different channels in the wavenumber domain, followed by bulk focusing. In this paper, we reverse this paradigm by first upsampling and focusing each acquisition and then combining the focused images to form a high-resolution, unambiguous image. Such a procedure is suited to estimate and mitigate artifacts generated by incorrect positioning of the sensors. An efficient wave–number method is proposed to focus data by adequately coping with the orbit curvature. Two implementations are provided with different quality/efficiency. The image quality in phase preservation, resolution, sidelobes, and ambiguities suppression is evaluated by simulating both point and distributed scatterers. Finally, a demonstration of the capability to compensate for ambiguities due to a small across-track baseline between sensors is provided by simulating a realistic X-band multi-sensor acquisition starting from a stack of COSMO-SkyMed images.
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Rott, Helmut. "Advances in interferometric synthetic aperture radar (InSAR) in earth system science." Progress in Physical Geography: Earth and Environment 33, no. 6 (October 12, 2009): 769–91. http://dx.doi.org/10.1177/0309133309350263.
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During recent years, synthetic aperture radar (SAR) interferometry (InSAR) has become an important tool for precise measurements of the earth’s surface topography and deformation. This paper presents an overview on recent developments in InSAR applications, with emphasis on the use of satellite-borne sensors for applications in geoscience, topographic mapping, natural hazard monitoring and environmental research. InSAR measurement principles are briefly introduced. Recent results on the use of repeat-pass interferometry for mapping seismic and volcanic deformation, monitoring landslides and subsidence, and mapping glacier motion are described. Other InSAR applications introduced in the paper are: topographic mapping, retrieval of biogeophysical parameters on land surfaces, and measurements of water currents. Examples of interferometric products are shown for satellite-borne SAR systems operating at X-band, C-band and L-band radar frequencies. An outlook is provided on upcoming SAR systems which will spur further advances in InSAR techniques and applications.
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Notti, D., J. C. Davalillo, G. Herrera, and O. Mora. "Assessment of the performance of X-band satellite radar data for landslide mapping and monitoring: Upper Tena Valley case study." Natural Hazards and Earth System Sciences 10, no. 9 (September 7, 2010): 1865–75. http://dx.doi.org/10.5194/nhess-10-1865-2010.
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Abstract. The aim of this work is to analyse the advantages and disadvantages of using the new X-band SAR data acquired by TerraSAR-X sensors for landslides mapping. This dataset has been processed using a Persistent Scatterer Interferometry technique over the Upper Tena Valley (Central Pyrenees, Spain). In the first section, the geological and geomorphological setting of the study area is introduced, focusing on the description of the landslide inventory. Then the Stable Point Network technique is briefly described, followed by the assessment of the performance of the X-band SAR dataset. In this context, we present first a model to predict the distribution of Persistent Scatterers based on the slope geometry and the land use information, which has then been validated with X-band data results. On a second stage, we have assessed the performance of X-band dataset to detect and monitor mapped landslides. Finally some illustrative case studies are shown demonstrating the potential of using X-band SAR data not only for landslide mapping but also to detect and monitor deformations affecting human infrastructures.
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Karimzadeh, Sadra, and Masashi Matsuoka. "Remote Sensing X-Band SAR Data for Land Subsidence and Pavement Monitoring." Sensors 20, no. 17 (August 22, 2020): 4751. http://dx.doi.org/10.3390/s20174751.
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In this study, we monitor pavement and land subsidence in Tabriz city in NW Iran using X-band synthetic aperture radar (SAR) sensor of Cosmo-SkyMed (CSK) satellites (2017–2018). Fifteen CSK images with a revisit interval of ~30 days have been used. Because of traffic jams, usually cars on streets do not allow pure backscattering measurements of pavements. Thus, the major paved areas (e.g., streets, etc.) of the city are extracted from a minimum-based stacking model of high resolution (HR) SAR images. The technique can be used profitably to reduce the negative impacts of the presence of traffic jams and estimate the possible quality of pavement in the HR SAR images in which the results can be compared by in-situ road roughness measurements. In addition, a time series small baseline subset (SBAS) interferometric SAR (InSAR) analysis is applied for the acquired HR CSK images. The SBAS InSAR results show land subsidence in some parts of the city. The mean rate of line-of-sight (LOS) subsidence is 20 mm/year in district two of the city, which was confirmed by field surveying and mean vertical velocity of Sentinel-1 dataset. The SBAS InSAR results also show that 1.4 km2 of buildings and 65 km of pavement are at an immediate risk of land subsidence.
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Zhang, P., and Z. Zhao. "EVALUATION OF DATA APPLICABILITY FOR D-INSAR IN AREAS COVERED BY ABUNDANT VEGETATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 2277–81. http://dx.doi.org/10.5194/isprs-archives-xlii-3-2277-2018.
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In the past few years, the frequent geological disasters have caused enormous casualties and economic losses. Therefore, D-InSAR (differential interferometry synthetic aperture radar) has been widely used in early-warning and post disaster assessment. However, large area of decorrelation often occurs in the areas covered with abundant vegetation, which seriously affects the accuracy of surface deformation monitoring. In this paper, we analysed the effect of sensor parameters and external environment parameters on special decorrelation. Then Synthetic Aperture Radar (SAR) datasets acquired by X-band TerraSAR-X, Phased Array type L-band Synthetic Aperture Satellite-2 (ALOS-2), and C-band Sentinel-1 in Guizhou province were collected and analysed to generate the maps of coherence, which were used to evaluating the applicability of datasets of different wavelengths for D-InSAR in forest area. Finally, we found that datasets acquired by ALOS-2 had the best monitoring effect.
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Reigber, Andreas, Eric Schreiber, Kurt Trappschuh, Sebastian Pasch, Gerhard Müller, Daniel Kirchner, Daniel Geßwein, et al. "The High-Resolution Digital-Beamforming Airborne SAR System DBFSAR." Remote Sensing 12, no. 11 (May 27, 2020): 1710. http://dx.doi.org/10.3390/rs12111710.
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Synthetic Aperture Radar (SAR) is an established remote sensing technique that can robustly provide high-resolution imagery of the Earth’s surface. However, current space-borne SAR systems are limited, as a matter of principle, in achieving high azimuth resolution and a large swath width at the same time. Digital beamforming (DBF) has been identified as a key technology for resolving this limitation and provides various other advantages, such as an improved signal-to-noise ratio (SNR) or the adaptive suppression of radio interference (RFI). Airborne SAR sensors with digital beamforming capabilities are essential tools to research and validate this important technology for later implementation on a satellite. Currently, the Microwaves and Radar Institute of the German Aerospace Center (DLR) is developing a new advanced high-resolution airborne SAR system with digital beamforming capabilities, the so-called DBFSAR, which is planned to supplement its operational F-SAR system in near future. It is operating at X-band and features 12 simultaneous receive and 4 sequential transmit channels with 1.8 GHz bandwidth each, flexible DBF antenna setups and is equipped with a high-precision navigation and positioning unit. This paper aims to present the DBFSAR sensor development, including its radar front-end, its digital back-end, the foreseen DBF antenna configuration and the intended calibration strategy. To analyse the status, performance, and calibration quality of the DBFSAR system, this paper also includes some first in-flight results in interferometric and multi-channel marine configurations. They demonstrate the excellent performance of the DBFSAR system during its first flight campaigns.
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Wnuk, Kendall, Wendy Zhou, and Marte Gutierrez. "Mapping Urban Excavation Induced Deformation in 3D via Multiplatform InSAR Time-Series." Remote Sensing 13, no. 23 (November 23, 2021): 4748. http://dx.doi.org/10.3390/rs13234748.
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Excavation of a subway station and rail crossover cavern in downtown Los Angeles, California, USA, induced over 1.8 cm of surface settlement between June 2018 and February 2019 as measured by a ground-based monitoring system. Point measurements of surface deformation above the excavation were extracted by applying Interferometric Synthetic Aperture Radar (InSAR) time-series analyses to data from multiple sensors with different wavelengths. These sensors include C-band Sentinel-1, X-band COSMO-SkyMed, and L-band Uninhabited Aerial Vehicle SAR (UAVSAR). The InSAR time-series point measurements were interpolated to continuous distribution surfaces, weighted by distance, and entered into the Minimum-Acceleration (MinA) algorithm to calculate 3D displacement values. This dataset, composed of satellite and airborne SAR data from X, C, and L band sensors, revealed previously unidentified deformation surrounding the 2nd Street and Broadway Subway Station and the adjacent rail crossover cavern, with maximum vertical and horizontal deformations reaching 2.5 cm and 1.7 cm, respectively. In addition, the analysis shows that airborne SAR data with alternative viewing geometries to traditional polar-orbiting SAR satellites can be used to constrain horizontal displacements in the North-South direction while maintaining agreement with ground-based data.
