Atmospheric Correction in Sentinel-2 Simplified Level 2 Product Prototype Processor: Technical Aspects of Design and Implementation

This paper presents the scientific and technical aspects of the Level 2A (atmospheric/topographic correction) for the Sentinel-2 Simplified Level 2 Product Prototype Processor (S2SL2PPP). Design aspects are partly fixed by the ESA as main customer. Together with the alternative atmospheric correction system MACCS, the developed chain based on ATCOR is used for the estimation of the following products: Atmosphere type, Bottom of atmosphere reflectance (including cirrus detection and correction), Aerosol optical thickness, and Water vapor. Being a mono-temporal correction chain ATCOR requires a selection of the spectral bands for the estimation of Aerosol type, Aerosol optical thickness based on the dense dark vegetation method and Water vapor based on the atmospherically pre-corrected differential absorption method as well as an estimation of the best parameter set for these methods. The parameter set was estimated by a sensitivity analysis on a simulated top and bottom of atmosphere radiance/reflectance data based on radiative transfer simulations. The aerosol type is estimated by the comparison of the path radiances ratio to the ground truth path radiances ratio for the standard atmospheres, namely rural, urban, maritime, and desert. Aerosol optical thickness map and Water vapor map are initially estimated on the 20m pixel size data, then the maps are interpolated to the pixel size of 10m and the 10m reflectance data are estimated. The cirrus cloud map is created by the cirrus 1.38 µm band thresholding to the thin, medium, thick cirrus and cirrus clouds. Cirrus compensation is performed by correlating the cirrus band reflectance to the reflective region bands and subtraction of the cirrus contribution per band. Validation of the chain is performed given the top of atmosphere data (as input) and bottom of atmosphere products (the reference). Estimated reflectance is assessed given the ground truth reflectance, Aerosol optical thickness is validated given the AERONET measurements, cirrus correction is validated using a pair of Landsat-8 scenes acquired for the same area with a small time difference. One scene is contaminated by cirrus cloud that has to be restored, while the other is cirrus free and used as reference. A comparison of the estimated products is also performed with an alternative atmospheric correction chain – FLAASH. The software is developed using the Interactive Data Language (IDL) and python. This paper presents the scientific and technical aspects of the Level 2A (atmospheric/topographic correction) for the Sentinel-2 Simplified Level 2 Product Prototype Processor (S2SL2PPP). Design aspects are partly fixed by the ESA as main customer. Together with the alternative atmospheric correction system MACCS, the developed chain based on ATCOR is used for the estimation of the following products: Atmosphere type, Bottom of atmosphere reflectance (including cirrus detection and correction), Aerosol optical thickness, and Water vapor. Being a mono-temporal correction chain ATCOR requires a selection of the spectral bands for the estimation of Aerosol type, Aerosol optical thickness based on the dense dark vegetation method and Water vapor based on the atmospherically pre-corrected differential absorption method as well as an estimation of the best parameter set for these methods. The parameter set was estimated by a sensitivity analysis on a simulated top and bottom of atmosphere radiance/reflectance data based on radiative transfer simulations. The aerosol type is estimated by the comparison of the path radiances ratio to the ground truth path radiances ratio for the standard atmospheres, namely rural, urban, maritime, and desert. Aerosol optical thickness map and Water vapor map are initially estimated on the 20m pixel size data, then the maps are interpolated to the pixel size of 10m and the 10m reflectance data are estimated. The cirrus cloud map is created by the cirrus 1.38 µm band thresholding to the thin, medium, thick cirrus and cirrus clouds. Cirrus compensation is performed by correlating the cirrus band reflectance to the reflective region bands and subtraction of the cirrus contribution per band. Validation of the chain is performed given the top of atmosphere data (as input) and bottom of atmosphere products (the reference). Estimated reflectance is assessed given the ground truth reflectance, Aerosol optical thickness is validated given the AERONET measurements, cirrus correction is validated using a pair of Landsat-8 scenes acquired for the same area with a small time difference. One scene is contaminated by cirrus cloud that has to be restored, while the other is cirrus free and used as reference. A comparison of the estimated products is also performed with an alternative atmospheric correction chain – FLAASH. The software is developed using the Interactive Data Language (IDL) and python

