Top Of Atmosphere Radiance Research Articles

Characterization of the aerosol contribution to the top-of-atmosphere radiance for satellite ocean color retrievals

A method is proposed for characterization of the aerosol contribution to the top-of-atmosphere (TOA) radiance. The method is based on solving the problem of radiative transfer in the atmosphere-ocean system and expanding the solution in powers of the aerosol optical thickness τ a . We show that the linear term of the expansion is analytically expressed in terms of the bidirectional transmittance/reflectance of the aerosol-free Rayleigh atmosphere. A procedure is also proposed for successively extracting the terms of higher order in τ a from the data of the TOA radiance computation with the DISORT code. As analysis shows, the radiance expansion in τ a is not purely polynomial. Beginning from the quadratic term, the coefficients of the series expansion in powers of τ a become dependent logarithmically on τ a . The approach proposed enables us to reproduce analytically the τ a -dependence of the TOA radiance with controlled accuracy. We determine the expansion coefficients up to the cubic term inclusive and validate our results on the aerosol model embedded in NASA’s SeaDAS algorithm for aerosol loadings, representative for the Barents and Kara seas. In the visible and near-infrared spectral ranges, accounting for the terms up to a quadratic one is found to be sufficient for the atmospheric correction of satellite ocean color data typical for the Arctic region.

Gaussian Process Regression Hybrid Models for the Top-of-Atmosphere Retrieval of Vegetation Traits Applied to PRISMA and EnMAP Imagery

The continuous monitoring of the terrestrial Earth system by a growing number of optical satellite missions provides valuable insights into vegetation and cropland characteristics. Satellite missions typically provide different levels of data, such as level 1 top-of-atmosphere (TOA) radiance and level 2 bottom-of-atmosphere (BOA) reflectance products. Exploiting TOA radiance data directly offers the advantage of bypassing the complex atmospheric correction step, where errors can propagate and compromise the subsequent retrieval process. Therefore, the objective of our study was to develop models capable of retrieving vegetation traits directly from TOA radiance data from imaging spectroscopy satellite missions. To achieve this, we constructed hybrid models based on radiative transfer model (RTM) simulated data, thereby employing the vegetation SCOPE RTM coupled with the atmosphere LibRadtran RTM in conjunction with Gaussian process regression (GPR). The retrieval evaluation focused on vegetation canopy traits, including the leaf area index (LAI), canopy chlorophyll content (CCC), canopy water content (CWC), the fraction of absorbed photosynthetically active radiation (FAPAR), and the fraction of vegetation cover (FVC). Employing band settings from the upcoming Copernicus Hyperspectral Imaging Mission (CHIME), two types of hybrid GPR models were assessed: (1) one trained at level 1 (L1) using TOA radiance data and (2) one trained at level 2 (L2) using BOA reflectance data. Both the TOA- and BOA-based GPR models were validated against in situ data with corresponding hyperspectral data obtained from field campaigns. The TOA-based hybrid GPR models revealed a range of performance from moderate to optimal results, thus reaching R2 = 0.92 (LAI), R2 = 0.72 (CCC) and 0.68 (CWC), R2 = 0.94 (FAPAR), and R2 = 0.95 (FVC). To demonstrate the models’ applicability, the TOA- and BOA-based GPR models were subsequently applied to imagery from the scientific precursor missions PRISMA and EnMAP. The resulting trait maps showed sufficient consistency between the TOA- and BOA-based models, with relative errors between 4% and 16% (R2 between 0.68 and 0.97). Altogether, these findings illuminate the path for the development and enhancement of machine learning hybrid models for the estimation of vegetation traits directly tailored at the TOA level.

Effects of the Earth curvature on Mie-scattering radiances at high solar-sensor geometries based on Monte Carlo simulations.

