224 research outputs found

    First results from the GPS atmosphere sounding experiment TOR aboard the TerraSAR-X satellite

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    GPS radio occultation events observed between 24 July and 17 November 2008 by the IGOR occultation receiver aboard the TerraSAR-X satellite are processed and analyzed. The comparison of 15 327 refractivity profiles with collocated ECMWF data yield a mean bias between zero and −0.30 % at altitudes between 5 and 30 km. Standard deviations decrease from about 1.4 % at 5 km to about 0.6 % at 10 km altitude, however, increase significantly in the upper stratosphere. At low latitudes mean biases and standard deviations are larger, in particular in the lower troposphere. The results are consistent with 15 159 refractivity observations collected during the same time period by the BlackJack receiver aboard GRACE-A and processed by GFZ's operational processing system. The main difference between the two occultation instruments is the implementation of open-loop signal tracking in the IGOR (TerraSAR-X) receiver which improves the tropospheric penetration depth in terms of ray height by about 2 km compared to the conventional closed-loop data acquired by BlackJack (GRACE-A)

    ย Ocean Remote Sensing with Synthetic Aperture Radar

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    The ocean covers approximately 71% of the Earthโ€™s surface, 90% of the biosphere and contains 97% of Earthโ€™s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This bookโ€”Progress in SAR Oceanographyโ€”provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objectsโ€™ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

    On Small Satellites for Oceanography: A Survey

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    The recent explosive growth of small satellite operations driven primarily from an academic or pedagogical need, has demonstrated the viability of commercial-off-the-shelf technologies in space. They have also leveraged and shown the need for development of compatible sensors primarily aimed for Earth observation tasks including monitoring terrestrial domains, communications and engineering tests. However, one domain that these platforms have not yet made substantial inroads into, is in the ocean sciences. Remote sensing has long been within the repertoire of tools for oceanographers to study dynamic large scale physical phenomena, such as gyres and fronts, bio-geochemical process transport, primary productivity and process studies in the coastal ocean. We argue that the time has come for micro and nano satellites (with mass smaller than 100 kg and 2 to 3 year development times) designed, built, tested and flown by academic departments, for coordinated observations with robotic assets in situ. We do so primarily by surveying SmallSat missions oriented towards ocean observations in the recent past, and in doing so, we update the current knowledge about what is feasible in the rapidly evolving field of platforms and sensors for this domain. We conclude by proposing a set of candidate ocean observing missions with an emphasis on radar-based observations, with a focus on Synthetic Aperture Radar.Comment: 63 pages, 4 figures, 8 table

    A novel satellite mission concept for upper air water vapour, aerosol and cloud observations using integrated path differential absorption LiDAR limb sounding

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    We propose a new satellite mission to deliver high quality measurements of upper air water vapour. The concept centres around a LiDAR in limb sounding by occultation geometry, designed to operate as a very long path system for differential absorption measurements. We present a preliminary performance analysis with a system sized to send 75 mJ pulses at 25 Hz at four wavelengths close to 935 nm, to up to 5 microsatellites in a counter-rotating orbit, carrying retroreflectors characterized by a reflected beam divergence of roughly twice the emitted laser beam divergence of 15 ยตrad. This provides water vapour profiles with a vertical sampling of 110 m; preliminary calculations suggest that the system could detect concentrations of less than 5 ppm. A secondary payload of a fairly conventional medium resolution multispectral radiometer allows wide-swath cloud and aerosol imaging. The total weight and power of the system are estimated at 3 tons and 2,700 W respectively. This novel concept presents significant challenges, including the performance of the lasers in space, the tracking between the main spacecraft and the retroreflectors, the refractive effects of turbulence, and the design of the telescopes to achieve a high signal-to-noise ratio for the high precision measurements. The mission concept was conceived at the Alpbach Summer School 2010

    Multi-dimensional characterization of soil surface roughness for microwave remote sensing applications

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    Towards Retrieving Reliable Ocean Surface Currents in the Coastal Zone From the Sentinel-1 Doppler Shift Observations

