Atmospheric Correction Techniques Research Articles

Suspended Sediment Concentration Estimation along Turbid Water Outflow Using a Multispectral Camera on an Unmanned Aerial Vehicle

Optical remote sensing using unmanned aerial vehicles (UAVs) is proposed to monitor changes in marine environments effectively. Optical measurements were performed using a UAV multispectral camera (RedEdge, five spectral wavelengths of 475, 560, 668, 717, and 842 nm) with high spatial (5 cm) and temporal (1 s) resolutions to monitor the rapidly changing suspended sediment concentration (SSC) in the Saemangeum coastal area on the western coast of Korea. To develop the SSC algorithm, optical field, and water sample measurements were obtained from outside (11 stations) and inside (three stations) regions separated by a seawall, accounting for 100 measurements from 2018 to 2020. Accordingly, the remote sensing reflectance (Rrs) was estimated at each sampling station and used to develop the SSC algorithm based on multiple linear regression. The algorithm reasonably estimated the SSC with an R2 and root mean square error of 0.83 and 4.27 (mg L−1), respectively. Continuous individual UAV measurements over the coastal area of Saemangeum were combined to generate a wider SSC map. For the UAV observational data, the atmospheric influence at each altitude was reduced to the surface altitude level using a relative atmospheric correction technique. The SSC map enabled front monitoring of SSC fluctuations caused by discharge water due to the sluice gate opening. These results demonstrated the usability of the UAV-based SSC algorithm and confirmed the possibility of monitoring rapid SSC fluctuations.

Radiative interaction of atmosphere and surface: Write-up with elements of code

In passive satellite remote sensing of the Earth, separation of the path radiance (atmosphere-only contribution) from the surface reflection remains a “significant challenge”. Recent literature names it among the gaps in radiative transfer (RT) topics that “require continued research in the near future”. The challenge comes from multiple reflections (bouncing) between the atmosphere and surface – radiative interaction. In this paper we use a known RT technique, the matrix-operator method (MOM), and a new modification of the monochromatic vector RT (vRT) code IPOL (Intensity and POLarization) to simulate the interaction of a plane-parallel atmosphere and a few widely used surface reflection models.Following the idea of the Green's function method, IPOL no longer takes the surface model parameters on input. Instead, it provides the path radiance, and the atmospheric reflection and transmission matrices as output. Despite many RT codes use the MOM formalism, this output does not seem common. The surface reflection matrix is computed externally. Therefore, this paper extends the Green's function atmospheric correction technique to the case of polarized light.Aiming clarity rather than performance, we explain in Python the structure of the surface matrices for the isotropic (Lambertian), directional unpolarized, and polarized ocean reflection models. We then combine these surface matrices and the precomputed IPOL output to get numerically accurate signal at the top of atmosphere (TOA) and test it vs. published benchmarks. Then, for each benchmark scenario we show how to get the surface from the TOA signal, i.e. perform the RT-based atmospheric correction.

Assessment of Sentinel-2 and Landsat-8 OLI for Small-Scale Inland Water Quality Modeling and Monitoring Based on Handheld Hyperspectral Ground Truthing

This study investigates the best available methods for remote monitoring inland small-scale waterbodies, using remote sensing data from both Landsat-8 and Sentinel-2 satellites, utilizing a handheld hyperspectral device for ground truthing. Monitoring was conducted to evaluate water quality indicators: chlorophyll-a (Chl-a), colored dissolved organic matter (CDOM), and turbidity. Ground truthing was performed to select the most suitable atmospheric correction technique (ACT). Several ACT have been tested: dark spectrum fitting (DSF), dark object subtraction (DOS), atmospheric and topographic correction (ATCOR), and exponential extrapolation (EXP). Classical sampling was conducted first; then, the resulting concentrations were compared to those obtained using remote sensing analysis by the above-mentioned ACT. This research revealed that DOS and DSF achieved the best performance (an advantage ranging between 29% and 47%). Further, we demonstrated the appropriateness of the use of Sentinel-2 red and vegetation red edge reciprocal bands 1 / B 4 × B 6 for estimating Chl-a ( R 2 = 0.82 , RMSE = 14.52 mg / m 3 ). As for Landsat-8, red to near-infrared ratio ( B 4 / B 5 ) produced the best performing model ( R 2 = 0.71 , RMSE = 39.88 mg / m 3 ), but it did not perform as well as Sentinel-2. Regarding turbidity, the best model ( R 2 = 0.85 , RMSE = 0.87 NTU) obtained by Sentinel-2 utilized a single band (B4), while the best model (with R 2 = 0.64 , RMSE = 0.90 NTU) using Landsat-8 was performed by applying two bands ( B 1 / B 3 ). Mapping the water quality parameters using the best performance biooptical model showed the significant effect of the adjacent land on the boundary pixels compared to pixels of deeper water.