Given the importance of vector radiative transfer models in ocean color remote sensing and a lack of suitable models capable of analyzing the Earth curvature effects on Mie-scattering radiances, this study presents an enhanced vector radiative transfer model for a spherical shell atmosphere geometry by the Monte Carlo method (MC-SRTM), considering the effects of Earth curvature, different atmospheric conditions, flat sea surface reflectance, polarization, high solar and sensor geometries, altitudes and wavelengths. A Monte Carlo photon transport model was employed to simulate the vector radiative transfer processes and their effects on the top-of-atmosphere (TOA) radiances. The accuracy of the MC-SRTM was verified by comparing its scalar model outputs from Henyey-Greenstein (HG) phase function with the Kattawar-Adams model results, and the mean relative differences were less than 2.75% and 4.33% for asymmetry factors (g-values) of 0.5 and 0.7, respectively. The vector mode results of MC-SRTM for a spherical shell geometry with the Mie-scattering phase matrix were compared with the PCOART-SA model results (from Polarized Coupled Ocean-Atmosphere Radiative Transfer model based on the pseudo-spherical assumption), and the mean relative differences were less than 2.67% when solar zenith angles (SZAs) > 70 ∘ and sensor viewing zenith angles (VZAs) < 60 ∘ for two aerosol models (coastal and tropospheric models). Based on the MC-SRTM, the effects of Earth curvature on TOA radiances at high SZAs and VZAs were analyzed. For pure aerosol atmosphere, the effects of Earth curvature on TOA radiances reached up to 5.36% for SZAs > 70 ∘ and VZAs < 60 ∘ and reduced to less than 2.60% for SZAs < 70 ∘ and VZAs > 60 ∘. The maximum Earth curvature effect of pure aerosol atmosphere was nearly same (10.06%) as that of the ideal molecule atmosphere. The results also showed no statistically significant differences for the aerosol-molecule mixed and pure aerosol atmospheres. Our study demonstrates that there is a need to consider the Earth curvature effects in the atmospheric correction of satellite ocean color data at high solar and sensor geometries.

Single field-of-view sounder atmospheric product retrieval algorithm: establishing radiometric consistency for hyper-spectral sounder retrievals

Abstract. The single field-of-view (SFOV) sounder atmospheric product (SiFSAP) retrieval algorithm has been developed to address the need to retrieve high-spatial-resolution atmospheric data products from hyper-spectral sounders and ensure the radiometric consistency between the retrieved properties and measured spectral radiances. It is based on an integrated optimal-estimation inversion scheme that processes data from the satellite-based synergistic microwave (MW) and infrared (IR) spectral measurements from advanced sounders. The retrieval system utilizes the principal component radiative transfer model (PCRTM), which performs radiative transfer calculations monochromatically and includes accurate cloud-scattering simulations. SiFSAP includes temperature, water vapor, surface skin temperature and emissivity, cloud height and microphysical properties, and concentrations of essential trace gases for each SFOV at a native instrument spatial resolution. Error estimations are provided based on a rigorous analysis for uncertainty propagation from the top-of-atmosphere (TOA) spectral radiances to the retrieved geophysical properties. As a comparison, the spatial resolution for the traditional hyper-spectral sounder retrieval products is much coarser than the native resolution of the instruments due to the common use of the “cloud-clearing” technique to compensate for the lack of cloud-scattering simulation in the forward model. The degraded spatial resolution in traditional cloud-clearing sounder retrieval products limits their applications for capturing meteorological or climate signals at finer spatial scales. Moreover, a rigorous uncertainty propagation estimation needed for long-term climate trend studies cannot be given due to the lack of direct radiative transfer relationships between the observed TOA radiances and the retrieved geophysical properties. With the advantages of the higher spatial resolution; the simultaneous retrieval of atmospheric, cloud, and surface properties using all available spectral information; and the establishment of “radiance closure” in the sounder spectral measurements, the SiFSAP provides additional information needed for various weather and climate studies and applications using sounding observations. This paper gives an overview of the SiFSAP retrieval algorithm and assessment of SiFSAP atmospheric temperature, water vapor, clouds, and surface products derived from the Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) data.