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Yamada, Y. "Preliminary Study on the Radar Vegetation Index (RVI) Application to Actual Paddy Fields by ALOS/PALSAR Full-polarimetry SAR Data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 28, 2015): 129–31. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-129-2015.
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Kim and van Zyl (2001) proposed a kind of radar vegetation index (RVI). RVI = 4*min(λ1, λ2, λ3) / (λ1 + λ2 + λ3) They modified the equation as follows. (2009) RVI = 8 * σ<sup>0</sup>hv / (σ<sup>0</sup>hh + σ<sup>0</sup>vv +σ<sup>0</sup>hv ) by L-band full-polarimetric SAR data. They applied it into rice crop and soybean. (Y.Kim, T.Jackson et al., 2012) They compared RVI for L-, C- and X-bands to crop growth data, LAI and NDVI. They found L-band RVI was well correlated with Vegetation Water Content, LAI and NDVI. But the field data were collected by the multifrequency polarimetric scatterometer. The platform height was 4.16 meters from the ground. The author tried to apply the method to actual paddy fields near Tsukuba science city in Japan using ALOS/PALSAR, full-polarimetry L-band SAR data. The staple crop in Eastern Asia is rice and paddy fields are dominant land use. A rice-planting machine comes into wide use in this areas. The young rice plants were bedded regularly ridged line in the paddy fields by the machine. The space between two ridges of rice plants is about 30 cm and the wave length of PALSAR sensor is about 23 cm. Hence the Bragg scattering will appear depending upon the direction of the ridges of paddy fields. Once the Bragg scattering occurs, the backscattering values from the pixels should be very high comparing the surrounding region. Therefore the radar vegetation index (RVI) would be saturated. The RVI did not follow the increasing of vegetation anymore. Japan has launched ALOS-2 satellite and it has PALSAR-2, L-band SAR. Therefore RVI application product by PALSAR-2 will be watched with deep interest.
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Charron, Stéphane, Jean-Michel Negret, Erlinda Bieseas, Georges Peigne, and Dario Tarchi. "Towards a New Concept of Airborne Surveillance System for Oil Pollution Fighting." International Oil Spill Conference Proceedings 2003, no. 1 (April 1, 2003): 1213–18. http://dx.doi.org/10.7901/2169-3358-2003-1-1213.
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ABSTRACT RAPSODI (Remote sensing Anti-Pollution System for geographical Data Integration) is a European Community funded program. Within this program, remote sensing, radar and oil spill control specialists, associated with airborne system designers, have gathered their efforts in order to propose a new concept of airborne surveillance system for oil pollution fighting. This paper describes the main tasks carried out in the RAPSODI project. Firstly, we describe the real size experimentation campaigns at sea with voluntary controlled releases of crude oil and other chemicals which were conducted to collect data for the project. These campaigns have involved many aerial and naval assets from various organizations. Secondly, the development of an airborne SAR (Synthetic Aperture Radar) sensor based upon the existing Thaïes Ocean Master X-band radar is presented. This sensor is able to generate high-resolution images and allows detecting sea pollution in almost any environmental conditions. It is the key sensor of the proposed system. Thirdly, we introduce the innovative technique of images processing developed and assessed in the frame of RAPSODI. These techniques allow extracting oil spill airborne SAR signatures even in very unfavorable conditions. Fourthly, we stress a major technical issue: the GIS (Geographic Information System) approach chosen for the system. Since an airborne system for oil pollution fighting relies on various sensors and, moreover, as their data can be geocoded, the use of GIS improves the efficiency of an airborne system in merging sensor data, chart data and tactical objects. Finally, we describe the proposed airborne system. Its architecture is based on software and hardware on the shelf components. It is generic in order to be adaptable to different types of carriers, types of missions and crew concepts.
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Xu, Yuankun, Zhong Lu, and Jin-Woo Kim. "P-Band InSAR for Geohazard Detection over Forested Terrains: Preliminary Results." Remote Sensing 13, no. 22 (November 14, 2021): 4575. http://dx.doi.org/10.3390/rs13224575.
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Decorrelation of X, C, and L-band InSAR (Interferometric Synthetic Aperture Radar) over densely vegetated regions is a common obstacle for detecting ground deformation beneath forest canopies. Using long-wavelength P-band SAR sensors (wavelength of 69.72 cm), which can penetrate through dense forests and collect relatively consistent signals from ground surface, is one potential solution. Here, we experimented using the NASA JPL (Jet Propulsion Laboratory)’s P-band AirMOSS (Airborne Microwave Observatory of Subcanopy and Subsurface) radar system to collect repeat-pass P-band SAR data over densely vegetated regions in Oregon and California (USA), and generated by far the first P-band InSAR results to test the capability of P-band InSAR for geohazard detection over forested terrains. Our results show that the AirMOSS P-band InSAR could retain coherence two times as high as the L-band satellite ALOS-2 (Advanced Land Observing Satellite-2) data, and was significantly more effective in discovering localized geohazards that were unseen by the ALOS-2 interferograms over densely vegetated areas. Our results suggest that the airborne P-band InSAR could be a revolutionary tool for studying geohazards under dense forest canopies.
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Lanz, Peter, Armando Marino, Thomas Brinkhoff, Frank Köster, and Matthias Möller. "The InflateSAR Campaign: Testing SAR Vessel Detection Systems for Refugee Rubber Inflatables." Remote Sensing 13, no. 8 (April 13, 2021): 1487. http://dx.doi.org/10.3390/rs13081487.
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Countless numbers of people lost their lives at Europe’s southern borders in recent years in the attempt to cross to Europe in small rubber inflatables. This work examines satellite-based approaches to build up future systems that can automatically detect those boats. We compare the performance of several automatic vessel detectors using real synthetic aperture radar (SAR) data from X-band and C-band sensors on TerraSAR-X and Sentinel-1. The data was collected in an experimental campaign where an empty boat lies on a lake’s surface to analyse the influence of main sensor parameters (incidence angle, polarization mode, spatial resolution) on the detectability of our inflatable. All detectors are implemented with a moving window and use local clutter statistics from the adjacent water surface. Among tested detectors are well-known intensity-based (CA-CFAR), sublook-based (sublook correlation) and polarimetric-based (PWF, PMF, PNF, entropy, symmetry and iDPolRAD) approaches. Additionally, we introduced a new version of the volume detecting iDPolRAD aimed at detecting surface anomalies and compare two approaches to combine the volume and the surface in one algorithm, producing two new highly performing detectors. The results are compared with receiver operating characteristic (ROC) curves, enabling us to compare detectors independently of threshold selection.
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Verma, A. K., R. Nandan, and A. Verma. "KNOWLEDGE BASED CLASSIFIER BASED ON BACKSCATTERING COEFFICIENT FOR MONITORING THE CROP GROWTH ANALYSIS USING MULTI-TEMPORAL IMAGES OF SPACE-BORNE SYNTHETIC APERTURE RADAR (SAR) SENSORS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 643–47. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-643-2019.
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<p><strong>Abstract.</strong> Space-based observation of crops and agro-system on the Earth surface is one of the most important applications of remote sensing using the sensors in optical and microwave spectrum to assess the crop growth for decision making for developing crop information and management system. Remote sensing technology provides scalable and reliable information in respect of rice crop grown area, its crop growth and prediction of crop yield due to acquisition of satellite imagery during the revisit of the orbit by space-borne sensors in optical and microwave spectrum. Synthetic Aperture Radar has the advantages of all-weather, day and night imaging, canopy penetration, and high-resolution capabilities, which makes Space-borne SAR sensors as an effective system for monitoring crop growth, crop classification and mapping of crop area based on the crop canopy interaction of SAR signals due to backscattering coefficients of the earth surface. SAR data from ERS-1/2 SAR, ENVISAT ASAR, ALOS-1/2 PALSAR, Radarsat-1/2 SAR, TerraSAR, COSMO-SkyMed, and Sentinel-1 have been used by various researchers for identification and analysis of rice crop growth based on the backscattering values in different regions of Asia and European region, where backscattered image depends of various earth surface and SAR sensors parameters. In this paper, knowledge based classifier using SAR images of existing space-borne-SAR sensors have been developed based on modeling of SAR backscattering coefficients in C-band and X-band for monitoring the rice crop growth and its analysis using multi-temporal and multi-frequency- SAR sensors data.</p>
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Esposito, Carmen, Antonio Natale, Gianfranco Palmese, Paolo Berardino, Riccardo Lanari, and Stefano Perna. "On the Capabilities of the Italian Airborne FMCW AXIS InSAR System." Remote Sensing 12, no. 3 (February 6, 2020): 539. http://dx.doi.org/10.3390/rs12030539.