Decadal Measurements of the First Geostationary Ocean Color Satellite (GOCI) Compared with MODIS and VIIRS Data

The first geostationary ocean color data from the Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean, and Meteorological Satellite (COMS) have been accumulating for more than ten years from 2010. This study performs a multi-year quality assessment of GOCI chlorophyll-a (Chl-a) and radiometric data for 2012–2021 with an advanced atmospheric correction technique and a regionally specialized Chl-a algorithm. We examine the consistency and stability of GOCI, Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) level 2 products in terms of annual and seasonal climatology, two-dimensional frequency distribution, and multi-year time series. Overall, the GOCI agrees well with MODIS and VIIRS on annual and seasonal variability in Chl-a, as the central biological pattern of the most transparent waters over the western North Pacific, productive waters over the East Sea, and turbid waters over the Yellow Sea are reasonably represented. Overall, an excellent agreement is remarkable for western North Pacific oligotrophic waters (with a correlation higher than 0.91 for Chl-a and 0.96 for band-ratio). However, the sporadic springtime overestimation of MODIS Chl-a values compared with others is notable over the Yellow Sea and East Sea due to the underestimation of MODIS blue-green band ratios for moderate-high aerosol optical depth. The persistent underestimation of VIIRS Chl-a values compared with GOCI and MODIS occurs due to inherent sensor calibration differences. In addition, the artificially increasing trends in GOCI Chl-a (+0.48 mg m−3 per 9 years) arise by the decreasing trends in the band ratios. However, decreasing Chl-a trends in MODIS and VIIRS (−0.09 and −0.08 mg m−3, respectively) are reasonable in response to increasing sea surface temperature. The results indicate GOCI sensor degradation in the late mission period. The long-term application of the GOCI data should be done with a caveat, however; planned adjustments to GOCI calibration (2022) in the following GOCI-II satellite will essentially eliminate the bias in Chl-a trends.

A Robust Atmospheric Correction Procedure for Determination of Spectral Reflectance of Terrestrial Surfaces from Satellite Spectral Measurements

In this work, we propose simple and robust technique for the retrieval of underlying surface spectral reflectance using spaceborne observations. It can be used to process both multispectral moderate resolution satellite data and also multi-zone high spatial resolution data. The technique can work automatically for different types of land surfaces without using huge databases accumulated in advance. The new cloud discrimination and retrieval of the water vapor content in atmosphere procedures are presented. The key point of the proposed atmospheric correction technique is the suggested single-wavelength method for determining the atmospheric aerosol optical thickness without reference to a specific type of underlying surface spectrum. The retrievals of spectral reflectance for various land surfaces with developed technique, performed using computer simulation and experimental data, have demonstrated a high retrieval accuracy.

AVALIAÇÃO DE ÍNDICES ESPECTRAIS OBTIDOS COM IMAGENS SENTINEL-2 E LANDSAT 8 ANTES E APÓS ROMPIMENTO DA BARRAGEM DA MINA DO FEIJÃO, BRUMADINHO – MG

Métodos e dados de sensoriamento remoto têm sido largamente utilizados em diversas aplicações de análises ambientais. Dentro desse aspecto, a sua utilização tem auxiliado de forma efetiva em análises de mudanças do solo, bem como, nos estudos de vegetação. Os índices de vegetação permitem realizar análises e estudos da biomassa e do vigor vegetativo apresentado por uma região de forma simples e eficaz. Esse artigo realizou uma análise do Índice de Vegetação de Diferença Normalizada (NDVI), comparando os resultados obtidos a partir de imagens dos sensores Operational Land Imager (OLI) e Multispectral Instrument (MSI). Além disso, o estudo realizou uma comparação entre a aplicação do índice em imagens com e sem a utilização de técnicas de correção da atmosfera. A área de estudo é a região da mina do feijão, em Brumadinho – MG, onde ocorreu o rompimento da barragem de rejeitos em 25 de janeiro de 2019. Os resultados encontrados permitiram realizar uma comparação entre as imagens, com e sem a utilização de técnicas de pré-processamento para correção atmosférica, e analisar o comportamento apresentado pela região antes e depois do acidente.