Generation of global 1-km daily top-of-atmosphere outgoing longwave radiation product from 2000 to 2021 using machine learning

ABSTRACT Top-of-atmosphere (TOA) outgoing longwave radiation (OLR), a key component of the Earth’s energy budget, serves as a diagnostic of the Earth’s climate system response to incoming solar radiation. However, existing products are typically estimated using broadband sensors with coarse spatial resolutions. This paper presents a machine learning method to estimate TOA OLR by directly linking Moderate Resolution Imaging Spectroradiometer (MODIS) TOA radiances with TOA OLR determined by Clouds and the Earth’s Radiant Energy System (CERES) and other information, such as the viewing geometry, land surface temperature and cloud top temperature determined by Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). Models are built separately under clear- and cloudy-sky conditions using a gradient boosting regression tree. Independent test results show that the root mean square errors (RMSEs) of the clear-sky and cloudy-sky models for estimating instantaneous values are 4.1 and 7.8 W/m2, respectively. Real-time conversion ratios derived from CERES daily and hourly OLR data are used to convert the instantaneous MODIS OLR to daily results. Inter-comparisons of the daily results show that the RMSE of the estimated MODIS OLR is 8.9 W/m2 in East Asia. The developed high resolution dataset will be beneficial in analyzing the regional energy budget.

Spectrum recovery from multispectral ocean color data using system spectral shape factor

Data from ocean color monitoring sensors at different spectral channels are available for remote sensing of radiation as seen in the given spectral windows, which is used for deriving information on various atmospheric parameters. However, recent studies have demonstrated the potential of hyperspectral (HS) data over multispectral ocean color (MSOC) data in accurately estimating phytoplankton concentration and in monitoring the coastal dynamics. We propose system spectral shape factor (SSSF)-based approach to recover the embedded HS top-of-atmosphere (TOA) radiance (TOARAD) from the MSOC data. SSSF is defined as convolution of normalized input spectrum and sensor spectral response function (SRF). The advantage of SSSF is that it decouples magnitude and spectral shape part of sensor output and enables recovery of TOARAD. To test this method, the airborne visible/infrared imaging spectrometer-next generation data are used to simulate inputs to MSOC. SRF of ocean color monitor simulated MSOC. SSSF of TOARAD is estimated using SSSF of model-based path radiance spectrum of the pixel, which is similar in spectral shape. Methodology, developed using data from five stations, is validated with data from other five stations. The procedure is successfully repeated using SRFs of sea-viewing wide-field-of-view sensor. The recovered HS data are found to be consistent with the original spectra with very small deviations in spectral angle map (<0.012 rad) spectral information divergence (<5.8 × 10 − 5), mean percentage relative error (MPRE) of TOARAD (<0.7 % ), and MPRE of TOA water leaving radiance (<5.8 % ). This approach possibly opens up research for application of HS analysis on MSOC recovered spectra and for optimization of sensor configurations.

Sea Surface Temperature Gradients Estimation Using Top-of-Atmosphere Observations from the ESA Earth Explorer 10 Harmony Mission: Preliminary Studies