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Airborne Synthetic Aperture Radar (SAR) systems are gaining increasing interest within the remote sensing community due to their operational flexibility and observation capabilities. Among these systems, those exploiting the Frequency-Modulated Continuous-Wave (FMCW) technology are compact, lightweight, and comparatively low cost. For these reasons, they are becoming very attractive, since they can be easily mounted onboard ever-smaller and highly flexible aerial platforms, like helicopters or unmanned aerial vehicles (UAVs). In this work, we present the imaging and topographic capabilities of a novel Italian airborne SAR system developed in the frame of cooperation between a public research institute (IREA-CNR) and a private company (Elettra Microwave S.r.l.). The system, which is named AXIS (standing for Airborne X-band Interferometric SAR), is based on FMCW technology and is equipped with a single-pass interferometric layout. In the work we first provide a description of the AXIS system. Then, we describe the acquisition campaign carried out in April 2018, just after the system completion. Finally, we perform an analysis of the radar data acquired during the campaign, by presenting a quantitative assessment of the quality of the SLC (Single Look Complex) SAR images and the interferometric products achievable through the system. The overall analysis aims at providing first reference values for future research and operational activities that will be conducted with this sensor.
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Sadeh, Yuval, Hai Cohen, Shimrit Maman, and Dan Blumberg. "Evaluation of Manning’s n Roughness Coefficient in Arid Environments by Using SAR Backscatter." Remote Sensing 10, no. 10 (September 20, 2018): 1505. http://dx.doi.org/10.3390/rs10101505.
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The prediction of arid region flash floods (magnitude and frequency) is essential to ensure the safety of human life and infrastructures and is commonly based on hydrological models. Traditionally, catchment characteristics are extracted using point-based measurements. A considerable improvement of point-based observations is offered by remote sensing technologies, which enables the determination of continuous spatial hydrological parameters and variables, such as surface roughness, which significantly influence runoff velocity and depth. Hydrological models commonly express the surface roughness using Manning’s roughness coefficient (n) as a key variable. The objectives were thus to determine surface roughness by exploiting a new high spatial resolution spaceborne synthetic aperture radar (SAR) technology and to examine the correlation between radar backscatter and Manning’s roughness coefficient in an arid environment. A very strong correlation (R2 = 0.97) was found between the constellation of small satellites for Mediterranean basin observation (COSMO)-SkyMed SAR backscatter and surface roughness. The results of this research demonstrate the feasibility of using an X-band spaceborne sensor with high spatial resolution for the evaluation of surface roughness in flat arid environments. The innovative method proposed to evaluate Manning’s n roughness coefficient in arid environments with sparse vegetation cover using radar backscatter may lead to improvements in the performance of hydrological models.
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Voglimacci-Stephanopoli, Joëlle, Anna Wendleder, Hugues Lantuit, Alexandre Langlois, Samuel Stettner, Andreas Schmitt, Jean-Pierre Dedieu, Achim Roth, and Alain Royer. "Potential of X-band polarimetric synthetic aperture radar co-polar phase difference for arctic snow depth estimation." Cryosphere 16, no. 6 (June 9, 2022): 2163–81. http://dx.doi.org/10.5194/tc-16-2163-2022.
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Abstract. Changes in snowpack associated with climatic warming has drastic impacts on surface energy balance in the cryosphere. Yet, traditional monitoring techniques, such as punctual measurements in the field, do not cover the full snowpack spatial and temporal variability, which hampers efforts to upscale measurements to the global scale. This variability is one of the primary constraints in model development. In terms of spatial resolution, active microwaves (synthetic aperture radar – SAR) can address the issue and outperform methods based on passive microwaves. Thus, high-spatial-resolution monitoring of snow depth (SD) would allow for better parameterization of local processes that drive the spatial variability of snow. The overall objective of this study is to evaluate the potential of the TerraSAR-X (TSX) SAR sensor and the wave co-polar phase difference (CPD) method for characterizing snow cover at high spatial resolution. Consequently, we first (1) investigate SD and depth hoar fraction (DHF) variability between different vegetation classes in the Ice Creek catchment (Qikiqtaruk/Herschel Island, Yukon, Canada) using in situ measurements collected over the course of a field campaign in 2019; (2) evaluate linkages between snow characteristics and CPD distribution over the 2019 dataset; and (3) determine CPD seasonality considering meteorological data over the 2015–2019 period. SD could be extracted using the CPD when certain conditions are met. A high incidence angle (>30∘) with a high topographic wetness index (TWI) (>7.0) showed correlation between SD and CPD (R2 up to 0.72). Further, future work should address a threshold of sensitivity to TWI and incidence angle to map snow depth in such environments and assess the potential of using interpolation tools to fill in gaps in SD information on drier vegetation types.
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Weinman, J. A., and F. S. Marzano. "An Exploratory Study to Derive Precipitation over Land from X-Band Synthetic Aperture Radar Measurements." Journal of Applied Meteorology and Climatology 47, no. 2 (February 1, 2008): 562–75. http://dx.doi.org/10.1175/2007jamc1663.1.
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Abstract Global precipitation measurements from space-based radars and microwave radiometers have been the subject of numerous studies during the past decade. Rainfall retrievals over land from spaceborne microwave radiometers depend mainly on scattering from frozen hydrometeors. Unfortunately, the relationship between frozen hydrometeors and rainfall varies considerably. The large field of view and related beam filling of microwave radiometer footprints introduce additional difficulties. Some of these problems will be addressed by the improved sensors that will be placed on the Global Precipitation Measurement (GPM) core satellite. Two shuttle missions demonstrated that X-band synthetic aperture radar (X-SAR) could observe rainfall over land. Several X-band SARs that can provide such measurements will be launched in the coming decade. These include four Constellation of Small Satellites for Mediterranean Basin Observations (COSMO-SkyMed), two TerraSAR-X, and a fifth Korea Multipurpose Satellite (KOMPSAT-5) to be launched by the Italian, German, and Korean Space Agencies, respectively. Data from these satellites could augment the information available to the GPM science community. The present study presents computations of normalized radar cross sections (NRCS) that employed a simple, idealized two-layer cloud model that contained both rain and frozen hydrometeors. The modeled spatial distributions of these hydrometeors varied with height and horizontal distance. An exploratory algorithm was developed to retrieve the shape, width, and simple representations of the vertical profiles of frozen hydrometeors and rain from modeled NRCS scans. A discussion of uncertainties in the retrieval is presented.
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Lapini, Alessandro, Simone Pettinato, Emanuele Santi, Simonetta Paloscia, Giacomo Fontanelli, and Andrea Garzelli. "Comparison of Machine Learning Methods Applied to SAR Images for Forest Classification in Mediterranean Areas." Remote Sensing 12, no. 3 (January 22, 2020): 369. http://dx.doi.org/10.3390/rs12030369.
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In this paper, multifrequency synthetic aperture radar (SAR) images from ALOS/PALSAR, ENVISAT/ASAR and Cosmo-SkyMed sensors were studied for forest classification in a test area in Central Italy (San Rossore), where detailed in-situ measurements were available. A preliminary discrimination of the main land cover classes and forest types was carried out by exploiting the synergy among L-, C- and X-bands and different polarizations. SAR data were preliminarily inspected to assess the capabilities of discriminating forest from non-forest and separating broadleaf from coniferous forests. The temporal average backscattering coefficient ( σ ¯ °) was computed for each sensor-polarization pair and labeled on a pixel basis according to the reference map. Several classification methods based on the machine learning framework were applied and validated considering different features, in order to highlight the contribution of bands and polarizations, as well as to assess the classifiers’ performance. The experimental results indicate that the different surface types are best identified by using all bands, followed by joint L- and X-bands. In the former case, the best overall average accuracy (83.1%) is achieved by random forest classification. Finally, the classification maps on class edges are discussed to highlight the misclassification errors.
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Ojha, Chandrakanta, Adele Fusco, and Innocenzo M. Pinto. "Interferometric SAR Phase Denoising Using Proximity-Based K-SVD Technique." Sensors 19, no. 12 (June 14, 2019): 2684. http://dx.doi.org/10.3390/s19122684.
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This paper addresses the problem of interferometric noise reduction in Synthetic Aperture Radar (SAR) interferometry based on sparse and redundant representations over a trained dictionary. The idea is to use a Proximity-based K-SVD (ProK-SVD) algorithm on interferometric data for obtaining a suitable dictionary, in order to extract the phase image content effectively. We implemented this strategy on both simulated as well as real interferometric data for the validation of our approach. For synthetic data, three different training dictionaries have been compared, namely, a dictionary extracted from the data, a dictionary obtained by a uniform random distribution in [ − π , π ] , and a dictionary built from discrete cosine transform. Further, a similar strategy plan has been applied to real interferograms. We used interferometric data of various SAR sensors, including low resolution C-band ERS/ENVISAT, medium L-band ALOS, and high resolution X-band COSMO-SkyMed, all over an area of Mt. Etna, Italy. Both on simulated and real interferometric phase images, the proposed approach shows significant noise reduction within the fringe pattern, without any considerable loss of useful information.
