Estimation of Ocean Surface Currents from Maximum Cross Correlation applied to GOCI geostationary satellite remote sensing data over the Tsushima (Korea) Straits

Attempts to automatically estimate surface current velocities from satellite-derived thermal or visible imagery face the limitations of data occlusion due to cloud cover, the complex evolution of features and the degradation of their surface signature. The Geostationary Ocean Color Imager (GOCI) provides a chance to reappraise such techniques due to its multi-year record of hourly high-resolution visible spectrum data. Here we present the results of applying a Maximum Cross Correlation (MCC) technique to GOCI data. Using a combination of simulated and real data we derive suitable processing parameters and examine the robustness of different satellite products, those being water-leaving radiance and chlorophyll concentration. These estimates of surface currents are evaluated using High Frequency (HF) radar systems located in the Tsushima (Korea) Strait. We show the performance of the MCC approach varies depending on the amount of missing data and the presence of strong optical contrasts. Using simulated data it was found that patchy cloud cover occupying 25% of the image pair reduces the number of vectors by 20% compared to using perfect images. Root mean square errors between the MCC and HF radar velocities are of the order of 20 cm s−1. Performance varies depending on the wavelength of the data with the blue-green products out-performing the red and near infra-red products. Application of MCC to GOCI chlorophyll data results in similar performance to radiances in the blue-green bands. The technique has been demonstrated using specific examples of an eddy feature and tidal induced features in the region. This article is protected by copyright. All rights reserved

Sensor capability and atmospheric correction in ocean colour remote sensing

© 2015 by the authors; licensee MDPI, Basel, Switzerland. Accurate correction of the corrupting effects of the atmosphere and the water's surface are essential in order to obtain the optical, biological and biogeochemical properties of the water from satellite-based multi-and hyper-spectral sensors. The major challenges now for atmospheric correction are the conditions of turbid coastal and inland waters and areas in which there are strongly-absorbing aerosols. Here, we outline how these issues can be addressed, with a focus on the potential of new sensor technologies and the opportunities for the development of novel algorithms and aerosol models. We review hardware developments, which will provide qualitative and quantitative increases in spectral, spatial, radiometric and temporal data of the Earth, as well as measurements from other sources, such as the Aerosol Robotic Network for Ocean Color (AERONET-OC) stations, bio-optical sensors on Argo (Bio-Argo) floats and polarimeters. We provide an overview of the state of the art in atmospheric correction algorithms, highlight recent advances and discuss the possible potential for hyperspectral data to address the current challenges