The Harmony satellite mission was recently approved as the next European Space Agency (ESA) Earth Explorer 10. The mission science objectives cover several applications related to solid earth, the cryosphere, upper-ocean dynamics and air–sea interactions. The mission consists of a constellation of two satellites, flying with the Copernicus Sentinel 1 (C or D) spacecraft, each hosting a C-band receive-only radar and a thermal infrared (TIR) payload. From an ocean dynamics/air–sea interaction perspective, the mission will provide the unique opportunity to observe simultaneously the signature of submesoscale upper-ocean processes via synthetic aperture radar and TIR imagery. The TIR imager is based on microbolometer technology and its acquisitions will rely on four channels: three narrow-band channels yielding observations at a ≃1 km spatial sampling distance (SSD) and a panchromatic (PAN, 8–12 μm) channel characterized by a ≃300 m SSD. Our study investigates the potential of Harmony in retrieving spatial features related to sea surface temperature (SST) gradients from the high-resolution PAN channel, relying on top-of-atmosphere (TOA) observations. Compared to a standard SST gradient retrieval, our approach does not require atmospheric correction, thus avoiding uncertainties due to inter-channel co-registration and radiometric consistency, with the possibility of exploiting the higher resolution of the PAN channel. The investigations were carried out simulating the future Harmony TOA radiances (TARs), as well as relying on existing state-of-the-art level 1 satellite products. Our approach enables the correct description of SST features at the sea surface avoiding the generation of spurious features due to atmospheric correction and/or instrumental issues. In addition, analyses based on existing satellite products suggest that the clear-sky TOA observations, in a typical mid-latitude scene, allow the reconstruction of up to 85% of the gradient magnitudes found at the sea-surface level. The methodology is less efficient in tropical areas, suffering from smoothing effects due to the high concentrations of water vapor.

On-Orbit Absolute Radiometric Calibration and Uncertainty Analysis of OCM2 Sensor onboard Oceansat-2 Satellite

ABSTRACT This study describes On-Orbit absolute radiometric calibration for the Ocean Colour Monitor2 (OCM2) onboard Oceansat-2 satellite through a vicarious calibration experiment performed at the Great Rann of Kutch calibration site in Gujarat, India, in February 2022. To achieve accurate and consistent calibration for the OCM2 channels, a reflectance-based calibration method was used which relies on synchronous in-situ measurements of surface reflectance and atmospheric parameters at the time of satellite overpass. In this exercise, the 6 SV (Second Simulation of a Satellite Signal in the Solar Spectrum Vector) radiative transfer (RT) model was used to simulate the Top Of Atmosphere (TOA) spectral radiance for the OCM2 channels. The on-orbit radiometric performance/changes were derived by comparing the 6 SV simulated TOA radiance with those of the OCM2 Level 1B (L1B) data product. The results indicate that the average gain for band 1 to band 8 was found in the range from 0.88 to 1.23 and the relative error from 1.58% to 18.79% for the OCM2 sensor. However, the Root-Mean-Square-Error (RMSE) between the OCM2 measured and the 6 SV simulated TOA radiance data range from 0.17 (µW cm−2 sr−1 nm−1) at 443 nm to 2.04 (µW cm−2 sr−1 nm−1) at 620 nm. Furthermore, we analysed in detail the various uncertainties in this approach emanating from surface reflectance, atmospheric conditions (ozone, water vapour and aerosol optical depth), aerosol-type assumption in the RT model, BRDF, and inherent accuracy of the 6 SV RT model. The overall uncertainty was within 6% estimated using the reflectance-based calibration method.

A Spatial Downscaling Method for Deriving High-Resolution Downward Shortwave Radiation Data Under All-Sky Conditions

Downward shortwave radiation (DSR) is an essential parameter in land surface energy budget. However, current DSR products are mainly generated at coarse-resolution scales (more than 5 km) and fail to accurately depict DSR distribution over different topographic and land cover conditions. Meanwhile, the existence of frequent cloud cover constrains the high-resolution DSR estimation. To overcome the above issues, a novel spatial downscaling method for high-resolution DSR estimation was proposed in this study by incorporating coarse-resolution Meteosat Second Generation (MSG) DSR product and Landsat-8 observations. Through decomposing the downscaling scheme into three separate models: fully cloudy, partial cloudy, and cloud-free, the 3 km MSG DSR data was spatially downscaled to 30 m scale under all-sky conditions, based on the assumption of scale-invariant of the models established at 3 km scale. An empirical model for DSR estimation under cloud cover condition was constructed between the top of atmosphere radiance from Landsat-8 and MSG DSR. The downscaled results showed reasonable DSR values under different cloud cover conditions and the spatial heterogeneity of the downscaled DSR was also well depicted with the variation of surface topography. Meanwhile, the validation with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> measurements also revealed the significant improvement in terms of the coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) (from 0.53 to 0.79) and the root mean squared error (RMSE) (from 198.5 to 140.41 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). In general, the proposed downscaling method in this study show good potential for high-resolution DSR estimation without regard to the atmospheric information required in traditional DSR estimation under all-sky condition.