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    Recent developments on calibration and partitioning of the signal between the wave and current contributions significantly improve the accuracy of geophysical retrievals from Sentinel-1 Synthetic Aperture Radar-based Doppler shift measurements in the open ocean. In this study, we revise the Sentinel-1B Interferometric Wide products acquired from December 2017 to January 2018 along the coastal zone of northern Norway. We find that the satellite attitude is responsible for 30% of the variation in the Doppler shift observations, while the antenna pattern can describe an additional 15%. The residual variation after recalibration is about 3.8 Hz, corresponding to 0.21โ€“0.15 m/s radial velocity (RVL) depending on the incidence angle. Using recalibrated Sentinel-1 observations, collocated with near-surface wind from MetCoOp-Ensemble Prediction System and sea state from MyWaveWAM, we develop an empirical function (CDOP3SiX) for estimating the sea-state-induced Doppler shift. CDOP3SiX improves the accuracy of sea state contribution estimates under mixed wind fetch conditions and demonstrates that the Norwegian Coastal Current can be detected in the Sentinel-1 derived ocean surface current RVL maps. Moreover, two anticyclonic mesoscale eddies with radial velocities of about 0.5 m/s are detected. The surface current patterns are consistent with the collocated sea surface temperature observations. The Doppler shift observations from Sentinel-1 can therefore be used to study ocean surface currents in the coastal zone with a 1.5 km spatial resolution. Key Points The Sentinel-1 Doppler shift observations are used to retrieve information about the ocean surface currents in the coastal zone Mesoscale eddies are detected in the Synthetic Aperture Radar-derived ocean surface current radial velocity fields Combination of the wind and wave information from collocated models improves the accuracy of the wave-induced contribution estimates Plain Language Summary Knowledge of ocean surface currents is crucial for studies of volume, heat and salt transport, tracking pollutants, and fisheries. The Doppler shift from Sentinel-1 Synthetic Aperture Radar (SAR) observations can be used to retrieve information about ocean surface currents. Challenging calibration and lack of algorithms for separating the wave and current contributions have limited the application of this observation-based method. Recent developments on calibration showed promising improvements in the accuracy of the signal. In this study, we apply this recent calibration method to Sentinel-1B scenes and develop an algorithm applicable for the challenging conditions in the coastal zone. We found that the signal from the Norwegian Coastal Current can be detected in the Sentinel-1 derived ocean surface current radial velocity fields. Also, we demonstrated the potential of SAR data for observing eddies with diameter of about 40โ€“70 km. The Sentinel-1 derived surface currents express meandering structures and boundaries in consistence with the satellite-based sea surface temperature field. Comparison with the ocean model also reveals reasonable agreement, especially for the major surface current features. Therefore, given accurate calibration and new algorithm for removal of the wind and wave contribution, the Sentinel-1 observations can be used for monitoring ocean surface currents in the coastal zone with high spatial resolution.publishedVersio

    Parameters affecting interferometric coherence and implications for long-term operational monitoring of mining-induced surface deformation

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    Includes abstract.Includes bibliographical references.Surface deformation due to underground mining poses risks to health and safety as well as infrastructure and the environment. Consequently, the need for long-term operational monitoring systems exists. Traditional field-based measurements are point-based meaning that the full extent of deforming areas is poorly understood. Field-based techniques are also labour intensive if large areas are to be monitored on a regular basis. To overcome these limitations, this investigation considered traditional and advanced differential radar interferometry techniques for their ability to monitor large areas over time, remotely. An area known to be experiencing mining induced surface deformation was used as test case. The agricultural nature of the area implied that signal decorrelation effects were expected. Consequently, four sources of data, captured at three wavelengths by earth-orbiting satellites were obtained. This provided the opportunity to investigate different phase decorrelation effects on data from standard imaging platforms using real-world deformation phenomenon as test-case. The data were processed using standard dInSAR and polInSAR techniques. The deformation measurement results together with an analysis of parameters most detrimental to long-term monitoring were presented. The results revealed that, contrary to the hypothesis, polInSAR techniques did not provide an enhanced ability to monitor surface deformation compared to dInSAR techniques. Although significant improvements in coherence values were obtained, the spatial heterogeneity of phase measurements could not be improved. Consequently, polInSAR could not overcome ecorrelation associated with vegetation cover and evolving land surfaces. However, polarimetric information could be used to assess the scattering behaviour of the surface, thereby guiding the definition of optimal sensor configuration for long-term monitoring. Despite temporal and geometric decorrelation, the results presented demonstrated that mining-induced deformation could be measured and monitored using dInSAR techniques. Large areas could be monitored remotely and the areal extent of deforming areas could be assessed, effectively overcoming the limitations of field-based techniques. Consequently, guidelines for the optimal sensor configuration and image acquisition strategy for long-term operational monitoring of mining-induced surface deformation were provided

