Overview of Intercalibration of Satellite Instruments

Intercalibration of satellite instruments is critical for detection and quantification of changes in the Earth’s environment, weather forecasting, understanding climate processes, and monitoring climate and land cover change. These applications use data from many satellites; for the data to be interoperable, the instruments must be cross-calibrated. To meet the stringent needs of such applications, instruments must provide reliable, accurate, and consistent measurements over time. Robust techniques are required to ensure that observations from different instruments can be normalized to a common scale that the community agrees on. The long-term reliability of this process needs to be sustained in accordance with established reference standards and best practices. Furthermore, establishing physical meaning to the information through robust Système International d’unités traceable calibration and validation (Cal/Val) is essential to fully understand the parameters under observation. The processes of calibration, correction, stabilitymonitoring, and quality assurance need to be underpinned and evidenced by comparison with “peer instruments” and, ideally, highly calibrated in-orbit reference instruments. Intercalibration between instruments is a central pillar of the Cal/Val strategies of many national and international satellite remote sensing organizations. Intercalibration techniques as outlined in this paper not only provide a practical means of identifying and correcting relative biases in radiometric calibration between instruments but also enable potential data gaps between measurement records in a critical time series to be bridged. Use of a robust set of internationally agreed upon and coordinated intercalibration techniques will lead to significant improvement in the consistency between satellite instruments and facilitate accurate monitoring of the Earth’s climate at uncertainty levels needed to detect and attribute the mechanisms of change. This paper summarizes the state-of-the-art of postlaunch radiometric calibration of remote sensing satellite instruments through intercalibration

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

Overview of Intercalibration of Satellite Instruments

Intercalibration of satellite instruments is critical for detection and quantification of changes in the Earth’s environment, weather forecasting, understanding climate processes, and monitoring climate and land cover change. These applications use data from many satellites; for the data to be interoperable, the instruments must be cross-calibrated. To meet the stringent needs of such applications, instruments must provide reliable, accurate, and consistent measurements over time. Robust techniques are required to ensure that observations from different instruments can be normalized to a common scale that the community agrees on. The long-term reliability of this process needs to be sustained in accordance with established reference standards and best practices. Furthermore, establishing physical meaning to the information through robust Système International d’unités traceable calibration and validation (Cal/Val) is essential to fully understand the parameters under observation. The processes of calibration, correction, stabilitymonitoring, and quality assurance need to be underpinned and evidenced by comparison with “peer instruments” and, ideally, highly calibrated in-orbit reference instruments. Intercalibration between instruments is a central pillar of the Cal/Val strategies of many national and international satellite remote sensing organizations. Intercalibration techniques as outlined in this paper not only provide a practical means of identifying and correcting relative biases in radiometric calibration between instruments but also enable potential data gaps between measurement records in a critical time series to be bridged. Use of a robust set of internationally agreed upon and coordinated intercalibration techniques will lead to significant improvement in the consistency between satellite instruments and facilitate accurate monitoring of the Earth’s climate at uncertainty levels needed to detect and attribute the mechanisms of change. This paper summarizes the state-of-the-art of postlaunch radiometric calibration of remote sensing satellite instruments through intercalibration

Uncertainties in the Geostationary Ocean Color Imager (GOCI) Remote Sensing Reflectance for Assessing Diurnal Variability of Biogeochemical Processes