A Direct Method for the Estimation of Top-of-Atmosphere Outgoing Longwave Radiation from Himawari-8/AHI Data

Top-of-atmosphere (TOA) outgoing longwave radiation (OLR), a key component of the Earth’s energy budget, serves as a diagnostic of the Earth’s climate system response to incoming solar radiation. However, existing products are typically estimated using the traditional two-step method, which may bring extra uncertainties. This paper presents a direct machine learning method to estimate TOA OLR by directly linking Himawari-8/Advanced Himawari Imager (AHI) TOA radiances with TOA OLR determined by Clouds and the Earth’s Radiant Energy System (CERES) and other information, such as the viewing geometry. Models are built separately under clear- and cloudy-sky conditions using a gradient-boosting regression tree. Independent test results show that the root mean square errors (RMSEs) of the clear-sky and cloudy-sky models for estimating instantaneous values are 7.46 W/m2 (3.0%) and 11.61 W/m2 (5.8%), respectively. Daily results are obtained by averaging all the instantaneous results in one day. Intercomparisons of the daily results with CERES TOA OLR data show that the RMSE of the estimated AHI OLR is ~6 W/m2 (3%). The developed high-resolution AHI TOA OLR dataset will be beneficial in analyzing the regional energy budget.

Using Machine Learning for Timely Estimates of Ocean Color Information From Hyperspectral Satellite Measurements in the Presence of Clouds, Aerosols, and Sunglint

Retrievals of ocean color from space are important for better understanding of the ocean ecosystem but can be limited under conditions such as clouds, aerosols, and sunglint. Many ocean color algorithms use a few selected spectral bands to perform an atmospheric correction and then derive the upwelling radiance from the ocean. The limitations in the atmospheric correction under certain conditions lead to many gaps in daily spatial coverage of ocean color retrievals. To address these limitations, we introduce a new approach that uses machine learning to estimate ocean color from top of atmosphere radiances or reflectance measurements. In this approach, a principal component analysis is used to decompose the hyperspectral measurements into spectral features that describe the scattering and absorption of the atmosphere and the underlying surface. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from the MODIS atmospheric correction algorithm. This machine learning approach is independent of a priori information and does not rely on any radiative transfer modeling. We apply the approach to two hyperspectral UV/VIS instruments, the ozone monitoring instrument (OMI) and the TROPOspheric Monitoring Instrument (TROPOMI), using measurements from 320–500 nm to show that it can be used to reproduce ocean color properties in less-than-ideal conditions. This machine learning approach complements the current atmospheric correction ocean color retrievals by filling in the gaps resulting from cloud, aerosol, and sunglint contamination. This method can be applied to the future hyperspectral Ocean Color Instrument (OCI), which will be onboard NASA’s Plankton, Aerosol Cloud, ocean Ecosystem (PACE) ocean color satellite set to launch in 2024.

Atmosphere and Terrain Coupling Simulation Framework for High-Resolution Visible-Thermal Spectral Imaging over Heterogeneous Land Surface

Realistic modeling of high-resolution earth radiation signals in the visible-thermal spectral domain remains difficult, due to the complex radiation interdependence induced by the heterogeneous and rugged features of land surface. To find the trade-off between accuracy and efficiency for image simulation, this paper established a unified simulation framework for the entire visible-thermal spectral domain, based on the energy balance between solar-reflected and thermal radiation components over rugged surfaces. Considering the joint contributions of atmospheric and topographic adjacency effects, three spatial–spectral convolution kernels were uniformly designed to quantify the topographic irradiance, the trapping effect, and the atmospheric adjacency effect. Radiation signal simulation was implemented in three forms: land surface temperature (LST), bottom of atmosphere (BOA) radiance, and top of atmosphere (TOA) radiance. The accuracy was validated with onboard data from China’s Gaofen-5 visual and infrared multispectral sensor (VIMS) over rugged desert. The simulation results demonstrate that the root mean square of relative deviations between the simulated and onboard TOA radiance are related to terrain, as 3–17% and 6–38% for the summer and winter scene, respectively. The evaluation of radiance components indicates the utility of the simulation framework to quantify the uncertainty associated with atmosphere and terrain coupling effects, in the sensor design and operation stages.

