Improving Sea-Surface Remote Sensing of Ocean Wind Vectors by Scatterometers

Though scatterometers have been used to sense global ocean surface wind vectors for over 40 years, there remain some significant shortcomings. The largest problems appear in retrieving the wind vector when the ocean is being driven by high wind speeds or when rain is present in the beam-illuminated volume. Geophysical model functions (GMFs) developed using data from high-wind events can improve retrievals at high wind speeds, but only if sufficient ground truth measurements exist in the scatterometer swath. Airborne scatterometers, such as the Imaging Wind and Rain Airborne Profiler (IWRAP) developed by the Microwave Remote Sensing Laboratory (MIRSL) at the University of Massachusetts Amherst (UMass), are well-suited for collecting such high-wind data, largely due to their abilities to reposition to areas of interest, sample the ocean surface on a small scale, and use complementary in-situ sensors. The IWRAP system is also able to investigate the effect of precipitation impact (the “splash effect”) on the sea surface normalized radar cross-section (NRCS), since it can discriminate between volume and surface effects of precipitation. This dissertation will improve upon the existing IWRAP GMF and quantify the effect of precipitation on wind vector retrievals. Additionally, IWRAP is used to observe the effects of Earth-incidence angle and polarization on the sea-surface radar backscatter, helping scatterometer GMFs to be applicable to other satellite sensors. IWRAP and collocated Stepped Frequency Microwave Radiometer (SFMR) data were gathered from 4 years of flight experiments. Using this data, the high-wind IWRAP GMF is extended to incidence angles near 22° at C- and Ku-band VV- and HH-polarization from 15 m s−1 to 45 m s−1. There is also a revision made to the higher harmonics of the GMF near 50° incidence, but the mean NRCS appears to be modeled appropriately. There is no splash effect observed in the mean NRCS or first harmonic at wind speeds from 15 m s−1 to 45 m s−1. The second harmonic shows some muted behavior in precipitation. Lastly, a wind speed dependence is observed in the VV/HH NRCS polarization ratio in both incidence angle and azimuth

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’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 book—Progress in SAR Oceanography—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 objects’ 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

Wind speed retrieval from the Gaofen-3 synthetic aperture radar for VV- and HH-polarization using a re-tuned algorithm

In this study, a re-tuned algorithm based on the geophysical model function (GMF) C-SARMOD2 is proposed to retrieve wind speed from Synthetic Aperture Radar (SAR) imagery collected by the Chinese C-band Gaofen-3 (GF-3) SAR. More than 10,000 Vertical-Vertical (VV) and Horizontal-Horizontal (HH) polarization GF-3 images acquired in quad-polarization stripmap (QPS) and wave (WV) modes have been collected during the last three years, in which wind patterns are observed over open seas with incidence angles ranging from 18° to 52°. These images, collocated with wind vectors from the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis at 0.125° resolution, are used to re-tune the C-SARMOD2 algorithm to specialize it for the GF-3 SAR (CSARMOD-GF). In particular, the CSARMOD-GF performs differently from the C-SARMOD2 at low-to-moderate incidence angles smaller than about 34°. Comparisons with wind speed data from the Advanced Scatterometer (ASCAT), Chinese Haiyang-2B (HY-2B) and buoys from the National Data Buoy Center (NDBC) show that the root-mean-square error (RMSE) of the retrieved wind speed is approximately 1.8 m/s. Additionally, the CSARMOD-GF algorithm outperforms three state-of-the-art methods – C-SARMOD, C-SARMOD2, and CMOD7 – that, when applied to GF-3 SAR imagery, generating a RMSE of approximately 2.0–2.4 m/s

Challenges to Satellite Sensors of Ocean Winds: Addressing Precipitation Effects

Measurements of global ocean surface winds made by orbiting satellite radars have provided valuable information to the oceanographic and meteorological communities since the launch of the Seasat in 1978, by the National Aeronautics and Space Administration (NASA). When Quick Scatterometer (QuikSCAT) was launched in 1999, it ushered in a new era of dual-polarized, pencil-beam, higher-resolution scatterometers for measuring the global ocean surface winds from space. A constant limitation on the full utilization of scatterometer-derived winds is the presence of isolated rain events, which affect about 7% of the observations. The vector wind sensors, the Ku-band scatterometers [NASA\u27s SeaWinds on the QuikSCAT and Midori-II platforms and Indian Space Research Organisation\u27s (ISRO\u27s) Ocean Satellite (Oceansat)-2], and the current C-band scatterometer [Advanced Wind Scatterometer (ASCAT), on the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)\u27s Meteorological Operation (MetOp) platform] all experience rain interference, but with different characteristics. Over this past decade, broad-based research studies have sought to better understand the physics of the rain interference problem, to search for methods to bypass the problem (using rain detection, flagging, and avoidance of affected areas), and to develop techniques to improve the quality of the derived wind vectors that are adversely affected by rain. This paper reviews the state of the art in rain flagging and rain correction and describes many of these approaches, methodologies, and summarizes the results