천리안 해양위성의 대기보정 및 대리교정 연구

본 학위논문은 세계최초의 정지궤도 해색 위성인 천리안 해양 위성 (GOCI : Geostationary Ocean Color Imager)에 표준으로 사용되는 대기보정 이론에 대하여 기술하고 있다. 타 극궤도 해색위성들이 1~2일 주기로 한 장소를 방문하며 전 지구를 관측하는 것과 달리 천리안 해양위성은 한반도를 포함한 동북아해역을 0.5 km 공간해상도로 낮 시간 동안 1시간의 시간간격으로 관측하고 있으며 (하루 8회 관측) 가시광~근적외파장대 (412, 443, 490, 555, 660, 680, 745, 865 nm) 영역에서 관측한다. 대기상층 위성궤도에서 일반적인 맑은 해역을 대상으로 관측된 가시광~근적외파장대 신호 중 90%이상은 대기신호이며, 해수신호의 크기는 10% 미만을 차지한다. 대기신호의 크기가 해수신호의 크기보다 10배 이상 크기 때문에 1%의 대기신호 추정 오차는 10%이상의 해수 광 스펙트럼 추정오류를 일으킨다. 이런 이유로 위성을 통한 해색원격탐사 임무는 높은 대기보정 정밀도를 요구하고 있으며 대기보정의 개발이 해색원격탐사 알고리즘 개발 중 가장 핵심이 된다. 천리안 해양위성 표준 대기보정은 NASA가 해색원격탐사 임무를 위해 개발한 SeaWiFS 표준 대기보정에 이론적인 기반을 두고 있다. SeaWiFS 방법은 우선 두개의 근적외 파장대 관측결과와 복사전달시뮬레이션 결과(조견표)를 서로 비교하여 대기 중 에어로졸 입자의 종류 및 농도 최적값을 추정해 내며 이 추정결과를 바탕으로 모든 가시광 파장의 에어로졸 반사도 스펙트럼을 다시 조견표를 이용하여 계산한다. 천리안 해양위성의 대기보정도 유사하게 두 근적외파장대 에어로졸 반사도 상관관계를 이용하여 에어로졸 종류 및 농도를 계산하는데, 이 연구를 통하여 SeaWiFS 및 다른 유사 대기보정 방법들과 비교하여 정확도 뿐 아니라 계산 효율 또한 개선하였다. 추가적으로 SeaWiFS에 적용된 수증기 흡광 보정 모델을 천리안 해양위성의 분광특성에 맞게 수정하여 적용하였으며, 탁도가 높은 해역에서 대기보정 오차를 줄이는 방법도 천리안 해양위성 관측영역의 해수 광 특성 및 반사도 정보들을 이용하여 개발하였다. 초기버전의 천리안 해양위성 표준 대기보정의 검보정 결과 탁도가 높은 연안해역에서는 10% 내외의 만족할 만한 오차수준을 보여주었으나, 탁도가 낮은 해역에서는 50% 이상의 오차를 발생되었다. 이는 대리교정 수행의 부재가 주된 요인이며, 본 연구에서는 이를 보완하기 위해 SeaWiFS 표준 대리교정 프로세스에 기반을 두고 천리안 해양위성에 맞게 대리교정을 수행하였다. 이 대리교정 방법에서는 특정 해역의 에어로졸 광특성이 항상 해양성 에어로졸이라 가정하고 이를 바탕으로 근적외 파장대 위성 관측 조도를 시뮬레이션 하여 두 근적외 파장대를 먼저 상대교정 한다. 이후, 상대교정된 두 근적외 파장대를 이용하면 맑은 해역에서 복사전달시뮬레이션을 통하여 가시광 파장대 대기조도를 모의 할 수 있게 되고, 여기에 맑은 해역의 현장 광 측정 자료가 추가되면 가시광파장대 위성관측조도의 시뮬레이션이 가능하다. 이 가시광파장대 모의 결과와 실제 위성관측조도와 비교하면 가시광파장대 대리교정을 완료할 수 있다. 본 대리교정 결과 대리교정 상수가 최대 3.2% 바뀌었으며 (490 nm 밴드) 새 대리교정 상수 적용 시 맑은 해역 대기보정 정확도가 최대 50% 이상 상승하였다. 본 연구에서는 천리안해양위성 대기보정의 성능을 평가하기 위해서 대기보정 결과 원격반사도 (remote-sensing reflectance: Rrs)를 한국해양과학기술원 해양위성연구센터에서 2010년 이후로 한반도 주변 해역 현장조사를 통해 수집한 원격반사도 자료들과 비교검정 하였으며, 검정결과 76, 84, 88, 90, 81, 82%의 정확도를 보여주었다. 추가로 현장자료가 아닌 시뮬레이션 자료를 통해 천리안 해양위성 알고리즘 뿐 아니라 다른 해색원격탐사 임무를 위해 개발된 주요 대기보정 알고리즘들 구현하여 함께 비교검증 하였고, 본 비교검증에서도 천리안 해양위성 표준 대기보정이 다른 대기보정 방법들과 비교하여 가장 낮은 오차율을 보여주었으며, 특히 다중산란 효과가 큰 작은 입자크기의 에어로졸 모델에서 더 좋은 성능을 보여주었다. 본 연구결과는 이론적으로 SeaWiFS 등 비슷한 밴드 특성을 가진 타 해색위성의 대기보정방법으로도 적용이 가능하며, 천리안 해양위성 자료처리시스템 (GOCI data processing system: GDPS) 1.5버전에의 적용될 예정이다.Chapter 1. Introduction 1 1.1 Ocean color remote sensing 1 1.2 Geostationary Ocean Color Imager (GOCI) 2 1.3 Atmospheric correction and vicarious calibration 5 Chapter 2. Initial atmospheric correction for the GOCI data 10 2.1 Introduction 10 2.2 Method 11 2.2.1 Correction for gaseous absorption and whitecap radiance 13 2.2.2 Solar irradiance normalization 15 2.2.3 Correction for molecular (Rayleigh) scattering 17 2.2.4 Cloud mask 18 2.2.5 Correction for multiple scattering by aerosols 19 2.2.6 Correction for atmospheric transmittance 22 2.2.7 Correction for near-infrared water reflectance over turbid waters 22 2.3 Conclusion 24 Chapter 3. Algorithm updates and vicarious calibration for the GOCI atmospheric correction 25 3.1 Backgrounds 25 3.2 Updates to the initial GOCI atmospheric correction algorithm 26 3.2.1 Correction for gaseous absorption and whitecap radiance 26 3.2.2 Sun-glint correction 28 3.2.3 Considering gravity effect for Rayleigh scattering 29 3.2.4 Correction for multiple scattering by aerosols - SRAMS 30 3.2.5 Correction for bidirectional effects for water reflectance 35 3.2.6 Correction for near-infrared water reflectance over turbid waters 39 3.2.7 Atmospheric transmittance with considering anisotropic angular distribution of water reflectance 40 3.3 Vicarious calibration of GOCI near-infrared bands 41 3.3.1 Method 44 3.3.2 Inter-calibration of GOCI near-infrared bands 45 3.3.3 Vicarious calibration of GOCI visible bands 49 Chapter 4. Validation results 51 4.1 Data 51 4.1.1 Synthetic data derived by simulations 51 4.1.2 In situ radiometric data measured from shipboard 52 4.1.3 AERONET-OC radiometric data 56 4.2 Validation of SRAMS scheme with simulation data 58 4.3 Assessment of the atmospheric correction improvements with in situ radiometric data 59 Chapter 5. Discussions 61 5.1 Impacts of water vapor correction on ocean color products 61 5.2 Stability for high solar and satellite zenith angle for diurnal observation 62 5.3 Cloud masking on fast-moving clouds and quality analysis 63 5.4 Evaluation of the GOCI aerosol correction scheme compared with other approaches 64 5.4.1 Aerosol correction approach for OCTS 64 5.4.2 Aerosol correction approach for MERIS 67 5.4.3 Evaluation results 69 5.5 Pitfalls in estimation of aerosol reflectance using 2-NIR bands 71 5.6 Issues in the vicarious calibration of GOCI VIS and NIR bands 72 5.7 Uncertainties from bidirectional effect 75 Chapter 6. Conclusion 76 Appendix. Glossary of symbols 82 Acknowledgements 86 References 88Docto