Atmospheric correction of multispectral satellite imagery

Atmospheric correction is a necessary step in the processing of remote sensing data acquired in the visible and NIR spectral bands.The paper describes the developed atmospheric correction technique for multispectral satellite data with a small number of relatively broad spectral bands (not hyperspectral). The technique is based on the proposed analytical formulae that expressed the spectrum of outgoing radiation at the top of a cloudless atmosphere with rather high accuracy. The technique uses a model of the atmosphere and its optical and physical parameters that are significant from the point of view of radiation transfer, the atmosphere is considered homogeneous within a satellite image. To solve the system of equations containing the measured radiance of the outgoing radiation in the bands of the satellite sensor, the number of which is less than the number of unknowns of the model, it is proposed to use various additional relations, including regression relations between the optical parameters of the atmosphere. For a particular image pixel selected in a special way, unknown atmospheric parameters are found, which are then used to calculate the reflectance for all other pixels.Testing the proposed technique on OLI sensor data of Landsat 8 satellite showed higher accuracy in comparison with the FLAASH and QUAC methods implemented in the well-known ENVI image processing software. The technique is fast and there is using no additional information about the atmosphere or land surface except images under correction.

Retrieval of Aerosol Optical Thickness in the Arctic Snow-Covered Regions Using Passive Remote Sensing: Impact of Aerosol Typing and Surface Reflection Model

Currently, no aerosol optical thickness (AOT) data set over the Arctic snow/ice-covered regions derived from space-borne passive remote sensing is available. The challenge is to develop an accurate and robust technique to derive AOT above highly variable and bright snow/ice surfaces. To extend data coverage of the eXtensible Bremen Aerosol/cloud and surfacE Retrieval (XBAER) AOT data product in the future, we propose a new algorithm for the retrieval of AOT and surface properties over snow/ice simultaneously. The algorithm utilizes the linear perturbation theory and does not use any simplified atmospheric correction techniques. Key issues like the selection of a proper aerosol type and optimal surface parameterization method for the retrieval of AOT over the Arctic have been investigated. The aerosol type is investigated using the aerosol climatology microphysical properties derived from four Aerosol Robotic Network (AERONET) sites (Barrow, Hornsund, Kangerlussuaq, and Tiksi). The three-parametric Ross-Li linear kernel model is used to describe the snow bidirectional reflectance distribution function (BRDF). The a priori knowledge of wavelength-dependent features of the coefficients in the Ross-Li linear kernel model is derived from Polarization and Directionality of the Earth's Reflectances (POLDER) measurements over the Arctic and utilized as constraints in the retrieval. The studies show that the combination of Ross-Li surface model and weakly absorbing aerosol parameterization provides an optimal way to derive AOT over the Arctic snow/ice-covered regions from passive remote sensing observations. The retrieved AOTs using POLDER show good agreement with AERONET observations.

Performance Evaluation of Phase and Weather-Based Models in Atmospheric Correction With Sentinel-1Data: Corvara Landslide in the Alps

Phase delay caused by atmospheric effects due to spatial and temporal variations of pressure, temperature, and water vapor content is one of the major error sources in estimation of ground deformation by interferometric synthetic aperture radar (InSAR>). Therefore, accuracy of ground deformation measurement is highly contingent on the robustness of the atmospheric correction techniques. These techniques rely either on auxiliary data such as numerical weather models (NWMs) or on the analysis of the interferometric phase itself. The accuracy in phase delays estimation of mixing effects of turbulent delay in atmosphere and stratified delay in lower troposphere is a key factor in determination of performance of each technique. Hence, the performance evaluation of the techniques is required in order to assess their potentials, robustness, and limitations. This article analyzes and evaluates the performance of four NWMs (i.e., ERA-Interim, ERA5, MERRA2, and WRF) and two phase-based techniques (i.e., linear and power law) to estimate phase delay using Sentinel-1A/B data over the Corvara landslide located in the Alps. The GPS data and GACOS product were used to validate the results. We generally found that ERA5 outperformed among other weather models with a phase standard deviation reduction of 77.7% (with respect to the InSAR phase), a correlation coefficient of 0.86 (between InSAR phase and estimated tropospheric delay) and a less significant error in the velocity estimation of the landslide.