Short-term (sub-diurnal) biological and biogeochemical processes cannot be fully captured by the current suite of polar-orbiting satellite ocean color sensors, as their temporal resolution is limited to potentially one clear image per day. Geostationary sensors, such as the Geostationary Ocean Color Imager (GOCI) from the Republic of Korea, allow the study of these short-term processes because their orbit permit the collection of multiple images throughout each day for any area within the sensors field of regard. Assessing the capability to detect sub-diurnal changes in in-water properties caused by physical and biogeochemical processes characteristic of open ocean and coastal ocean ecosystems, however, requires an understanding of the uncertainties introduced by the instrument and/or geophysical retrieval algorithms. This work presents a study of the uncertainties during the daytime period for an ocean region with characteristically low-productivity with the assumption that only small and undetectable changes occur in the in-water properties due to biogeochemical processes during the daytime period. The complete GOCI mission data were processed using NASAs SeaDAS/l2gen package. The assumption of homogeneity of the study region was tested using three-day sequences and diurnal statistics. This assumption was found to hold based on the minimal diurnal and day-to-day variability in GOCI data products. Relative differences with respect to the midday value were calculated for each hourly observation of the day in order to investigate what time of the day the variability is greater. Also, the influence of the solar zenith angle in the retrieval of remote sensing reflectances and derived products was examined. Finally, we determined that the uncertainties in water-leaving remote-sensing reflectance (Rrs) for the 412,443, 490, 555, 660 and 680 nm bands on GOCI are 8.05 x 10(exp -4), 5.49 x 10(exp -4), 4.48 x 10(exp -4), 2.51 x 10(exp -4), 8.83 x 10(exp -5), and 1.36 x 10(exp -4)/sr, respectively, and 1.09 x 10(exp -2)/cu.mgm for the chlorophyll-a concentration (Chl-a), 2.09 x 10(exp -3)/m for the absorption coefficient of chromophoric dissolved organic matter at 412 nm (a(sub g) (412)), and 3.7 mg/cu.m for particulate organic carbon (POC). These R(sub rs) values can be considered the threshold values for detectable changes of the in-water properties due to biological, physical or biogeochemical processes from GOCI

Assessment of Normalized Water-Leaving Radiance Derived From Goci Using AERONET-OC Data

The geostationary ocean color imager (GOCI), as the world’s first operational geostationary ocean color sensor, is aiming at monitoring short-term and small-scale changes of waters over the northwestern Pacific Ocean. Before assessing its capability of detecting subdiurnal changes of seawater properties, a fundamental understanding of the uncertainties of normalized water-leaving radiance (nLw) products introduced by atmospheric correction algorithms is necessarily required. This paper presents the uncertainties by accessing GOCI-derived nLw products generated by two commonly used operational atmospheric algorithms, the Korea Ocean Satellite Center (KOSC) standard atmospheric algorithm adopted in GOCI Data Processing System (GDPS) and the NASA standard atmospheric algorithm implemented in Sea-Viewing Wide Field-of-View Sensor Data Analysis System (SeaDAS/l2gen package), with Aerosol Robotic Network Ocean Color (AERONET-OC) provided nLw data. The nLw data acquired from the GOCI sensor based on two algorithms and four AERONET-OC sites of Ariake, Ieodo, Socheongcho, and Gageocho from October 2011 to March 2019 were obtained, matched, and analyzed. The GDPS-generated nLw data are slightly better than that with SeaDAS at visible bands; however, the mean percentage relative errors for both algorithms at blue bands are over 30%. The nLw data derived by GDPS is of better quality both in clear and turbid water, although underestimation is observed at near-infrared (NIR) band (865 nm) in turbid water. The nLw data derived by SeaDAS are underestimated in both clear and turbid water, and the underestimation worsens toward short visible bands. Moreover, both algorithms perform better at noon (02 and 03 Universal Time Coordinated (UTC)), and worse in the early morning and late afternoon. It is speculated that the uncertainties in nLw measurements arose from aerosol models, NIR water-leaving radiance correction method, and bidirectional reflectance distribution function (BRDF) correction method in corresponding atmospheric correction procedure