Arctic–CHAMP: A program to study Arctic hydrology and its role in global change

The Arctic constitutes a unique and important environment that is central to the dynamics and evolution of the Earth system. The Arctic water cycle, which controls countless physical, chemical, and biotic processes, is also unique and important. These processes, in turn, regulate the climate, habitat, and natural resources that are of great importance to both native and industrial societies. Comprehensive understanding of water cycling across the Arctic and its linkage to global biogeophysical dynamics is a scientific as well as strategic policy imperative

Assessing the potential benefits of the geostationary vantage point for generating daily chlorophyll-a Maps in the Baltic Sea

Currently, observations from low-Earth orbit (LEO) ocean color sensors represent one of the most used tools to study surface optical and biogeochemical properties of the ocean. LEO observations are available at daily temporal resolution, and are often combined into weekly, monthly, seasonal, and annual averages in order to obtain sufficient spatial coverage. Indeed, daily satellite maps of the main oceanic variables (e.g., surface phytoplankton chlorophyll-a) generally have many data gaps, mainly due to clouds, which can be filled using either Optimal Interpolation or the Empirical Orthogonal Functions approach. Such interpolations, however, may introduce large uncertainties in the final product. Here, our goal is to quantify the potential benefits of having high-temporal resolution observations from a geostationary (GEO) ocean color sensor to reduce interpolation errors in the reconstructed hourly and daily chlorophyll-a products. To this aim, we used modeled chlorophyll-a fields from the Copernicus Marine Environment Monitoring Service's (CMEMS) Baltic Monitoring and Forecasting Centre (BAL MFC) and satellite cloud observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor (on board the geostationary satellite METEOSAT). The sampling of a GEO was thus simulated by combining the hourly chlorophyll fields and clouds masks, then hourly and daily chlorophyll-a products were generated after interpolation from neighboring valid data using the Multi-Channel Singular Spectral Analysis (M-SSA). Two cases are discussed: (i) A reconstruction based on the typical sampling of a LEO and, (ii) a simulation of a GEO sampling with hourly observations. The results show that the root mean square and interpolation bias errors are significantly reduced using hourly observations