    ํ›ˆ๋ จ ์ž๋ฃŒ ์ž๋™ ์ถ”์ถœ ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ๊ธฐ๊ณ„ ํ•™์Šต์„ ํ†ตํ•œ SAR ์˜์ƒ ๊ธฐ๋ฐ˜์˜ ์„ ๋ฐ• ํƒ์ง€

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ์ง€๊ตฌํ™˜๊ฒฝ๊ณผํ•™๋ถ€, 2021. 2. ๊น€๋•์ง„.Detection and surveillance of vessels are regarded as a crucial application of SAR for their contribution to the preservation of marine resources and the assurance on maritime safety. Introduction of machine learning to vessel detection significantly enhanced the performance and efficiency of the detection, but a substantial majority of studies focused on modifying the object detector algorithm. As the fundamental enhancement of the detection performance would be nearly impossible without accurate training data of vessels, this study implemented AIS information containing real-time information of vesselโ€™s movement in order to propose a robust algorithm which acquires the training data of vessels in an automated manner. As AIS information was irregularly and discretely obtained, the exact target interpolation time for each vessel was precisely determined, followed by the implementation of Kalman filter, which mitigates the measurement error of AIS sensor. In addition, as the velocity of each vessel renders an imprint inside the SAR image named as Doppler frequency shift, it was calibrated by restoring the elliptic satellite orbit from the satellite state vector and estimating the distance between the satellite and the target vessel. From the calibrated position of the AIS sensor inside the corresponding SAR image, training data was directly obtained via internal allocation of the AIS sensor in each vessel. For fishing boats, separate information system named as VPASS was applied for the identical procedure of training data retrieval. Training data of vessels obtained via the automated training data procurement algorithm was evaluated by a conventional object detector, for three detection evaluating parameters: precision, recall and F1 score. All three evaluation parameters from the proposed training data acquisition significantly exceeded that from the manual acquisition. The major difference between two training datasets was