PRISMA L1 and L2 Performances within the PRISCAV Project: The Pignola Test Site in Southern Italy

In March 2019, the PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral satellite was launched by the Italian Space Agency (ASI), and it is currently operational on a global basis. The mission includes the hyperspectral imager PRISMA working in the 400–2500 nm spectral range with 237 bands and a panchromatic (PAN) camera (400–750 nm). This paper presents an evaluation of the PRISMA top-of-atmosphere (TOA) L1 products using different in situ measurements acquired over a fragmented rural area in Southern Italy (Pignola) between October 2019 and July 2021. L1 radiance values were compared with the TOA radiances simulated with a radiative transfer code configured using measurements of the atmospheric profile and the surface spectral characteristics. The L2 reflectance products were also compared with the data obtained by using the ImACor code atmospheric correction tool. A preliminary assessment to identify PRISMA noise characteristics was also conducted. The results showed that: (i) the PRISMA performance, as measured at the Pignola site over different seasons, is characterized by relative mean absolute differences (RMAD) of about 5–7% up to 1800 nm, while a decrease in accuracy was observed in the SWIR; (ii) a coherent noise could be observed in all the analyzed images below the 630th scan line, with a frequency of about 0.3–0.4 cycles/pixel; (iii) the most recent version of the standard reflectance L2 product (i.e., Version 2.05) matched well the reflectance values obtained by using the ImACor atmospheric correction tool. All these preliminary results confirm that PRISMA imagery is suitable for an accurate retrieval of the bio-geochemical variables pertaining to a complex fragmented ecosystem such as that of the Southern Apennines. Further studies are needed to confirm and monitor PRISMA data performance on different land-cover areas and on the Radiometric Calibration Network (RadCalNet) targets.

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Radiometric Cross-Calibration of Large-View-Angle Satellite Sensors Using Global Searching to Reduce BRDF Influence

Satellite sensors with large view angles can provide wide-swath imaging for earth observations, which pose new challenges for cross-calibration due to bidirectional reflectance distribution function (BRDF). To address these challenges, we adopted global searching (GS) algorithms to “search” calibration coefficients with BRDF considered. The GS-based methods were implemented to calibrate two Chinese large-view-angle sensors: the Gaofen-1 first wide-field-of-view (WFV1) camera and Gaofen-4 panchromatic multispectral sensor (PMS) with Landsat-8/Operational Land Imager (OLI) as references. Validations were conducted by evaluating the top of atmosphere (TOA) radiance and surface reflectance using synchronous OLI data. The mean relative biases (MRBs) of the GS-derived TOA radiance for the WFV1 were smaller than 5.5% compared with the OLI-simulated TOA radiance, while those of using official coefficients were up to 13%. The performances of the GS method and traditional method using the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF products for BRDF correction are comparable. The GS-based scheme has the potential to correct BRDF during cross-calibration and thus free cross-calibration of large-view-angle sensors from BRDF models and products.

First Evaluation of PRISMA Level 1 Data for Water Applications.