Algorithm for Atmospheric Corrections of Aircraft and Satellite Imagery

A simple and fast atmospheric correction algorithm is described which is used to correct radiances of scattered sunlight measured by aircraft and/or satellite above a uniform surface. The atmospheric effect, the basic equations, a description of the computational procedure, and a sensitivity study are discussed. The program is designed to take the measured radiances, view and illumination directions, and the aerosol and gaseous absorption optical thickness to compute the radiance just above the surface, the irradiance on the surface, and surface reflectance. Alternatively, the program will compute the upward radiance at a specific altitude for a given surface reflectance, view and illumination directions, and aerosol and gaseous absorption optical thickness. The algorithm can be applied for any view and illumination directions and any wavelength in the range 0.48 micron to 2.2 micron. The relation between the measured radiance and surface reflectance, which is expressed as a function of atmospheric properties and measurement geometry, is computed using a radiative transfer routine. The results of the computations are presented in a table which forms the basis of the correction algorithm. The algorithm can be used for atmospheric corrections in the presence of a rural aerosol. The sensitivity of the derived surface reflectance to uncertainties in the model and input data is discussed

MODIS: Moderate-resolution imaging spectrometer. Earth observing system, volume 2B

The Moderate-Resolution Imaging Spectrometer (MODIS), as presently conceived, is a system of two imaging spectroradiometer components designed for the widest possible applicability to research tasks that require long-term (5 to 10 years), low-resolution (52 channels between 0.4 and 12.0 micrometers) data sets. The system described is preliminary and subject to scientific and technological review and modification, and it is anticipated that both will occur prior to selection of a final system configuration; however, the basic concept outlined is likely to remain unchanged

Technique for validating remote sensing products of water quality

Remote sensing of water quality is initiated as an additional part of the on going activities of the EAGLE2006 project. Within this context intensive in-situ and airborne measurements campaigns were carried out over the Wolderwijd and Veluwemeer natural waters. However, in-situ measurements and image acquisitions were not simultaneous. This poses some constraints on validating air/space-borne remote sensing products of water quality. Nevertheless, the detailed insitu measurements and hydro-optical model simulations provide a bench mark for validating remote sensing products. That is realized through developing a stochastic technique to quantify the uncertainties on the retrieved aquatic inherent optical properties (IOP). The output of the proposed technique is applied to validate remote sensing products of water quality. In this processing phase, simulations of the radiative transfer in the coupled atmosphere-water system are performed to generate spectra at-sensor-level. The upper and the lower boundaries of perturbations, around each recorded spectrum, are then modelled as function of residuals between simulated and measured spectra. The perturbations are parameterized as a function of model approximations/inversion, sensor-noise and atmospheric residual signal. All error sources are treated as being of stochastic nature. Three scenarios are considered: spectrally correlated (i.e. wavelength dependent) perturbations, spectrally uncorrelated perturbations and a mixed scenario of the previous two with equal probability of occurrence. Uncertainties on the retrieved IOP are quantified with the relative contribution of each perturbation component to the total error budget of the IOP. This technique can be used to validate earth observation products of water quality in remote areas where few or no in– situ measurements are available

Determination of aerosol content in the atmosphere from ERTS-1 data

The author has identified the following significant results. Significant results, relating the radiance over water surfaces to the atmospheric aerosol content, have been obtained. The results indicate that the MSS channels 4, 5, and 6 centered at 0.55, 0.65, and 0.75 microns have comparable sensitivity, and that the aerosol content can be determined within + or - 10% with the assumed measurement errors of the MSS. The fourth channel, MSS 7, is not useful for aerosol determination due to the water radiance values from this channel generally being less than the instrument noise. The accuracy of the aerosol content measurement could be increased by using an instrument specifically designed for this purpose. This radiance-aerosol content relationship can possibly provide a basis for monitoring the atmospheric aerosol content on a global basis, allowing a base-line value of aerosols to be established. The contrast-aerosol content investigation shows useful linear relationships in MSS channels 4 and 5, allowing the aerosol content to be determined within + or - 10%. MSS 7 is not useful due to the low accuracy in the water radiance, and MSS 6 is found to be too insensitive. These results rely on several assumptions due to the lack of ground truth data, but do serve to indicate which channels are most useful

Passive remote sensing of tropospheric aerosol and atmospheric correction for the aerosol effect