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Khosravi, Mohammad R., Babak Bahri-Aliabadi, Seyed R. Salari, Sadegh Samadi, Habib Rostami, and Vahid Karimi. "A Tutorial and Performance Analysis on ENVI Tools for SAR Image Despeckling." Current Signal Transduction Therapy 15, no. 2 (December 1, 2020): 215–22. http://dx.doi.org/10.2174/1574362413666181005101315.
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Background: The presence of speckle noise in synthetic aperture radar (SAR) images makes the images of low quality in terms of textural features and spatial resolution which are required for processing issues such as image classification and clustering. Already, there are many adaptive filters to remove noise in SAR images. ENVI software is a fully applicable tool for this purpose which has a good library including several filters in the classes of adaptive, orderstatistics and non-linear filters. Materials and Methods: In this study, the toolbox of ENVI is reviewed, analyzed and then numerically evaluated based on several single-band images along with multi-band polarimetric SAR (Pol-SAR) images achieved from SAR sensors such as TerraSAR-X. For evaluation, two metrics including Equivalent Number of Looks (ENL) and Edge Preservation Index (EPI) are used which show the ability of the filters in preserving jointly spatial/textural features based on general information and edges quality, respectively. Results: It is notable that both metrics illustrate that some classic filters are better in comparison to newer filters. Conclusion: The experiments can help us in selecting a better filter towards our aims. In this respect, attention to the results of commercial filters of ENVI software and their analysis can guide us to find the best case in order to process commercial data of SAR sensors in the applications of environmental monitoring, geo-science studies, industrial usages and so on.
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Beckett, K. "UrtheCast SECOND-GENERATION EARTH OBSERVATION SENSORS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 30, 2015): 1069–73. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-1069-2015.
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UrtheCast’s Second-Generation state-of-the-art Earth Observation (EO) remote sensing platform will be hosted on the NASA segment of International Space Station (ISS). This platform comprises a high-resolution dual-mode (pushbroom and video) optical camera and a dual-band (X and L) Synthetic Aperture RADAR (SAR) instrument. These new sensors will complement the firstgeneration medium-resolution pushbroom and high-definition video cameras that were mounted on the Russian segment of the ISS in early 2014. <br><br> The new cameras are expected to be launched to the ISS in late 2017 via the Space Exploration Technologies Corporation Dragon spacecraft. The Canadarm will then be used to install the remote sensing platform onto a CBM (Common Berthing Mechanism) hatch on Node 3, allowing the sensor electronics to be accessible from the inside of the station, thus limiting their exposure to the space environment and allowing for future capability upgrades. <br><br> The UrtheCast second-generation system will be able to take full advantage of the strengths that each of the individual sensors offers, such that the data exploitation capabilities of the combined sensors is significantly greater than from either sensor alone. This represents a truly novel platform that will lead to significant advances in many other Earth Observation applications such as environmental monitoring, energy and natural resources management, and humanitarian response, with data availability anticipated to begin after commissioning is completed in early 2018.
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Kang, Q., J. Zhang, G. Huang, S. Yang, and Z. Zhang. "TECHNOLOGY ON HIGH-ACCURACY DEM EXTRACTION FROM AIRBORNE INTERFEROMETRIC SAR." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 31, 2022): 1209–15. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-1209-2022.
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Abstract. Remote sensing image mapping (aviation, aerospace, UAV) is the main form of current mapping. Optical image mapping has been a certain research foundation and played an important role in the mapping task. However, cloud, ice, snow, rain, and haze conditions seriously affect the interpretation and processing of optical images, making it difficult to meet the needs of rapid emergency response. Synthetic Aperture Radar (SAR) is currently the only method that can achieve all-day, all-weather rapid imaging under various weather conditions such as day, night, cloud, snow, rain, etc. With unique advantages unmatched by traditional optical remote sensing technology, it is an important technical guarantee for improving emergency response capabilities and promoting remote sensing and geographic information industries, and an earth observation technology that has been invested and vigorously developed internationally. Digital Elevation Model (DEM) plays an increasingly important role in the application of terrain information acquisition such as rapid acquisition of sudden disasters and surface deformation information acquisition. Rapid acquisition of large area high-accuracy DEM is one of the important research contents of SAR mapping. Based on interferometric SAR data combined with sensor orbit data, external DEM and other auxiliary data, DEM is finally generated through complex image registration, interference image generation, removal of flat ground effects, interference phase filtering, phase unwrapping, interference calibration and geocoding. The registration accuracy of multiple images greatly affects the accuracy of DEM. This paper proposes an improved registration method based on the conventional rough registration, pixel-level registration and sub-pixel registration. When the motion compensation of the sensor platform is unknown during imaging, it can meet the requirements of high-accuracy and large-scale rapid registration of dual-antenna polarization interferometric SAR. The experimental data is X-band airborne SAR data in Weinan region of Shaanxi Province, and DEM products with the elevation accuracy better than 0.5m is generated, which verifies the effectiveness, accuracy and fast processing ability of the method.
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Rosenow, Andrew A., Kenneth Howard, and José Meitín. "Gap-Filling Mobile Radar Observations of a Snow Squall in the San Luis Valley." Monthly Weather Review 146, no. 8 (July 24, 2018): 2469–81. http://dx.doi.org/10.1175/mwr-d-17-0323.1.
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Abstract On 24 January 2017, a convective snow squall developed in the San Luis Valley of Colorado. This squall produced rapidly varying winds at San Luis Valley airport in Alamosa, Colorado, with gusts up to 12 m s−1, and an associated visibility drop to 1.4 km from unlimited in less than 10 min. This snow squall was largely undetected by the operational WSR-88D network because of the Sangre de Cristo Range of the Rocky Mountains lying between the valley and the nearest WSR-88D in Pueblo, Colorado. This study presents observations of the snow squall from the X-band NOAA X-Pol radar, which was deployed in the San Luis Valley during the event. These observations document the squall developing from individual convective cells and growing upscale into a linear squall, with peak radial velocities of 15 m s−1. The environment conducive to the development of this snow squall is examined using data from the High-Resolution Rapid Refresh model, which shows an environment unstable to ascending surface-based parcels, with surface-based convective available potential energy (SBCAPE) values up to 600 J kg−1 in the San Luis Valley. The mobile radar data are integrated into the Multi-Radar Multi-Sensor (MRMS) mosaic to illustrate both the large improvement in detectability of this event gained from a gap-filling radar as well as the capability of MRMS to incorporate data from new radars designed to fill gaps in the current radar network.
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Sano, Edson E., Paola Rizzoli, Christian N. Koyama, Manabu Watanabe, Marcos Adami, Yosio E. Shimabukuro, Gustavo Bayma, and Daniel M. Freitas. "Comparative Analysis of the Global Forest/Non-Forest Maps Derived from SAR and Optical Sensors. Case Studies from Brazilian Amazon and Cerrado Biomes." Remote Sensing 13, no. 3 (January 21, 2021): 367. http://dx.doi.org/10.3390/rs13030367.
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Global-scale forest/non-forest (FNF) maps are of crucial importance for applications like biomass estimation and deforestation monitoring. Global FNF maps based on optical remote sensing data have been produced by the wall-to-wall satellite image analyses or sampling strategies. The German Aerospace Center (DLR) and the Japan Aerospace Exploration Agency (JAXA) also made available their global FNF maps based on synthetic aperture radar (SAR) data. This paper attempted to answer the following scientific question: how comparable are the FNF products derived from optical and SAR data? As test sites we selected the Amazon (tropical rainforest) and Cerrado (tropical savanna) biomes, the two largest Brazilian biomes. Forest estimations from 2015 derived from TanDEM-X (X band; HH polarization) and ALOS-2 (L band; HV polarization) SAR data, as well as forest cover information derived from Landsat 8 optical data were compared with each other at the municipality and image sampling levels. The optical-based forest estimations considered in this study were derived from the MapBiomas project, a Brazilian multi-institutional project to map land use and land cover (LULC) classes of an entire country based on historical time series of Landsat data. In addition to the existing forest maps, a set of 1619 Landsat 8 RGB color composites was used to generate new independent comparison data composed of circular areas with 5-km diameter, which were visually interpreted after image segmentation. The Spearman rank correlation estimated the correlation among the data sets and the paired Mann–Whitney–Wilcoxon tested the hypothesis that the data sets are statistically equal. Results showed that forest maps derived from SAR and optical satellites are statistically different regardless of biome or scale of study (municipality or image sampling), except for the Cerrado´s forest estimations derived from TanDEM-X and ALOS-2. Nevertheless, the percentage of pixels classified as forest or non-forest by both SAR sensors were 90% and 80% for the Amazon and Cerrado biome, respectively, indicating an overall good agreement.