Evaluation of Atmospherically Gases Using Models FLAASH and QUAC to Hyper-spectral imagery

The important step is correction of the effect of atmospheric on hyper-spectral imagery of the VIS “visible”, short wave & NIR “near-infrared” spectral range. In general, the cause for limiting the use of hyperspectral images is the atmospheric effects, so, atmospheric correction is necessary for any accurate processing. In this work, two atmospheric correction techniques have been applied on Hyper-spectral image. From the raw original image and also from the FLAASH and QUAC atmospheric corrected images the spectra of vegetation, water and soil were extracted. The acquisition data for study contained Hyperion bands for year “2015” images over each of the following regions: first region is the sedimentary plain in the central region of Republic of Iraq and the second region is a mountainous area of the northern regions of the Republic of Iraq. The survey will focus mainly on the precision of the atmospheric compensation algorithms by comparing the corrected bands location in the reflectance measurement of different surface types collected for each region. The results of two algorithms in the mountainous area that have terrain are best than the sedimentary plain area.

Field scale wheat LAI retrieval from multispectral Sentinel 2A-MSI and LandSat 8-OLI imagery: effect of atmospheric correction, image resolutions and inversion techniques

This study assessed the effect of atmospheric correction algorithms, inversion techniques and image spatial and spectral resolution on wheat crop LAI retrieval using Sentinel-2 MSI and Landsat-8 OLI imagery. The LAI retrievals were validated with in-situ measurements collected in farmers’ fields. The MSI-based LAI retrievals improved significantly when images were atmospherically corrected using MODTRAN than using the libRadtran code. Among the two PROSAIL inversion approaches, look-up table outperforms artificial neural network for LAI retrievals. Using the best strategy of atmospheric correction and inversion, the effect of spatial resolution from 20 m (MSI) to 30 m (OLI) while using common six bands, showed non-significant improvement in LAI retrievals. The inclusion of additional two red-edge bands as available in MSI significantly reduced the uncertainly in LAI retrievals over that obtained by using six bands, while inclusion of only additional VNIR band did not show any significant effect on LAI retrievals.

An Assessment of Atmospheric Correction Methods for GOCI Images in the Yellow River Estuary

Mu, B.; Qin, P.; Liu, C.; Liang, X.-J., and Huang, T.-X., 2019. An assessment of atmospheric correction methods for GOCI images in the Yellow River Estuary. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 171-182. Coconut Creek (Florida), ISSN 0749-0208.The Yellow River Estuary is a typical case II water body with high turbidity. Atmospheric correction of Geostationary Ocean Color Imager (GOCI) data is difficult in such areas. The applicability of existing atmospheric correction methods suffers from a lack of systematic assessment. In this study, the nearest-neighbor, near-infrared (NIR)-ratio, and ultraviolet atmospheric correction (UV-AC) techniques are applied to GOCI images of the Yellow River Estuary, and the quality of the water-leaving reflectance ρw(λ) obtained by these methods is evaluated. The results show that the performance of the NIR-ratio and UV-AC methods is almost the same compared with in situ synchronous data, with the absolute percentage difference (APD) of each band ranging from 6–48 % and 9–47 %, respectively. The accuracy of the 660 nm and 680 nm bands is the highest (APD less than 10 %). The values of ρw(λ) retrieved by the nearest-neighbor method are obviously underestimated, with an APD ranging from 30–196 %. Moreover, negative values appear in the NIR and blue bands. A rationality evaluation of the spectral shape of ρw(λ) extracted from the GOCI images further confirms the consistency of the results obtained by the UV-AC and NIR-ratio methods. Approximately 86 % of pixels were scored the same by the two methods. Based on the above evaluation results, the NIR-ratio and UV-AC methods are concluded to have the same accuracy, and both perform better than the nearest-neighbor method. The input parameters of the NIR-ratio method are determined in advance using in situ measured data. On account of the limited amount of measured data, the representativeness and applicability of the input parameters are yet to be confirmed.