demonstrated in the inshore regions and in the vicinity of strong scattering vessels in which land artifacts, ships and the ghost signals derived from them were indiscernible by visual inspection. This study additionally introduced a possibility of resolving the unclassified usage of each vessel by comparing AIS information with the accurate vessel detection results.์ „์ฒœํ›„ ์ง€๊ตฌ ๊ด€์ธก ์œ„์„ฑ์ธ SAR๋ฅผ ํ†ตํ•œ ์„ ๋ฐ• ํƒ์ง€๋Š” ํ•ด์–‘ ์ž์›์˜ ํ™•๋ณด์™€ ํ•ด์ƒ ์•ˆ์ „ ๋ณด์žฅ์— ๋งค์šฐ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค. ๊ธฐ๊ณ„ ํ•™์Šต ๊ธฐ๋ฒ•์˜ ๋„์ž…์œผ๋กœ ์ธํ•ด ์„ ๋ฐ•์„ ๋น„๋กฏํ•œ ์‚ฌ๋ฌผ ํƒ์ง€์˜ ์ •ํ™•๋„ ๋ฐ ํšจ์œจ์„ฑ์ด ํ–ฅ์ƒ๋˜์—ˆ์œผ๋‚˜, ์ด์™€ ๊ด€๋ จ๋œ ๋‹ค์ˆ˜์˜ ์—ฐ๊ตฌ๋Š” ํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๊ฐœ๋Ÿ‰์— ์ง‘์ค‘๋˜์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ํƒ์ง€ ์ •ํ™•๋„์˜ ๊ทผ๋ณธ์ ์ธ ํ–ฅ์ƒ์€ ์ •๋ฐ€ํ•˜๊ฒŒ ์ทจ๋“๋œ ๋Œ€๋Ÿ‰์˜ ํ›ˆ๋ จ์ž๋ฃŒ ์—†์ด๋Š” ๋ถˆ๊ฐ€๋Šฅํ•˜๊ธฐ์—, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์„ ๋ฐ•์˜ ์‹ค์‹œ๊ฐ„ ์œ„์น˜, ์†๋„ ์ •๋ณด์ธ AIS ์ž๋ฃŒ๋ฅผ ์ด์šฉํ•˜์—ฌ ์ธ๊ณต ์ง€๋Šฅ ๊ธฐ๋ฐ˜์˜ ์„ ๋ฐ• ํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ์‚ฌ์šฉ๋  ํ›ˆ๋ จ์ž๋ฃŒ๋ฅผ ์ž๋™์ ์œผ๋กœ ์ทจ๋“ํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์ด์‚ฐ์ ์ธ AIS ์ž๋ฃŒ๋ฅผ SAR ์˜์ƒ์˜ ์ทจ๋“์‹œ๊ฐ์— ๋งž์ถ”์–ด ์ •ํ™•ํ•˜๊ฒŒ ๋ณด๊ฐ„ํ•˜๊ณ , AIS ์„ผ์„œ ์ž์ฒด๊ฐ€ ๊ฐ€์ง€๋Š” ์˜ค์ฐจ๋ฅผ ์ตœ์†Œํ™”ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์ด๋™ํ•˜๋Š” ์‚ฐ๋ž€์ฒด์˜ ์‹œ์„  ์†๋„๋กœ ์ธํ•ด ๋ฐœ์ƒํ•˜๋Š” ๋„ํ”Œ๋Ÿฌ ํŽธ์ด ํšจ๊ณผ๋ฅผ ๋ณด์ •ํ•˜๊ธฐ ์œ„ํ•ด SAR ์œ„์„ฑ์˜ ์ƒํƒœ ๋ฒกํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ์œ„์„ฑ๊ณผ ์‚ฐ๋ž€์ฒด ์‚ฌ์ด์˜ ๊ฑฐ๋ฆฌ๋ฅผ ์ •๋ฐ€ํ•˜๊ฒŒ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. ์ด๋ ‡๊ฒŒ ๊ณ„์‚ฐ๋œ AIS ์„ผ์„œ์˜ ์˜์ƒ ๋‚ด์˜ ์œ„์น˜๋กœ๋ถ€ํ„ฐ ์„ ๋ฐ• ๋‚ด AIS ์„ผ์„œ์˜ ๋ฐฐ์น˜๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ์„ ๋ฐ• ํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ํ›ˆ๋ จ์ž๋ฃŒ ํ˜•์‹์— ๋งž์ถ”์–ด ํ›ˆ๋ จ์ž๋ฃŒ๋ฅผ ์ทจ๋“ํ•˜๊ณ , ์–ด์„ ์— ๋Œ€ํ•œ ์œ„์น˜, ์†๋„ ์ •๋ณด์ธ VPASS ์ž๋ฃŒ ์—ญ์‹œ ์œ ์‚ฌํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ ๊ฐ€๊ณตํ•˜์—ฌ ํ›ˆ๋ จ์ž๋ฃŒ๋ฅผ ์ทจ๋“ํ•˜์˜€๋‹ค. AIS ์ž๋ฃŒ๋กœ๋ถ€ํ„ฐ ์ทจ๋“ํ•œ ํ›ˆ๋ จ์ž๋ฃŒ๋Š” ๊ธฐ์กด ๋ฐฉ๋ฒ•๋Œ€๋กœ ์ˆ˜๋™ ์ทจ๋“ํ•œ ํ›ˆ๋ จ์ž๋ฃŒ์™€ ํ•จ๊ป˜ ์ธ๊ณต ์ง€๋Šฅ ๊ธฐ๋ฐ˜ ์‚ฌ๋ฌผ ํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ํ†ตํ•ด ์ •ํ™•๋„๋ฅผ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ์ œ์‹œ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์œผ๋กœ ์ทจ๋“ํ•œ ํ›ˆ๋ จ ์ž๋ฃŒ๋Š” ์ˆ˜๋™ ์ทจ๋“ํ•œ ํ›ˆ๋ จ ์ž๋ฃŒ ๋Œ€๋น„ ๋” ๋†’์€ ํƒ์ง€ ์ •ํ™•๋„๋ฅผ ๋ณด์˜€์œผ๋ฉฐ, ์ด๋Š” ๊ธฐ์กด์˜ ์‚ฌ๋ฌผ ํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ํ‰๊ฐ€ ์ง€ํ‘œ์ธ ์ •๋ฐ€๋„, ์žฌํ˜„์œจ๊ณผ F1 score๋ฅผ ํ†ตํ•ด ์ง„ํ–‰๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•œ ํ›ˆ๋ จ์ž๋ฃŒ ์ž๋™ ์ทจ๋“ ๊ธฐ๋ฒ•์œผ๋กœ ์–ป์€ ์„ ๋ฐ•์— ๋Œ€ํ•œ ํ›ˆ๋ จ์ž๋ฃŒ๋Š” ํŠนํžˆ ๊ธฐ์กด์˜ ์„ ๋ฐ• ํƒ์ง€ ๊ธฐ๋ฒ•์œผ๋กœ๋Š” ๋ถ„๋ณ„์ด ์–ด๋ ค์› ๋˜ ํ•ญ๋งŒ์— ์ธ์ ‘ํ•œ ์„ ๋ฐ•๊ณผ ์‚ฐ๋ž€์ฒด ์ฃผ๋ณ€์˜ ์‹ ํ˜ธ์— ๋Œ€ํ•œ ์ •ํ™•ํ•œ ๋ถ„๋ณ„ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด์™€ ํ•จ๊ป˜, ์„ ๋ฐ• ํƒ์ง€ ๊ฒฐ๊ณผ์™€ ํ•ด๋‹น ์ง€์—ญ์— ๋Œ€ํ•œ AIS ๋ฐ VPASS ์ž๋ฃŒ๋ฅผ ์ด์šฉํ•˜์—ฌ ์„ ๋ฐ•์˜ ๋ฏธ์‹๋ณ„์„ฑ์„ ํŒ์ •ํ•  ์ˆ˜ ์žˆ๋Š” ๊ฐ€๋Šฅ์„ฑ ๋˜ํ•œ ์ œ์‹œํ•˜์˜€๋‹ค.Chapter 1. Introduction - 1 - 1.1 Research Background - 1 - 1.2 Research Objective - 8 - Chapter 2. Data Acquisition - 10 - 2.1 Acquisition of SAR Image Data - 10 - 2.2 Acquisition of AIS and VPASS Information - 20 - Chapter 3. Methodology on Training Data Procurement - 26 - 3.1 Interpolation of Discrete AIS Data - 29 - 3.1.1 Estimation of Target Interpolation Time for Vessels - 29 - 3.1.2 Application of Kalman Filter to AIS Data - 34 - 3.2 Doppler Frequency Shift Correction - 40 - 3.2.1 Theoretical Basis of Doppler Frequency Shift - 40 - 3.2.2 Mitigation of Doppler Frequency Shift - 48 - 3.3 Retrieval of Training Data of Vessels - 53 - 3.4 Algorithm on Vessel Training Data Acquisition from VPASS Information - 61 - Chapter 4. Methodology on Object Detection Architecture - 66 - Chapter 5. Results - 74 - 5.1 Assessment on Training Data - 74 - 5.2 Assessment on AIS-based Ship Detection - 79 - 5.3 Assessment on VPASS-based Fishing Boat Detection - 91 - Chapter 6. Discussions - 110 - 6.1 Discussion on AIS-Based Ship Detection - 110 - 6.2 Application on Determining Unclassified Vessels - 116 - Chapter 7. Conclusion - 125 - ๊ตญ๋ฌธ ์š”์•ฝ๋ฌธ - 128 - Bibliography - 130 -Maste
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