Satellite Ocean Color Sensor Design Concepts and Performance Requirements

In late 1978, the National Aeronautics and Space Administration (NASA) launched the Nimbus-7 satellite with the Coastal Zone Color Scanner (CZCS) and several other sensors, all of which provided major advances in Earth remote sensing. The inspiration for the CZCS is usually attributed to an article in Science by Clarke et al. who demonstrated that large changes in open ocean spectral reflectance are correlated to chlorophyll-a concentrations. Chlorophyll-a is the primary photosynthetic pigment in green plants (marine and terrestrial) and is used in estimating primary production, i.e., the amount of carbon fixed into organic matter during photosynthesis. Thus, accurate estimates of global and regional primary production are key to studies of the earth's carbon cycle. Because the investigators used an airborne radiometer, they were able to demonstrate the increased radiance contribution of the atmosphere with altitude that would be a major issue for spaceborne measurements. Since 1978, there has been much progress in satellite ocean color remote sensing such that the technique is well established and is used for climate change science and routine operational environmental monitoring. Also, the science objectives and accompanying methodologies have expanded and evolved through a succession of global missions, e.g., the Ocean Color and Temperature Sensor (OCTS), the Seaviewing Wide Field-of-view Sensor (SeaWiFS), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Medium Resolution Imaging Spectrometer (MERIS), and the Global Imager (GLI). With each advance in science objectives, new and more stringent requirements for sensor capabilities (e.g., spectral coverage) and performance (e.g., signal-to-noise ratio, SNR) are established. The CZCS had four bands for chlorophyll and aerosol corrections. The Ocean Color Imager (OCI) recommended for the NASA Pre-Aerosol, Cloud, and Ocean Ecosystems (PACE) mission includes 5 nanometers hyperspectral coverage from 350 to 800 nanometers with three additional discrete near infrared (NIR) and shortwave infrared (SWIR) ocean aerosol correction bands. Also, to avoid drift in sensor sensitivity from being interpreted as environmental change, climate change research requires rigorous monitoring of sensor stability. For SeaWiFS, monthly lunar imaging accurately tracked stability at an accuracy of approximately 0.1% that allowed the data to be used for climate studies [2]. It is now acknowledged by the international community that future missions and sensor designs need to accommodate lunar calibrations. An overview of ocean color remote sensing and a review of the progress made in ocean color remote sensing and the variety of research applications derived from global satellite ocean color data are provided. The purpose of this chapter is to discuss the design options for ocean color satellite radiometers, performance and testing criteria, and sensor components (optics, detectors, electronics, etc.) that must be integrated into an instrument concept. These ultimately dictate the quality and quantity of data that can be delivered as a trade against mission cost. Historically, science and sensor technology have advanced in a "leap-frog" manner in that sensor design requirements for a mission are defined many years before a sensor is launched and by the end of the mission, perhaps 15-20 years later, science applications and requirements are well beyond the capabilities of the sensor. Section 3 provides a summary of historical mission science objectives and sensor requirements. This progression is expected to continue in the future as long as sensor costs can be constrained to affordable levels and still allow the incorporation of new technologies without incurring unacceptable risk to mission success. The IOCCG Report Number 13 discusses future ocean biology mission Level-1 requirements in depth

CLARREO Approach for Reference Intercalibration of Reflected Solar Sensors: On-Orbit Data Matching and Sampling

The implementation of the Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission was recommended by the National Research Council in 2007 to provide an on-orbit intercalibration standard with accuracy of 0.3% (k = 2) for relevant Earth observing sensors. The goal of reference intercalibration, as established in the Decadal Survey, is to enable rigorous high-accuracy observations of critical climate change parameters, including reflected broadband radiation [Clouds and Earth's Radiant Energy System (CERES)], cloud properties [Visible Infrared Imaging Radiometer Suite (VIIRS)], and changes in surface albedo, including snow and ice albedo feedback. In this paper, we describe the CLARREO approach for performing intercalibration on orbit in the reflected solar (RS) wavelength domain. It is based on providing highly accurate spectral reflectance and reflected radiance measurements from the CLARREO Reflected Solar Spectrometer (RSS) to establish an on-orbit reference for existing sensors, namely, CERES and VIIRS on Joint Polar Satellite System satellites, Advanced Very High Resolution Radiometer and follow-on imagers on MetOp, Landsat imagers, and imagers on geostationary platforms. One of two fundamental CLARREO mission goals is to provide sufficient sampling of high-accuracy observations that are matched in time, space, and viewing angles with measurements made by existing instruments, to a degree that overcomes the random error sources from imperfect data matching and instrument noise. The data matching is achieved through CLARREO RSS pointing operations on orbit that align its line of sight with the intercalibrated sensor. These operations must be planned in advance; therefore, intercalibration events must be predicted by orbital modeling. If two competing opportunities are identified, one target sensor must be given priority over the other. The intercalibration method is to monitor changes in targeted sensor response function parameters: effective offset, gain, nonlinearity, optics spectral response, and sensitivity to polarization. In this paper, we use existing satellite data and orbital simulationmethods to determinemission requirements for CLARREO, its instrument pointing ability, methodology, and needed intercalibration sampling and data matching for accurate intercalibration of RS radiation sensors on orbit