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Martins Costa do Amaral, Lia, Stefano Barbieri, Daniel Vila, Silvia Puca, Gianfranco Vulpiani, Giulia Panegrossi, Thiago Biscaro, et al. "Assessment of Ground-Reference Data and Validation of the H-SAF Precipitation Products in Brazil." Remote Sensing 10, no. 11 (November 5, 2018): 1743. http://dx.doi.org/10.3390/rs10111743.
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The uncertainties associated with rainfall estimates comprise various measurement scales: from rain gauges and ground-based radars to the satellite rainfall retrievals. The quality of satellite rainfall products has improved significantly in recent decades; however, such algorithms require validation studies using observational rainfall data. For this reason, this study aims to apply the H-SAF consolidated radar data processing to the X-band radar used in the CHUVA campaigns and apply the well established H-SAF validation procedure to these data and verify the quality of EUMETSAT H-SAF operational passive microwave precipitation products in two regions of Brazil (Vale do Paraíba and Manaus). These products are based on two rainfall retrieval algorithms: the physically based Bayesian Cloud Dynamics and Radiation Database (CDRD algorithm) for SSMI/S sensors and the Passive microwave Neural network Precipitation Retrieval algorithm (PNPR) for cross-track scanning radiometers (AMSU-A/AMSU-B/MHS sensors) and for the ATMS sensor. These algorithms, optimized for Europe, Africa and the Southern Atlantic region, provide estimates for the MSG full disk area. Firstly, the radar data was treated with an overall quality index which includes corrections for different error sources like ground clutter, range distance, rain-induced attenuation, among others. Different polarimetric and non-polarimetric QPE algorithms have been tested and the Vulpiani algorithm (hereafter, R q 2 V u 15 ) presents the best precipitation retrievals when compared with independent rain gauges. Regarding the results from satellite-based algorithms, generally, all rainfall retrievals tend to detect a larger precipitation area than the ground-based radar and overestimate intense rain rates for the Manaus region. Such behavior is related to the fact that the environmental and meteorological conditions of the Amazon region are not well represented in the algorithms. Differently, for the Vale do Paraíba region, the precipitation patterns were well detected and the estimates are in accordance with the reference as indicated by the low mean bias values.
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Mohammadi, Ayub, Sadra Karimzadeh, Shazad Jamal Jalal, Khalil Valizadeh Kamran, Himan Shahabi, Saeid Homayouni, and Nadhir Al-Ansari. "A Multi-Sensor Comparative Analysis on the Suitability of Generated DEM from Sentinel-1 SAR Interferometry Using Statistical and Hydrological Models." Sensors 20, no. 24 (December 16, 2020): 7214. http://dx.doi.org/10.3390/s20247214.
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Digital elevation model (DEM) plays a vital role in hydrological modelling and environmental studies. Many essential layers can be extracted from this land surface information, including slope, aspect, rivers, and curvature. Therefore, DEM quality and accuracy will affect the extracted features and the whole process of modeling. Despite freely available DEMs from various sources, many researchers generate this information for their areas from various observations. Sentinal-1 synthetic aperture radar (SAR) images are among the best Earth observations for DEM generation thanks to their availabilities, high-resolution, and C-band sensitivity to surface structure. This paper presents a comparative study, from a hydrological point of view, on the quality and reliability of the DEMs generated from Sentinel-1 data and DEMs from other sources such as AIRSAR, ALOS-PALSAR, TanDEM-X, and SRTM. To this end, pair of Sentinel-1 data were acquired and processed using the SAR interferometry technique to produce a DEM for two different study areas of a part of the Cameron Highlands, Pahang, Malaysia, a part of Sanandaj, Iran. Based on the estimated linear regression and standard errors, generating DEM from Sentinel-1 did not yield promising results. The river streams for all DEMs were extracted using geospatial analysis tool in a geographic information system (GIS) environment. The results indicated that because of the higher spatial resolution (compared to SRTM and TanDEM-X), more stream orders were delineated from AIRSAR and Sentinel-1 DEMs. Due to the shorter perpendicular baseline, the phase decorrelation in the created DEM resulted in a lot of noise. At the same time, results from ground control points (GCPs) showed that the created DEM from Sentinel-1 is not promising. Therefore, other DEMs’ performance, such as 90-meters’ TanDEM-X and 30-meters’ SRTM, are better than Sentinel-1 DEM (with a better spatial resolution).
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Levin, Noam, and Stuart Phinn. "Assessing the 2022 Flood Impacts in Queensland Combining Daytime and Nighttime Optical and Imaging Radar Data." Remote Sensing 14, no. 19 (October 8, 2022): 5009. http://dx.doi.org/10.3390/rs14195009.
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In the Australian summer season of 2022, exceptional rainfall events occurred in Southeast Queensland and parts of New South Wales, leading to extensive flooding of rural and urban areas. Here, we map the extent of flooding in the city of Brisbane and evaluate the change in electricity usage as a proxy for flood impact using VIIRS nighttime brightness imagery. Scanning a wide range of possible sensors, we used pre-flood and peak-flood PlanetScope imagery to map the inundated areas, using a new spectral index we developed, the Normalized Difference Inundation Index (NDII), which is based on changes in the NIR reflectance due to sediment-laden flood waters. We compared the Capella-Space X-band/HH imaging radar data captured at peak-flood date to the PlanetScope-derived mapping of the inundated areas. We found that in the Capella-Space image, significant flooded areas identified in PlanetScope imagery were omitted. These omission errors may be partly explained by the use of a single-date radar image, by the X-band, which is partly scattered by tree canopy, and by the SAR look angle under which flooded streets may be blocked from the view of the satellite. Using VIIRS nightly imagery, we were able to identify grid cells where electricity usage was impacted due to the floods. These changes in nighttime brightness matched both the inundated areas mapped via PlanetScope data as well as areas corresponding with decreased electricity loads reported by the regional electricity supplier. Altogether we demonstrate that using a variety of optical and radar sensors, as well as nighttime and daytime sensors, enable us to overcome data gaps and better understand the impact of flood events. We also emphasize the importance of high temporal revisit times (at least twice daily) to more accurately monitor flood events.
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Budillon, Alessandra, and Gilda Schirinzi. "Remote Monitoring of Civil Infrastructure Based on TomoSAR." Infrastructures 7, no. 4 (April 6, 2022): 52. http://dx.doi.org/10.3390/infrastructures7040052.
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Structural health monitoring and damage detection tools are extremely important topics nowadays with the civil infrastructure aging and deteriorating problems observed in urban areas. These tasks can be done by visual inspection and by using traditional in situ methods, such as leveling or using traditional mechanical and electrical sensors, but these approaches are costly, labor-intensive and cannot be performed with a high temporal frequency. In recent years, remote sensing has proved to be a very promising methodology in evaluating the health of a structure by assessing its deformation and thermal dilation. The satellite-based Synthetic Aperture Radar Tomography (TomoSAR) technique, based on the exploitation of a stack of multi-temporal SAR images, allows to remotely sense the movement and the thermal dilation of individual structures with a centimeter- to millimeter-level accuracy, thanks to new generation high-resolution satellite-borne sensors. In this paper, the effectiveness of a recently developed TomoSAR technique in assessing both possible deformations and the thermal dilation evolution of man-made structures is shown. The results obtained using X-band SAR data in two case studies, concerning two urban structures in the city of Naples (Italy), are presented.
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Dabiri, Z., D. Hölbling, S. Lang, and A. Bartsch. "APPLICABILITY OF MULTI-SEASONAL X-BAND SAR IMAGERY FOR MULTIRESOLUTION SEGMENTATION: A CASE STUDY IN A RIPARIAN MIXED FOREST." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 10, 2015): 123–28. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-123-2015.
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The increasing availability of synthetic aperture radar (SAR) data from a range of different sensors necessitates efficient methods for semi-automated information extraction at multiple spatial scales for different fields of application. The focus of the presented study is two-fold: 1) to evaluate the applicability of multi-temporal TerraSAR-X imagery for multiresolution segmentation, and 2) to identify suitable Scale Parameters through different weighing of different homogeneity criteria, mainly colour variance. Multiresolution segmentation was used for segmentation of multi-temporal TerraSAR-X imagery, and the ESP (Estimation of Scale Parameter) tool was used to identify suitable Scale Parameters for image segmentation. The validation of the segmentation results was performed using very high resolution WorldView-2 imagery and a reference map, which was created by an ecological expert. The results of multiresolution segmentation revealed that in the context of object-based image analysis the TerraSAR-X images are applicable for generating optimal image objects. Furthermore, ESP tool can be used as an indicator for estimation of Scale Parameter for multiresolution segmentation of TerraSAR-X imagery. Additionally, for more reliable results, this study suggests that the homogeneity criterion of colour, in a variance based segmentation algorithm, needs to be set to high values. Setting the shape/colour criteria to 0.005/0.995 or 0.00/1 led to the best results and to the creation of adequate image objects.