Separating volcanic deformation and atmospheric signals at Mount St. Helens using Persistent Scatterer InSAR

Over the past two decades, GPS and leveling surveys have recorded cycles of inflation and deflation associated with dome building eruptions at Mount St. Helens. Due to spatial and temporal limitations of the data, it remains unknown whether any deformation occurred prior to the most recent eruption of 2004, information which could help anticipate future eruptions. Interferometric Synthetic Aperture Radar (InSAR), which boasts fine spatial resolution over large areas, has the potential to resolve pre-eruptive deformation that may have occurred, but eluded detection by campaign GPS surveys because it was localized to the edifice or crater. Traditional InSAR methods are challenging to apply in the Cascades volcanic arc because of a combination of environmental factors, and past attempts to observe deformation at Mount St. Helens were unable to make reliable observations in the crater or on much of the edifice.In this study, Persistent Scatterer InSAR, known to mitigate issues of decorrelation caused by environmental factors, is applied to four SAR data sets in an attempt to resolve localized sources of deformation on the volcano between 1995 and 2010. Many interferograms are strongly influenced by phase delay from atmospheric water vapor and require correction, evidenced by a correlation between phase and topography. To assess the bias imposed by the atmosphere, we perform sensitivity tests on a suite of atmospheric correction techniques, including several that rely on the correlation of phase delay to elevation, and explore approaches that directly estimate phase delay using the ERA-Interim and NARR climate reanalysis data sets. We find that different correction methods produce velocities on the edifice of Mount St. Helens that differ by up to 1cm/yr due to variability in how atmospheric artifacts are treated in individual interferograms. Additionally, simple phase-based techniques run the risk of minimizing any surface deformation signals that may themselves be correlated with elevation. The atmospherically corrected PS InSAR results for data sets overlapping in time are inconsistent with one another, and do not provide conclusive evidence for any pre-eruptive deformation at a broad scale or localized to the crater or edifice leading up to the 2004 eruption. However, we cannot rule out the possibility of deformation less than ~1cm/yr, or discern whether deformation rates increased in the months preceding the eruption. The results do significantly improve the spatial density of observations and our ability to resolve or rule out models for a potential deformation source for the pre-eruptive period.

Impacts of dust aerosol and adjacency effects on the accuracy of Landsat 8 and RapidEye surface reflectances

The atmospheric correction of satellite data is challenging over desert agricultural systems, due to the relatively high aerosol optical thicknesses (τ550), bright soils, and a heterogeneous surface reflectance field. Indeed, the contribution of reflected radiation from adjacent pixels scattered into the field of view of a target pixel is considerable and can significantly affect the fidelity of retrieved reflectances. In this study, uncertainties and quantitative errors associated with the atmospheric correction of multi-spectral Landsat 8 and RapidEye data were characterized over a desert agricultural landscape in Saudi Arabia. Surface reflectances were retrieved using an implementation of the 6SV atmospheric correction code, and validated against field collected spectroradiometer measurements over desert, cultivated soil, and vegetated surface targets. A combination of satellite and Aerosol Robotic Network (AERONET) data were used to parameterize aerosol properties and atmospheric state parameters. With optimal specification of τ550 and aerosol optical properties and correction for adjacency effects, the relative Mean Absolute Deviation (MAD) for all bands combined was 5.4% for RapidEye and 6.8% for Landsat 8. However uncertainties associated with satellite-based τ550 retrievals were shown to introduce significant error into the reflectance estimates. With respect to deriving common vegetation indices from corrected reflectance data, the Normalized Difference Vegetation Index (NDVI) was associated with the smallest errors (3–8% MAD). Surface reflectance errors were highest for bands in the visible part of the spectrum, particularly the blue band (5–16%), while there was more consistency within the red-edge (~5%) and near-infrared (5–7%). Results were generally better constrained when a τ550-dependent aerosol model for desert dust particles, parameterized on the basis of nearby AERONET site data, was used in place of a generic rural or background desert model. This adaptation was particularly pertinent for the Landsat bands in the shortwave infrared. Failure to correct for adjacency effects increased the overall surface reflectance MAD to around 18%. The impact was especially significant for the visible bands over vegetated surface targets, as evidenced by relative MADs increasing to >200%. In comparison, a relatively minor adjacency effect was observed within the near-infrared and over the more reflective bare soil and desert sites. Overall, the impact of adjacency effects was substantial (20–25% MAD) for all of the studied vegetation indices. This investigation fills a research gap in the quantification of uncertainties and evaluation of achievable accuracy levels in high spatial resolution surface reflectance imagery over desert agriculture and highlights the need for accurate and regionally representative atmospheric correction techniques.