This study presents a first assessment of the Top-Of-Atmosphere (TOA) radiances measured in the visible and near-infrared (VNIR) wavelengths from PRISMA (PRecursore IperSpettrale della Missione Applicativa), the new hyperspectral satellite sensor of the Italian Space Agency in orbit since March 2019. In particular, the radiometrically calibrated PRISMA Level 1 TOA radiances were compared to the TOA radiances simulated with a radiative transfer code, starting from in situ measurements of water reflectance. In situ data were obtained from a set of fixed position autonomous radiometers covering a wide range of water types, encompassing coastal and inland waters. A total of nine match-ups between PRISMA and in situ measurements distributed from July 2019 to June 2020 were analysed. Recognising the role of Sentinel-2 for inland and coastal waters applications, the TOA radiances measured from concurrent Sentinel-2 observations were added to the comparison. The results overall demonstrated that PRISMA VNIR sensor is providing TOA radiances with the same magnitude and shape of those in situ simulated (spectral angle difference, SA, between 0.80 and 3.39; root mean square difference, RMSD, between 0.98 and 4.76 [mW m−2 sr−1 nm−1]), with slightly larger differences at shorter wavelengths. The PRISMA TOA radiances were also found very similar to Sentinel-2 data (RMSD < 3.78 [mW m−2 sr−1 nm−1]), and encourage a synergic use of both sensors for aquatic applications. Further analyses with a higher number of match-ups between PRISMA, in situ and Sentinel-2 data are however recommended to fully characterize the on-orbit calibration of PRISMA for its exploitation in aquatic ecosystem mapping.

The Impact of Choice of Solar Spectral Irradiance Model on Atmospheric Correction of Landsat 8 OLI Satellite Data

Many atmospheric correction schemes of radiance-based optical satellite data require the selection of normalized solar spectral irradiance models at the top of atmosphere (TOA). However, there is no scientific consensus in literature as to which available model is most suitable. This article examines five commonly used models applied to Landsat 8 Operational Land Imager (OLI) TOA radiance and reflectance products to assess the accuracy and stability between models used to derive surface reflectance products. It is assumed that the calibration of the United States Geological Survey (USGS) Landsat 8 OLI TOA reflectance and radiance products are accurate to currently claimed levels. The results show that the retrieved surface reflectance can exhibit significant variations when different solar irradiance models are used, especially in the OLI coastal blue band at 443 nm. From the five solar irradiance models, the Kurucz 2005 model showed the least bias compared with OLI TOA reflectance product and least variance in surface reflectance. Furthermore, improvement was obtained by adjusting the total solar irradiance (TSI) normalization, and additional validation was provided using observed in situ water leaving reflectance data. The results from this article are particularly relevant to aquatic applications and to satellite sensors that provide TOA radiance such as previous Landsat and other current and historical missions.

Gaussian processes retrieval of LAI from Sentinel-2 top-of-atmosphere radiance data

Retrieval of vegetation properties from satellite and airborne optical data usually takes place after atmospheric correction, yet it is also possible to develop retrieval algorithms directly from top-of-atmosphere (TOA) radiance data. One of the key vegetation variables that can be retrieved from at-sensor TOA radiance data is leaf area index (LAI) if algorithms account for variability in atmosphere. We demonstrate the feasibility of LAI retrieval from Sentinel-2 (S2) TOA radiance data (L1C product) in a hybrid machine learning framework. To achieve this, the coupled leaf-canopy-atmosphere radiative transfer models PROSAIL-6SV were used to simulate a look-up table (LUT) of TOA radiance data and associated input variables. This LUT was then used to train the Bayesian machine learning algorithms Gaussian processes regression (GPR) and variational heteroscedastic GPR (VHGPR). PROSAIL simulations were also used to train GPR and VHGPR models for LAI retrieval from S2 images at bottom-of-atmosphere (BOA) level (L2A product) for comparison purposes. The BOA and TOA LAI products were consistently validated against a field dataset with GPR (R2 of 0.78) and with VHGPR (R2 of 0.80) and for both cases a slightly lower RMSE for the TOA LAI product (about 10% reduction). Because of delivering superior accuracies and lower uncertainties, the VHGPR models were further applied for LAI mapping using S2 acquisitions over the agricultural sites Marchfeld (Austria) and Barrax (Spain). The models led to consistent LAI maps at BOA and TOA scale. The LAI maps were also compared against LAI maps as generated by the SNAP toolbox, which is based on a neural network (NN). Maps were again consistent, however the SNAP NN model tends to overestimate over dense vegetation cover. Overall, this study demonstrated that hybrid LAI retrieval algorithms can be developed from TOA radiance data given a cloud-free sky, thus without the need of atmospheric correction. To the benefit of the community, the development of such hybrid models for the retrieval vegetation properties from BOA or TOA images has been streamlined in the freely downloadable ALG-ARTMO software framework.