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Pazhanivelan, S., P. Kannan, P. Christy Nirmala Mary, E. Subramanian, S. Jeyaraman, A. Nelson, T. Setiyono, F. Holecz, M. Barbieri, and M. Yadav. "Rice Crop Monitoring and Yield Estimation Through Cosmo Skymed and TerraSAR-X: A SAR-Based Experience in India." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 28, 2015): 85–92. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-85-2015.
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Rice is the most important cereal crop governing food security in Asia. Reliable and regular information on the area under rice production is the basis of policy decisions related to imports, exports and prices which directly affect food security. Recent and planned launches of SAR sensors coupled with automated processing can provide sustainable solutions to the challenges on mapping and monitoring rice systems. High resolution (3m) Synthetic Aperture Radar (SAR) imageries were used to map and monitor rice growing areas in selected three sites in TamilNadu, India to determine rice cropping extent, track rice growth and estimate yields. A simple, robust, rule-based classification for mapping rice area with multi-temporal, X-band, HH polarized SAR imagery from COSMO Skymed and TerraSAR X and site specific parameters were used. The robustness of the approach is demonstrated on a very large dataset involving 30 images across 3 footprints obtained during 2013-14. A total of 318 in-season site visits were conducted across 60 monitoring locations for rice classification and 432 field observations were made for accuracy assessment. Rice area and Start of Season (SoS) maps were generated with classification accuracies ranging from 87- 92 per cent. Using ORYZA2000, a weather driven process based crop growth simulation model; yield estimates were made with the inclusion of rice crop parameters derived from the remote sensing products viz., seasonal rice area, SoS and backscatter time series. Yield Simulation accuracy levels of 87 per cent at district level and 85- 96 per cent at block level demonstrated the suitability of remote sensing products for policy decisions ensuring food security and reducing vulnerability of farmers in India.
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Ma, Deying, Mahdi Motagh, Guoxiang Liu, Rui Zhang, Xiaowen Wang, Bo Zhang, Wei Xiang, and Bing Yu. "Thaw Settlement Monitoring and Active Layer Thickness Retrieval Using Time Series COSMO-SkyMed Imagery in Iqaluit Airport." Remote Sensing 14, no. 9 (April 30, 2022): 2156. http://dx.doi.org/10.3390/rs14092156.
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Thaw consolidation of degrading permafrost is a serious hazard to the safety and operation of infrastructure. Monitoring thermal changes in the active layer (AL), the proportion of the soil above permafrost that thaws and freezes periodically, is critical to understanding the conditions of the top layer above the permafrost and regulating the construction, operation, and maintenance of facilities. However, this is a very challenging task using ground-based methods such as ground-penetrating radar (GPR) or temperature sensors. This study explores the integration of interferometric measurements from high-resolution X-band Synthetic Aperture Radar (SAR) images and volumetric water content (VWC) data from SoilGrids to quantify detailed spatial variations in active layer thickness (ALT) in Iqaluit, the territorial capital of Nunavut in Canada. A total of 21 SAR images from COSMO Sky-Med (CSK) were first analyzed using the freely connected network interferometric synthetic aperture radar (FCNInSAR) method to map spatial and temporal variations in ground surface subsidence in the study area. Subsequently, we built an ALT retrieval model by introducing the thaw settlement coefficient, which takes soil properties and saturation state into account. The subsidence measurements from InSAR were then integrated with VWC extracted from the SoilGrids database to estimate changes in ALT. For validation, we conducted a comparison between estimated ALTs and in situ measurements in the airport sector. The InSAR survey identifies several sites of ground deformation at Iqaluit, subsiding at rates exceeding 80 mm/year. The subsidence rate changes along the runway coincide with frost cracks and ice-wedge furrows. The obtained ALTs, ranging from 0 to 5 m, vary significantly in different sediments. Maximum ALTs are found for rock areas, while shallow ALTs are distributed in the till blanket (Tb), the intertidal (Mi) sediments, and the alluvial flood plain (Afp) sediment units. The intersection of taxiway and runway has an AL thicker than other parts in the glaciomarine deltaic (GMd) sediments. Our study suggests that combining high-resolution SAR imagery with VWC data can provide more comprehensive ALT knowledge for hazard prevention and infrastructure operation in the permafrost zone.
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Tang, Maochuan, Qing Zhao, Antonio Pepe, Adam Thomas Devlin, Francesco Falabella, Chengfang Yao, and Zhengjie Li. "Changes of Chinese Coastal Regions Induced by Land Reclamation as Revealed through TanDEM-X DEM and InSAR Analyses." Remote Sensing 14, no. 3 (January 28, 2022): 637. http://dx.doi.org/10.3390/rs14030637.
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Chinese coastal topography has changed significantly over the last two decades due to human actions such as the development of extensive land reclamation projects. Newly-reclaimed lands typically have low elevations (<10 m) and often experience severe ground subsidence. These conditions, combined with the more frequent occurrence of extreme sea-level events amplified by global climate change, lead to an increased risk of flooding of coastal regions. This work focuses on twelve Chinese coastal areas that underwent significant changes from 2000 to 2015 in their environments, correlated to relevant land reclamation projects. First, the ground changes between 2000 and 2015 were roughly computed by comparing the TanDEM-X and the Shuttle Radar Topography Mission (SRTM) digital elevation models of the investigated areas. These results indicate that six of the analyzed coastal zones have reclaimed more than 200 km2 of new lands from 2000 to 2015, with five of them in northern China. Second, we focused specifically on the city of Shanghai, and characterized the risk of flood in this area. To this purpose, two independent sets of synthetic aperture radar (SAR) data collected at the X- and C-band through the COSMO-SkyMed (CSK) and the European Copernicus Sentinel-1 (S-1) sensors were exploited. We assumed that the still extreme seawater depth is chi-square distributed, and estimated the probability of waves overtopping the coast. We also evaluated the impact on the territory of potential extreme flood events by counting the number of very-coherent objects (at most anthropic, such as buildings and public infrastructures) that could be seriously affected by a flood. To forecast possible inundation patterns, we used the LISFLOOD-FP hydrodynamic model. Assuming that an extreme event destroyed a given sector of the coastline, we finally computed the extent of the flooded areas and quantified its impact in terms of coherent structures potentially damaged by the inundation. Experimental results showed that two coastline segments located in the southern districts of Shanghai, where the seawalls height is lower, had the highest probability of wave overtopping and the most significant density of coherent objects potentially subjected to severe flood impacts.
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Shi, Guoqiang, Peifeng Ma, Hui Lin, Bo Huang, Bowen Zhang, and Yuzhou Liu. "Potential of Using Phase Correlation in Distributed Scatterer InSAR Applied to Built Scenarios." Remote Sensing 12, no. 4 (February 19, 2020): 686. http://dx.doi.org/10.3390/rs12040686.
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The improved spatial resolution of Synthetic Aperture Radar (SAR) images from newly launched sensors has promoted a more frequent use of distributed scatterer (DS) interferometry (DSI) in urban monitoring, pursuing sufficient and detailed measurements. However, the commonly used statistical methods for homogeneous pixel clustering by exploring amplitude information are firstly, computationally intensive; furthermore, their necessity when applied to high-coherent built scenarios is little discussed in the literature. This paper explores the potential of using phase information for the detection of homogeneous pixels on built surfaces. We propose a simple phase-correlated pixel (PCP) clustering and introduce a coherence-weighted phase link (WPL), i.e., PCPWPL, to pursue a faster processing of interferogram phase denoising. Rather than relying on the statistical tests of amplitude characteristics, we exploit vector correlation in the complex domain to identify PCPs with similar phase observations, thus, avoiding the intensive hypothesis test. A coherence-weighted phase linking is applied for DS phase reconstruction. The estimation of geophysical parameters, e.g., deformation, is completed using an integrated network of persistent scatterers (PS) and DS. Efficiency of the proposed method is fairly illustrated by both synthetic and real data experiments. Pros and cons of the proposed PCPWPL were analyzed with the comparison to a conventional amplitude-based strategy using an X-band CosmoSkyMed dataset. It is demonstrated that the use of phase correlation is sufficient for DS monitoring in built scenarios, with equivalent measurement quantity and cheaper computational cost.