Supervised Classification of Benthic Reflectance in Shallow Subtropical Waters Using a Generalized Pixel-Based Classifier across a Time Series

We tested a supervised classification approach with Landsat 5 Thematic Mapper (TM) data for time-series mapping of seagrass in a subtropical lagoon. Seagrass meadows are an integral link between marine and inland ecosystems and are at risk from upstream processes such as runoff and erosion. Despite the prevalence of image-specific approaches, the classification accuracies we achieved show that pixel-based spectral classes may be generalized and applied to a time series of images that were not included in the classifier training. We employed in-situ data on seagrass abundance from 2007 to 2011 to train and validate a classification model. We created depth-invariant bands from TM bands 1, 2, and 3 to correct for variations in water column depth prior to building the classification model. In-situ data showed mean total seagrass cover remained relatively stable over the study area and period, with seagrass cover generally denser in the west than the east. Our approach achieved mapping accuracies (67% and 76% for two validation years) comparable with those attained using spectral libraries, but was simpler to implement. We produced a series of annual maps illustrating inter-annual variability in seagrass occurrence. Accuracies may be improved in future work by better addressing the spatial mismatch between pixel size of remotely sensed data and footprint of field data and by employing atmospheric correction techniques that normalize reflectances across images.

Assessing atmospheric bias correction for dynamical consistency using potential vorticity

Correcting biases in atmospheric variables prior to impact studies or dynamical downscaling can lead to new biases as dynamical consistency between the ‘corrected’ fields is not maintained. Use of these bias corrected fields for subsequent impact studies and dynamical downscaling provides input conditions that do not appropriately represent intervariable relationships in atmospheric fields. Here we investigate the consequences of the lack of dynamical consistency in bias correction using a measure of model consistency—the potential vorticity (PV). This paper presents an assessment of the biases present in PV using two alternative correction techniques—an approach where bias correction is performed individually on each atmospheric variable, thereby ignoring the physical relationships that exists between the multiple variables that are corrected, and a second approach where bias correction is performed directly on the PV field, thereby keeping the system dynamically coherent throughout the correction process. In this paper we show that bias correcting variables independently results in increased errors above the tropopause in the mean and standard deviation of the PV field, which are improved when using the alternative proposed. Furthermore, patterns of spatial variability are improved over nearly all vertical levels when applying the alternative approach. Results point to a need for a dynamically consistent atmospheric bias correction technique which results in fields that can be used as dynamically consistent lateral boundaries in follow-up downscaling applications.

On the Atmospheric Correction of Antarctic Airborne Hyperspectral Data

The first airborne hyperspectral campaign in the Antarctic Peninsula region was carried out by the British Antarctic Survey and partners in February 2011. This paper presents an insight into the applicability of currently available radiative transfer modelling and atmospheric correction techniques for processing airborne hyperspectral data in this unique coastal Antarctic environment. Results from the Atmospheric and Topographic Correction version 4 (ATCOR-4) package reveal absolute reflectance values somewhat in line with laboratory measured spectra, with Root Mean Square Error (RMSE) values of 5% in the visible near infrared (0.4–1 µm) and 8% in the shortwave infrared (1–2.5 µm). Residual noise remains present due to the absorption by atmospheric gases and aerosols, but certain parts of the spectrum match laboratory measured features very well. This study demonstrates that commercially available packages for carrying out atmospheric correction are capable of correcting airborne hyperspectral data in the challenging environment present in Antarctica. However, it is anticipated that future results from atmospheric correction could be improved by measuring in situ atmospheric data to generate atmospheric profiles and aerosol models, or with the use of multiple ground targets for calibration and validation.