Combination of Cross- and Inter-Band Radiometric Calibrations for a Hyperspectral Sensor Using Model-Based Spectral Band Adjustment

Recently, the growing number of hyperspectral satellite sensors have increased the demand for a flexible and robust approach to their calibration. This paper proposes an operational method for the simultaneous correction of inter-sensor and inter-band biases in hyperspectral sensors via the soil line concept for spectral band adjustment. Earth Observing-1 Hyperion was selected as an example, with the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) as a reference. The results over the Railroad Valley Playa calibration site indicated that the discrepancy in the analogous bands between Hyperion and MODIS during 2001–2008 was approximately 4–6% and 7–9% of the root-mean-square error in the top-of-atmosphere (TOA) radiance at the visible and near-infrared region and shortwave infrared region, respectively. For all Hyperion bands, the relative cross-calibration coefficients during this period were calculated (typically ranging from 0.9 to 1.1) to correct the Hyperion TOA radiance to be consistent with the MODIS and the other Hyperion bands. The application of the proposed approach could allow for more flexible cross-calibration of irregular-orbit sensors aboard the International Space Station.

Radiometric Calibration and Uncertainty Analysis of KOMPSAT-3A Using the Reflectance-Based Method.

In recent years, Korea has sustained consistent access to remote sensed data by launching Korea Multi-Purpose Satellite-3A (KOMPSAT-3A, K3A)—an updated version of the high-resolution KOMPSAT series. This KOMPSAT-3A required calibration and validation (Cal/Val) before and after its launch to enable proper functional characterization and to maintain the veracity of data collected. The Korea Aerospace Research Institute (KARI) executed the initial prelaunch calibration in the laboratory and we performed the Cal/Val of KOMPSAT-3A during the Launch and Early Operation Phase (LEOP) in the field. Two suitable sites in Korea and Mongolia with stable weather, almost uniform terrain, and near Lambertian diffusion, provided the necessary tarp reflectance to calculate the absolute radiometric calibration coefficients. The surface reflectance was determined using 12 and four well-calibrated reference reflectance tarps employing the FieldSpec® 3(Analytical Spectral Devices Inc., Boulder, CO, USA) Spectroradiometer. Subsequently, the top of atmosphere (TOA) radiance was estimated using radiative transfer code (RTC) software based on the Atmospheric and Topographic Correction (ATCOR). In addition, cross calibration was simultaneously performed at the Libya-4 pseudo invariant calibration site (PICS) for KOMPSAT-3A TOA radiance, using the spectral band adjustment factor (SBAF) compensated Landsat 8 reflectance and the Second Simulation of Satellite Signal in the Solar Spectrum (6S) to compute cross calibration coefficients. The results of the KOMPSAT-3A absolute calibration coefficient show that the R2 values were over 0.99, implying a significant correlation for almost all bands between the TOA radiance and the KOMPSAT-3A spectral band response at both campaign sites. However, this study reveals a difference of less than 5% calibration gains for all bands compared to the prelaunch values, while the cross calibration gain is below 5% in visible bands and above 5% in the near infrared band. An effort to optimize the reliability of the absolute calibration coefficients resorted to the rigorous quantification of uncertainties amongst atmospheric conditions, the digital number (DN), the reflectance tarp, the bidirectional reflectance distribution function (BRDF), and ozone levels. Therefore, we presumed that the total uncertainty was 4.27%, which conforms to some published results.