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Li, Liang, Gukun Liu, Jun Hong, Feng Ming, and Yu Wang. "Design and Implementation of a Multi-Band Active Radar Calibrator for SAR." Remote Sensing 11, no. 11 (June 1, 2019): 1312. http://dx.doi.org/10.3390/rs11111312.
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Over the past decade, IECAS (Institute of Electronics, Chinese Academy of Sciences) has developed a set of L-, S-, C-, and X-band active radar calibrators that are deployed during the calibration campaigns for HJ1C synthetic aperture radar (SAR), Gaofen-3 SAR, and so on. In the near future, P-band and Ka-band spaceborne SARs will be launched. We found that it is not convenient to develop special active radar calibrators (ARCs) for a specific SAR or a specific frequency band SAR, and the acquired experience could help in the design and development of a multi-band ARC. This paper describes the design and implementation of a multi-band active radar calibrator which can operate in the L-, C-, X-, and Ka-bands. Moreover, laboratory measurements are performed to characterize the performance of the multi-band ARC, paying particular attention to the gain stability, the system transfer function, the gain flatness, and the linearity of the ARC receiver. Three such ARCs are developed, and to our knowledge, the multi-band ARC is the first of its kind in China or even in the world, and it can be used to implement the calibration campaigns of the Chinese Gaofen-3 SAR, Shenzhen-1 SAR, Luojia-2 SAR, and so on.
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Yao, Hang, Bolin Fu, Ya Zhang, Sunzhe Li, Shuyu Xie, Jiaoling Qin, Donglin Fan, and Ertao Gao. "Combination of Hyperspectral and Quad-Polarization SAR Images to Classify Marsh Vegetation Using Stacking Ensemble Learning Algorithm." Remote Sensing 14, no. 21 (October 31, 2022): 5478. http://dx.doi.org/10.3390/rs14215478.
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Combinations of multi-sensor remote sensing images and machine learning have attracted much attention in recent years due to the spectral similarity of wetland plant canopy. However, the integration of hyperspectral and quad-polarization synthetic aperture radar (SAR) images for classifying marsh vegetation has still been faced with the challenges of using machine learning algorithms. To resolve this issue, this study proposed an approach to classifying marsh vegetation in the Honghe National Nature Reserve, northeast China, by combining backscattering coefficient and polarimetric decomposition parameters of C-band and L-band quad-polarization SAR data with hyperspectral images. We further developed an ensemble learning model by stacking Random Forest (RF), CatBoost and XGBoost algorithms for marsh vegetation mapping and evaluated its classification performance of marsh vegetation between combinations of hyperspectral and full-polarization SAR data and any of the lone sensor images. Finally, this paper explored the effect of different polarimetric decomposition methods and wavelengths of radar on classifying wetland vegetation. We found that a combination of ZH-1 hyperspectral images, C-band GF-3, and L-band ALOS-2 quad-polarization SAR images achieved the highest overall classification accuracy (93.13%), which was 5.58–9.01% higher than that only using C-band or L-band quad-polarization SAR images. This study confirmed that stacking ensemble learning provided better performance than a single machine learning model using multi-source images in most of the classification schemes, with the overall accuracy ranging from 77.02% to 92.27%. The CatBoost algorithm was capable of identifying forests and deep-water marsh vegetation. We further found that L-band ALOS-2 SAR images achieved higher classification accuracy when compared to C-band GF-3 polarimetric SAR data. ALOS-2 was more sensitive to deep-water marsh vegetation classification, while GF-3 was more sensitive to shallow-water marsh vegetation mapping. Finally, scattering model-based decomposition provided important polarimetric parameters from ALOS-2 SAR images for marsh vegetation classification, while eigenvector/eigenvalue-based and two-component decompositions produced a great contribution when using GF-3 SAR images.
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Yu, Peng, Wenxiang Xu, Xiaojing Zhong, Johnny A. Johannessen, Xiao-Hai Yan, Xupu Geng, Yuanrong He, and Wenfang Lu. "A Neural Network Method for Retrieving Sea Surface Wind Speed for C-Band SAR." Remote Sensing 14, no. 9 (May 8, 2022): 2269. http://dx.doi.org/10.3390/rs14092269.
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Based on the Ocean Projection and Extension neural Network (OPEN) method, a novel approach is proposed to retrieve sea surface wind speed for C-band synthetic aperture radar (SAR). In order to prove the methodology with a robust dataset, five-year normalized radar cross section (NRCS) measurements from the advanced scatterometer (ASCAT), a well-known side-looking radar sensor, are used to train the model. In situ wind data from direct buoy observations, instead of reanalysis wind data or model results, are used as the ground truth in the OPEN model. The model is applied to retrieve sea surface winds from two independent data sets, ASCAT and Sentinel-1 SAR data, and has been well-validated using buoy measurements from the National Oceanic and Atmospheric Administration (NOAA) and China Meteorological Administration (CMA), and the ASCAT coastal wind product. The comparison between the OPEN model and four C-band model (CMOD) versions (CMOD4, CMOD-IFR2, CMOD5.N, and CMOD7) further indicates the good performance of the proposed model for C-band SAR sensors. It is anticipated that the use of high-resolution SAR data together with the new wind speed retrieval method can provide continuous and accurate ocean wind products in the future.
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Matsuoka, Takeshi, Seiho Uratsuka, Makoto Satake, Tatsuharu Kobayashi, Akitsugu Nadai, Toshihiko Umehara, Hideo Maeno, Hiroyuki Wakabayashi, Kazuki Nakamura, and Fumihiko Nishio. "CRL/NASDA airborne SAR (Pi-SAR) observations of sea ice in the Sea of Okhotsk." Annals of Glaciology 33 (2001): 115–19. http://dx.doi.org/10.3189/172756401781818734.
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AbstractMulti-frequency, multi-polarization airborne synthetic aperture radar (SAR) observations of sea ice in the southern Sea of Okhotsk were carried out in February 1999 in conjunction with RADARSAT SAR observations. The final goal of this study is to clarify the backscattering characteristics and to understand the scattering mechanisms of sea ice in the Sea of Okhotsk by using microwave multiparametric SAR. The airborne SAR (Pi-SAR) has two frequencies (X- and L-band) and multi-polarization (HH, VV, HV, VH) with 1.5 m (X-band) and 3.0 m (L-band) resolution. It was developed by the Communications Research Laboratory (X-band) and the National Space Development Agency of Japan (L-band). We show the frequency dependence and polarization properties of radar backscattering from sea ice. We find that it is possible to distinguish ice types by comparing backscattering from sea ice in the X- and L-bands. Investigation of the polarization characteristics at X-band was very useful for detecting the thin-ice area (e.g. nilas and gray ice).
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Hosseiny, B., J. Amini, M. Esmaeilzade, and M. Nekoee. "RANGE MIGRATION ALGORITHM IN THE PROCESSING CHAIN OF SIGNALS OF A GROUND-BASED SAR SENSOR." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 521–25. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-521-2019.
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Abstract. Synthetic aperture radar (SAR) system based on frequency modulated continuous wave (FM-CW) transmission is a viable option for producing high-resolution ground-based imaging radars. Compared with pulsed SAR systems, the combination of FM-CW technology and SAR processing techniques have the advantages of small cubage, lightweight, cost-effectiveness, and high resolution in the SAR image. These characteristics make FM-CW SAR suitable to be deployed as payload on ground Based SARs (GB-SARs) for environmental and civilian applications. In this paper, the Range Migration Algorithm (RMA) is used in the processing chain of a Ground-Based SAR (GB-SAR) sensor. The mentioned sensor has been developed in Microwave Remote Sensing Laboratory (MReSL) at the School of Surveying and Geospatial Engineering, the University of Tehran for the generation of a complex image from the raw signal. The raw signal is acquired with that sensor working at S-band, frequency modulating from 2.26 GHz to 2.59 GHz.
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Molijn, Ramses, Lorenzo Iannini, Jansle Vieira Rocha, and Ramon Hanssen. "Sugarcane Productivity Mapping through C-Band and L-Band SAR and Optical Satellite Imagery." Remote Sensing 11, no. 9 (May 9, 2019): 1109. http://dx.doi.org/10.3390/rs11091109.