천리안위성 해양탑재체 자료를 이용한 대기산란 효과가 제거된 컬러합성 영상 제작

세계 최초의 정지궤도 해양관측 위성인 천리안 위성의 해색 센서인 Geostationary Ocean Color Imager (GOCI)는 2011년 4월부터 원시 자료를 생산하고 있다. 본 연구에서는 GOCI 원시 자료로부터 보다 자연색에 가까운 컬러합성영상 제작을 위한 방법론과 자료처리용 소프트웨어인 GOCI RGB Maker를 개발하였다. GOCI 원시자료는 대기보정과 재투영 기법을 이용하여 최종적으로 컬러합성영상을 제작할 수 있도록 최적화된 알고리듬을 구현하였다. 이 알고리즘이 적용된 소프트웨어는 다양한 하드웨어 환경에서도 선택적으로 관심영역과 출력창의 크기를 입력받아 처리할 수 있도록 제작되어 교육적 효과를 높였다. GOCI RGB Maker는 공개용 소프트웨어로서, GOCI 자료에 대한 이해와 활용을 증대시킬 수 있을 것이다. 또한, 정지궤도 관측 영상은 관측 영역의 환경특성 변화를 감시하는데 훌륭한 역할을 할 수 있을 것으로 기대된다. The Geostationary Ocean Color Imager (GOCI), the first geostationary ocean color observation instrument launched in 2010 on board the Communication, Ocean, and Meteorological Satellite (COMS), has been generating the operational level 1 data. This study describes a methodology for creating the GOCI true color image and data processing software, namely the GOCI RGB maker. The algorithm uses a generic atmospheric correction and reprojection technique to produce the color composite image. Especially, the program is designed for educational purpose in a way that the region of interest and image size can be determined by the user. By distributing software to public, it would maximize the understanding and utilizing the GOCI data. Moreover, images produced from the geostationary observations are expected to be an excellent tool for monitoring environmental changes.

Application of a probability density function-based atmospheric light-scattering correction to carbon dioxide retrievals from GOSAT over-sea observations

We present the application of a photon path length probability density function (PPDF) formalism to atmospheric carbon dioxide (CO 2) retrievals from reflected sunlight measured by the Greenhouse Gases Observing Satellite (GOSAT) over the ocean. GOSAT short-wave infrared (SWIR) radiance spectra detected over the ocean surface were shown to be strongly affected by atmospheric light-scattering. In particular, retrievals of column-averaged CO 2 dry-volume mixing ratios (XCO 2) were characterised by steady negative bias and significant scatter when optical path modification due to high variability of clouds and aerosols was neglected. Considering that the ocean surface in SWIR is dark in all directions except that of sun-glint observation, PPDF radiative transfer modelling was simplified by neglecting the contribution of photons that interacted with both aerosols/cloud particles and the ocean surface. This permitted implementation of an atmospheric correction technique based on simultaneous retrievals of CO 2 concentrations and two PPDF parameters: effective altitude of the aerosol layer and relative layer reflectivity. Using this correction, both bias and scatter of carbon dioxide retrievals were significantly reduced. The retrieval results of XCO 2 statistically agreed (both spatially and temporally) with those predicted by the atmospheric transport model.

Haze removal based on advanced haze-optimized transformation (AHOT) for multispectral imagery

Ever-present spatially varying haze contamination in satellite scenes limits applications using visible and near-infrared bands of low-temporal-resolution multispectral satellite images. A relative atmospheric correction technique, the virtual cloud point (VCP) method, which is based on advanced haze-optimized transformation (AHOT), is developed for haze removal. It is an improved algorithm of the previous dark-object subtraction (DOS) based on haze-optimized transformation (HOT). In AHOT, extra steps are added to HOT to remove confusion caused by some land-cover types. The VCP method uses the upper bound of the histogram, as well as the lower bound, so that it enlarges the digital number (DN) variance reduced by haze, which is not considered in DOS. To evaluate this algorithm, hazy subsets of one Landsat Thematic Mapper (TM) and one QuickBird image are employed. Through before-and-after comparison using true-colour images and the normalized difference vegetation index (NDVI), it proves that the VCP method based on AHOT is apparently better than DOS based on HOT, when haze is distributed over urban areas where vegetation is sparse.