한반도 주변해 연안 해양현상에 대한 합성개구레이더 활용

학위논문 (박사)-- 서울대학교 대학원 : 과학교육과 (지구과학전공), 2016. 8. 박경애.In this thesis, the applicability of synthetic aperture radar (SAR) to interpretation of oceanic phenomena at the coastal regions around Korea peninsula is presented. For that, the spatial and temporal variations of SAR-derived coastal wind fields and evolution of disastrous oil spills on SAR images were analyzed in relation to atmospheric and oceanic environmental factors using in-situ measurement and satellite observations. The SAR wind fields retrieved from the east coast of Korea in August 2007 during the upwelling period revealed a spatial distinction between near and offshore regions. Low wind speeds were associated with cold water regions with dominant coastal upwelling. Time series of in-situ measurements of both wind speed and water temperature indicated that the upwelling was induced by the wind field. SAR data at the present upwelling region showed a relatively large backscattering attenuation to SST ratio of 1.2 dB ºC−1 compared the known dependence of the water viscosity on the radar backscattering. In addition, wind speed magnitude showed a positive correlation with the difference between SST and air temperature. It implies that the low wind field from SAR was mainly induced by changes in atmospheric stability due to air-sea temperature differences. Oil spills at the Hebei Spirit accident off the coast of Korea in the Yellow Sea were identified using SAR data and their evolution was investigated. To quantitatively analyze the spatial and temporal variations of oil spills, objective detection methods based on adaptive thresholding and a neural network were applied. Prior to applying, the results from two methods were compared for verification. It showed good agreement enough for the estimation of the extent of oil patches and their trajectories, with the exception of negligible errors at the boundaries. Quantitative analyses presented that the detected oil slicks moved southeastward, corresponding to the prevailing wind and tidal currents, and gradually dissipated during the spill, except for an extraordinary rapid decrease in onshore regions at the initial stage. It was identified that the initial dissipation of the spilt oil was induced by strong tidal mixing in the tidal front zone from comparison with the tidal mixing index. The spatial and temporal variations of the oil slicks confirmed the influence of atmospheric and oceanic environmental factors. The overall horizontal migration of the oil spills detected from consecutive SAR images was mainly driven by Ekman drift during the winter monsoon rather than the tidal residual current.Chapter 1. Introduction 1 1.1. Study Background 1 1.2. Objectives of the Thesis 14 Chapter 2. Data Description 15 2.1. SAR Data 15 2.2. Other Satellite Data 21 2.2.1. Wind Data 21 2.2.2. Sea Surface Temperature Data 21 2.2.3. Ocean Color Data 22 2.3. Reanalysis Data 23 2.4. In-situ Measurements 23 2.5. Land Masking Data 26 2.6. Tidal Current Data 28 Chapter 3. Methods 29 3.1. SAR Wind Retrieval 29 3.2. Noise Reduction of ScanSAR Images 37 3.3. Conversion of Wind Speed to Neutral Wind 41 3.4. Estimation of Index of the Tidal Front 43 3.5. Estimation of Ekman Drift and Tidal Residual Current 45 3.6. Feature Detection Methods 46 3.6.1. Adaptive Threshold Method 47 3.6.2. Bimodal Histogram Method 50 3.6.3. Neural Network Method 54 Chapter 4. Coastal Wind Fields and Upwelling Response 58 4.1. Variations of Wind Fields during Coastal Upwelling 58 4.2. Stability Effect on Wind Speed 65 4.3. Biological Impact of Upwelling 70 Chapter 5. Characteristics of Objective Feature Detection 74 5.1. Comparison of Thresholding Methods 74 5.2. Oil Spill of the Hebei Spirit by Thresholding Method 81 5.3. Oil Spill by the Hebei Spirit by Neural Network Method 85 5.4. Differences by Detection Methods 88 Chapter 6. Evolution of Oil Spill at the Coastal Region 90 6.1. Temporal Evolution of the Hebei Spirit Oil Spill 90 6.2. Effect of Artificial Factor on the Evolution 96 Chapter 7. Effect of Environmental Factors on the Oil Spill 98 7.1. Effect of Tidal Mixing 98 7.2. Effect of Wind and Tidal Current 103 Chapter 8. Summary and Conclusion 110 Reference 114 Abstract in Korean 142Docto

Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

Theoretical modeling of dual-frequency scatterometer response: improving ocean wind and rainfall effects

Ocean surface wind is a key parameter of the Earth’s climate system. Occurring at the interface between the ocean and the atmosphere, ocean winds modulate fluxes of heat, moisture and gas exchanges. They reflect the lower branch of the atmospheric circulation and represent a major driver of the ocean circulation. Studying the long-term trends and variability of the ocean surface winds is of key importance in our effort to understand the Earth’s climate system and the causes of its changes. More than three decades of surface wind data are available from spaceborne scatterometer/radiometer missions and there is an ongoing effort to inter-calibrate all these measurements with the aim of building a complete and continuous picture of the ocean wind variability. Currently, spaceborne scatterometer wind retrievals are obtained by inversion algorithms of empirical Geophysical Model Functions (GMFs), which represent the relationship between ocean surface backscattering coefficient and the wind parameters. However, by being measurement-dependent, the GMFs are sensor-specific and, in addition, they may be not properly defined in all weather conditions. This may reduce the accuracy of the wind retrievals in presence of rain and it may also lead to inconsistencies amongst winds retrieved by different sensors. Theoretical models of ocean backscatter have the big potential of providing a more general and understandable relation between the measured microwave backscatter and the surface wind field than empirical models. Therefore, the goal of our research is to understand and address the limitations of the theoretical modeling, in order to propose a new strategy towards the definition of a unified theoretical model able to account for the effects of both wind and rain. In this work, it is described our approach to improve the theoretical modeling of the ocean response, starting from the Ku-band (13.4 GHz) frequency and then broadening the analysis at C-band (5.3 GHz) frequency. This research has revealed the need for new understanding of the frequency-dependent modeling of the surface backscatter in response to the wind-forced surface wave spectrum. Moreover, our ocean wave spectrum modification introduced to include the influences of the surface rain, allows the interpretation/investigation of the scatterometer observations in terms not only of the surface winds but also of the surface rain, defining an additional step needed to improve the wind retrievals algorithms as well as the possibility to jointly estimate wind and rain from scatterometer observations