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Space-based remote sensing imagery can provide a valuable and cost-effective set of observations for mapping crop-productivity differences. The effectiveness of such signals is dependent on several conditions that are related to crop and sensor characteristics. In this paper, we present the dynamic behavior of signals from five Synthetic Aperture Radar (SAR) sensors and optical sensors with growing sugarcane, focusing on saturation effects and the influence of precipitation events. In addition, we analyzed the level of agreement within and between these spaceborne datasets over space and time. As a result, we produced a list of conditions during which the acquisition of satellite imagery is most effective for sugarcane productivity monitoring. For this, we analyzed remote sensing data from two C-band SAR (Sentinel-1 and Radarsat-2), one L-band SAR (ALOS-2), and two optical sensors (Landsat-8 and WorldView-2), in conjunction with detailed ground-reference data acquired over several sugarcane fields in the state of São Paulo, Brazil. We conclude that satellite imagery from L-band SAR and optical sensors is preferred for monitoring sugarcane biomass growth in time and space. Additionally, C-band SAR imagery offers the potential for mapping spatial variations during specific time windows and may be further exploited for its precipitation sensitivity.
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Lapaz Olveira, Adrián, Hernán Saínz Rozas, Mauricio Castro-Franco, Walter Carciochi, Luciana Nieto, Mónica Balzarini, Ignacio Ciampitti, and Nahuel Reussi Calvo. "Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data Fusion." Remote Sensing 15, no. 3 (February 1, 2023): 824. http://dx.doi.org/10.3390/rs15030824.
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Corn (Zea mays L.) nitrogen (N) management requires monitoring plant N concentration (Nc) with remote sensing tools to improve N use, increasing both profitability and sustainability. This work aims to predict the corn Nc during the growing cycle from Sentinel-2 and Sentinel-1 (C-SAR) sensor data fusion. Eleven experiments using five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1) were conducted in the Pampas region of Argentina. Plant samples were collected at four stages of vegetative and reproductive periods. Vegetation indices were calculated with new combinations of spectral bands, C-SAR backscatters, and sensor data fusion derived from Sentinel-1 and Sentinel-2. Predictive models of Nc with the best fit (R2 = 0.91) were calibrated with spectral band combinations and sensor data fusion in six experiments. During validation of the models in five experiments, sensor data fusion predicted corn Nc with lower error (MAPE: 14%, RMSE: 0.31 %Nc) than spectral band combination (MAPE: 20%, RMSE: 0.44 %Nc). The red-edge (704, 740, 740 nm), short-wave infrared (1375 nm) bands, and VV backscatter were all necessary to monitor corn Nc. Thus, satellite remote sensing via sensor data fusion is a critical data source for predicting changes in plant N status.
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Weinman, J. A., F. S. Marzano, W. J. Plant, A. Mugnai, and N. Pierdicca. "Rainfall observation from X-band, space-borne, synthetic aperture radar." Natural Hazards and Earth System Sciences 9, no. 1 (February 4, 2009): 77–84. http://dx.doi.org/10.5194/nhess-9-77-2009.
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Abstract. Satellites carrying X-band Synthetic Aperture Radars (SAR) have recently been launched by several countries. These provide new opportunities to measure precipitation with higher spatial resolution than has heretofore been possible. Two algorithms to retrieve precipitation from such measurements over land have been developed, and the retrieved rainfall distributions were found to be consistent. A maritime rainfall distribution obtained from dual frequency (X and C-band) data was used to compute the Differential Polarized Phase Shift. The computed Differential Polarized Phase Shift compared well with the value measured from space. Finally, we show a comparison between a recent X-band SAR image of a precipitation distribution and an observation of the same rainfall from ground-based operational weather radar. Although no quantitative comparison of retrieved and conventional rainfall distributions could be made with the available data at this time, the results presented here point the way to such comparisons.
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Baumann-Ouyang, Andreas, Jemil Avers Butt, Matej Varga, and Andreas Wieser. "MIMO-SAR Interferometric Measurements for Wind Turbine Tower Deformation Monitoring." Energies 16, no. 3 (February 3, 2023): 1518. http://dx.doi.org/10.3390/en16031518.
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Deformations affect the structural integrity of wind turbine towers. The health of such structures is thus assessed by monitoring. The majority of sensors used for this purpose are costly and require in situ installations. We investigated whether Multiple-Input Multiple-Output Synthetic Aperture Radar (MIMO-SAR) sensors can be used to monitor wind turbine towers. We used an automotive-grade, low-cost, off-the-shelf MIMO-SAR sensor operating in the W-band with an acquisition frequency of 100 Hz to derive Line-Of-Sight (LOS) deformation measurements in ranges up to about 175 m. Time series of displacement measurements for areas at different heights of the tower were analyzed and compared to reference measurements acquired by processing video camera recordings and total station measurements. The results showed movements in the range of up to 1 m at the top of the tower. We were able to detect the deformations also with the W-band MIMO-SAR sensor; for areas with sufficient radar backscattering, the results suggest a sub-mm noise level of the radar measurements and agreement with the reference measurements at the mm- to sub-mm level. We further applied Fourier transformation to detect the dominant vibration frequencies and identified values ranging from 0.17 to 24 Hz. The outcomes confirmed the potential of MIMO-SAR sensors for highly precise, cost-efficient, and time-efficient structural monitoring of wind turbine towers. The sensors are likely also applicable for monitoring other high-rise structures such as skyscrapers or chimneys.
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Zhao, J., and D. Floricioiu. "THE PENETRATION EFFECTS ON TANDEM-X ELEVATION USING THE GNSS AND LASER ALTIMETRY MEASUREMENTS IN ANTARCTICA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 14, 2017): 1593–600. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-1593-2017.
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Synthetic Aperture Radar (SAR) has been widely used in many different fields, such as geoscience, climate monitoring, security-related applications. However, over natural terrain the radar signal has the ability to penetrate the ground surface which can cause the bias in the elevation measurements. The aim of the paper is to assess the SAR signal penetration effect on the TanDEM-X absolute elevation over ice and snow covered areas and it presents the results concerning the X-band SAR signal penetration effect on dry snow areas and blue ice region. Additionally, the relationship between SAR signal penetration depth and backscattering coefficient is exploited and discussed. In this paper, two study sites, Schirmacher area and Recovery Ice Stream are selected and it is found that the general X-band SAR signal penetration depth is around 3&ndash;7 meter on dry snow area while no penetration depth is expected on the blue-ice region.
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Meyer, Franz J., Olaniyi A. Ajadi, and Edward J. Hoppe. "Studying the Applicability of X-Band SAR Data to the Network-Scale Mapping of Pavement Roughness on US Roads." Remote Sensing 12, no. 9 (May 9, 2020): 1507. http://dx.doi.org/10.3390/rs12091507.
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The traveling public judges the quality of a road mostly by its roughness and/or ride quality. Hence, mapping, monitoring, and maintaining adequate pavement smoothness is of high importance to State Departments of Transportation in the US. Current methods rely mostly on in situ measurements and are, therefore, time consuming and costly when applied at the network scale. This paper studies the applicability of satellite radar remote sensing data, specifically, high-resolution Synthetic Aperture Radar (SAR) data acquired at X-band, to the network-wide mapping of pavement roughness of roads in the US. Based on a comparison of high-resolution X-band Cosmo-SkyMed images with road roughness data in the form of International Roughness Index (IRI) measurements, we found that X-band radar brightness generally increases when pavement roughness worsens. Based on these findings, we developed and inverted a model to distinguish well maintained road segments from segments in need of repair. Over test sites in Augusta County, VA, we found that our classification scheme reaches an overall accuracy of 92.6%. This study illustrates the capacity of X-band SAR for pavement roughness mapping and suggests that incorporating SAR into DOT operations could be beneficial.
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Abdikan, Saygin, Caglar Bayik, Aliihsan Sekertekin, Filiz Bektas Balcik, Sadra Karimzadeh, Masashi Matsuoka, and Fusun Balik Sanli. "Burned Area Detection Using Multi-Sensor SAR, Optical, and Thermal Data in Mediterranean Pine Forest." Forests 13, no. 2 (February 18, 2022): 347. http://dx.doi.org/10.3390/f13020347.
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Burned area (BA) mapping of a forest after a fire is required for its management and the determination of the impacts on ecosystems. Different remote sensing sensors and their combinations have been used due to their individual limitations for accurate BA mapping. This study analyzes the contribution of different features derived from optical, thermal, and Synthetic Aperture Radar (SAR) images to extract BA information from the Turkish red pine (Pinus brutia Ten.) forest in a Mediterranean ecosystem. In addition to reflectance values of the optical images, Normalized Burn Ratio (NBR) and Land Surface Temperature (LST) data are produced from both Sentinel-2 and Landsat-8 data. The backscatter of C-band Sentinel-1 and L-band ALOS-2 SAR images and the coherence feature derived from the Interferometric SAR technique were also used. The pixel-based random forest image classification method is applied to classify the BA detection in 24 scenarios created using these features. The results show that the L-band data provided a better contribution than C-band data and the combination of features created from Landsat LST, NBR, and coherence of L-band ALOS-2 achieved the highest accuracy, with an overall accuracy of 96% and a Kappa coefficient of 92.62%.