Aeronautics and space report of the President, 1982 activities

Achievements of the space program are summerized in the area of communication, Earth resources, environment, space sciences, transportation, aeronautics, and space energy. Space program activities of the various deprtments and agencies of the Federal Government are discussed in relation to the agencies' goals and policies. Records of U.S. and world spacecraft launchings, successful U.S. launches for 1982, U.S. launched applications and scientific satellites and space probes since 1975, U.S. and Soviet manned spaceflights since 1961, data on U.S. space launch vehicles, and budget summaries are provided. The national space policy and the aeronautical research and technology policy statements are included

Arctic–CHAMP: A program to study Arctic hydrology and its role in global change

The Arctic constitutes a unique and important environment that is central to the dynamics and evolution of the Earth system. The Arctic water cycle, which controls countless physical, chemical, and biotic processes, is also unique and important. These processes, in turn, regulate the climate, habitat, and natural resources that are of great importance to both native and industrial societies. Comprehensive understanding of water cycling across the Arctic and its linkage to global biogeophysical dynamics is a scientific as well as strategic policy imperative

Monitoring the Characteristics of the Bohai Sea Ice Using High-Resolution Geostationary Ocean Color Imager (GOCI) Data

Satellite remote sensing data, such as moderate resolution imaging spectroradiometers (MODIS) and advanced very high-resolution radiometers (AVHRR), are being widely used to monitor sea ice conditions and their variability in the Bohai Sea, the southernmost frozen sea in the Northern Hemisphere. Monitoring the characteristics of the Bohai Sea ice can provide crucial information for ice disaster prevention for marine transportation, oil field operation, and regional climate change studies. Although these satellite data cover the study area with fairly high spatial resolution, their typically limited cloudless images pose serious restrictions for continuous observation of short-term dynamics, such as sub-seasonal changes. In this study, high spatiotemporal resolution (500 m and eight images per day) geostationary ocean color imager (GOCI) data with a high proportion of cloud-free images were used to monitor the characteristics of the Bohai Sea ice, including area and thickness. An object-based feature extraction method and an albedo-based thickness inversion model were used for estimating sea ice area and thickness, respectively. To demonstrate the efficacy of the new dataset, a total of 68 GOCI images were selected to analyze the evolution of sea ice area and thickness during the winter of 2012–2013 with severe sea ice conditions. The extracted sea ice area was validated using Landsat Thematic Mapper (TM) data with higher spatial resolution, and the estimated sea ice thickness was found to be consistent with in situ observation results. The entire sea ice freezing–melting processes, including the key events such as the day with the maximum ice area and the first and last days of the frozen season, were better resolved by the high temporal-resolution GOCI data compared with MODIS or AVHRR data. Both characteristics were found to be closely correlated with cumulative freezing/melting degree days. Our study demonstrates the applicability of the GOCI data as an improved dataset for studying the Bohai Sea ice, particularly for purposes that require high temporal resolution data, such as sea ice disaster monitoring

Summary of the Active Microwave Workshop, chapter 1

An overview is given of the utility, feasibility, and advantages of active microwave sensors for a broad range of applications, including aerospace. In many instances, the material provides an in-depth examination of the applicability and/or the technology of microwave remote sensing, and considerable documentation is presented in support of these techniques. An assessment of the relative strengths and weaknesses of active microwave sensor data indicates that satisfactory data are obtainable for several significant applications