The launch of ADEOS in August 1996 with POLDER, TOMS, and OCTS instruments on board and the future launch of EOS-AM 1 in mid-1998 with MODIS and MISR instruments on board start a new era in remote sensing of aerosol as part of a new remote sensing of the whole Earth system (see a list of the acronyms in the Notation section of the paper). These platforms will be followed by other international platforms with unique aerosol sensing capability, some still in this century (e.g., ENVISAT in 1999). These international spaceborne multispectral, multiangular, and polarization measurements, combined for the first time with international automatic, routine monitoring of aerosol from the ground, are expected to form a quantum leap in our ability to observe the highly variable global aerosol. This new capability is contrasted with present single-channel techniques for AVHRR, Meteosat, and GOES that although poorly calibrated and poorly characterized already generated important aerosol global maps and regional transport assessments. The new data will improve significantly atmospheric corrections for the aerosol effect on remote sensing of the oceans and be used to generate first real-time atmospheric corrections over the land. This special issue summarizes the science behind this change in remote sensing, and the sensitivity studies and applications of the new algorithms to data from present satellite and aircraft instruments. Background information and a summary of a critical discussion that took place in a workshop devoted to this topic is given in this introductory paper. In the discussion it was concluded that the anticipated remote sensing of aerosol simultaneously from several space platforms with different observation strategies, together with continuous validations around the world, is expected to be of significant importance to test remote sensing approaches to characterize the complex and highly variable aerosol field. So far, we have only partial understanding of the information content and accuracy of the radiative transfer inversion of aerosol information from the satellite data, due to lack of sufficient theoretical analysis and applications to proper field data. This limitation will make the anticipated new data even more interesting and challenging. A main concern is the present inadequate ability to sense aerosol absorption, from space or from the ground. Absorption is a critical parameter for climate studies and atmospheric corrections. Over oceans, main concerns are the effects of white caps and dust on the correction scheme. Future improvement in aerosol retrieval and atmospheric corrections will require better climatology of the aerosol properties and understanding of the effects of mixed composition and shape of the particles. The main ingredient missing in the planned remote sensing of aerosol are spaceborne and ground-based lidar observations of the aerosol profiles

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

Modeling Atmosphere-Ocean Radiative Transfer: A PACE Mission Perspective

The research frontiers of radiative transfer (RT) in coupled atmosphere-ocean systems are explored to enable new science and specifically to support the upcoming Plankton, Aerosol, Cloud ocean Ecosystem (PACE) satellite mission. Given (i) the multitude of atmospheric and oceanic constituents at any given moment that each exhibits a large variety of physical and chemical properties and (ii) the diversity of light-matter interactions (scattering, absorption, and emission), tackling all outstanding RT aspects related to interpreting and/or simulating light reflected by atmosphere-ocean systems becomes impossible. Instead, we focus on both theoretical and experimental studies of RT topics important to the science threshold and goal questions of the PACE mission and the measurement capabilities of its instruments. We differentiate between (a) forward (FWD) RT studies that focus mainly on sensitivity to influencing variables and/or simulating data sets, and (b) inverse (INV) RT studies that also involve the retrieval of atmosphere and ocean parameters. Our topics cover (1) the ocean (i.e., water body): absorption and elastic/inelastic scattering by pure water (FWD RT) and models for scattering and absorption by particulates (FWD RT and INV RT); (2) the air-water interface: variations in ocean surface refractive index (INV RT) and in whitecap reflectance (INV RT); (3) the atmosphere: polarimetric and/or hyperspectral remote sensing of aerosols (INV RT) and of gases (FWD RT); and (4) atmosphere-ocean systems: benchmark comparisons, impact of the Earth's sphericity and adjacency effects on space-borne observations, and scattering in the ultraviolet regime (FWD RT). We provide for each topic a summary of past relevant (heritage) work, followed by a discussion (for unresolved questions) and RT updates

Retrieval of Volcanic and Man-Made Stratospheric Aerosols from Orbital Polarimetric Measurements

Stratospheric aerosols that are caused by a major volcanic eruption can serve as a valuable test of global climate models, as well as severely complicate tropospheric-aerosol monitoring from space. In either case, it is highly desirable to have accurate global information on the optical thickness, size, and composition of volcanic aerosols. We report sensitivity study results, which analyze the implications of making precise multi-angle photopolarimetric measurements in a 1.378-m spectral channel residing within a strong water-vapor absorption band. We demonstrate that, under favorable conditions, such measurements would enable near-perfect retrievals of the optical thickness, effective radius, and refractive index of stratospheric aerosols. Besides enabling accurate retrievals of volcanic aerosols, such measurements can also be used to monitor man-made particulates injected in the stratosphere for geoengineering purposes