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The correction of atmospheric effects on optical remote sensing products is an essential component of Analysis Ready Data (ARD) production lines. The MAJA processor aims at providing accurate time series of surface reflectances over land for satellite missions, such as Sentinel-2, Venμs, and Landsat 8. The Centre d'Études Spatiales de la Biosphère (CESBIO) and the Centre National d'Études Spatiales (CNES) share a common effort to maintain, validate, and improve the MAJA processor, using state-of-the-art ground measurement sites, and participating in processor inter-comparisons, such as the Atmospheric Correction Intercomparison Exercise (ACIX). While contributing to the second ACIX-II Land validation exercise, it was found that the candidate MAJA dataset could not adequately be compared to the main reference dataset. MAJA reflectances were corrected for adjacency and topography effects while the reference dataset was not, excluding MAJA from a part of the performance metrics of the exercise. The first part of the following study aims at providing complementary performance assessment to ACIX-II by reprocessing MAJA surface reflectances without adjacency nor topographic correction, allowing for an un-biased full resolution comparison with the reference Sentinel-2 dataset. The second part of the study consists of validating MAJA against surface reflectance measurements time series of up to five years acquired at three automated stations. Both approaches provide extensive insights on the quality of MAJA Sentinel-2 Level 2 products. [ABSTRACT FROM AUTHOR]

Atmospheric correction (AC) plays a critical role in the preprocessing of remote sensing images. Although AC is necessary for applications based on remote sensing inversion, it is not always required for those based on remote sensing classification. Recently, remote sensing statistical inference has been proposed for evaluating water quality. However, input data for these models have always been remote sensing reflectance (Rrs), which requires AC. This raises the question of whether AC is necessary for remote sensing statistical inference. We conducted a theoretical analysis and image validations by testing 24 water bodies observed by Landsat-8 and compared their spectral probability distributions (SPDs) calculated from Rrs before and after AC (using the ACOLITE model). Additionally, we tested and found that, if we use remote sensing inference as a tool to quantitatively infer statistical parameters of a specific waterbody, it is better to perform atmospheric correction. However, if the quantitative inference is applied to a large number of water bodies and high inference accuracy is not required, atmospheric correction may not be necessary, and a quick calculation based on the strong correlations between Rrs at the surface and sensor-observed reflectance can be used as a substitute. [ABSTRACT FROM AUTHOR]

Recently, ground-based hyperspectral imaging has come to the fore, supporting the arduous task of mapping near-vertical, difficult-to-access geological outcrops. The application of outcrop sensing within a range of one to several hundred metres, including geometric corrections and integration with accurate terrestrial laser scanning models, is already developing rapidly. However, there are few studies dealing with ground-based imaging of distant targets (i.e., in the range of several kilometres) such as mountain ridges, cliffs, and pit walls. In particular, the extreme influence of atmospheric effects and topography-induced illumination differences have remained an unmet challenge on the spectral data. These effects cannot be corrected by means of common correction tools for nadir satellite or airborne data. Thus, this article presents an adapted workflow to overcome the challenges of long-range outcrop sensing, including straightforward atmospheric and topographic corrections. Using two datasets with different characteristics, we demonstrate the application of the workflow and highlight the importance of the presented corrections for a reliable geological interpretation. The achieved spectral mapping products are integrated with 3D photogrammetric data to create large-scale now-called "hyperclouds", i.e., geometrically correct representations of the hyperspectral datacube. The presented workflow opens up a new range of application possibilities of hyperspectral imagery by significantly enlarging the scale of ground-based measurements. [ABSTRACT FROM AUTHOR]

Calculation errors in ocean-brightness coefficients in the Far Eastern are analyzed for two atmospheric correction algorithms (NIR and MUMM). The daylight measurements in different water types show that the main error component is systematic and has a simple dependence on the magnitudes of the coefficients. The causes of the error behavior are considered. The most probable explanation for the large errors in ocean-color parameters in the Far East is a high concentration of continental aerosol absorbing light. A comparison between satellite and in situ measurements at AERONET stations in the United States and South Korea has been made. It is shown the errors in these two regions differ by up to 10 times upon close water turbidity and relatively high aerosol optical-depth computation precision in the case of using the NIR correction of the atmospheric effect. [ABSTRACT FROM AUTHOR]

In the last decade, significant progress has been made in estimating Solar-Induced chlorophyll Fluorescence (SIF) by passive remote sensing techniques that exploit the oxygen absorption spectral regions. Although the O2-B and the deep O2-A absorption bands present a high sensitivity to detect SIF, these regions are also largely influenced by atmospheric effects. Therefore, an accurate Atmospheric Correction (AC) process is required to measure SIF from oxygen bands. In this regard, the suitability of a two-step approach, i.e., first an AC and second a Spectral Fitting technique to disentangle SIF from reflected light, has been evaluated. One of the advantages of the two-step approach resides in the derived intermediate products provided prior to SIF estimation, such as surface apparent reflectance. Results suggest that errors introduced in the AC, e.g., related to the characterization of aerosol optical properties, are propagated into systematic residual errors in the apparent reflectance. However, of interest is that these errors can be easily detected in the oxygen bands thanks to the high spectral resolution required to measure SIF. To illustrate this, the predictive power of the apparent reflectance spectra to detect and correct inaccuracies in the aerosols characterization is assessed by using a simulated database with SCOPE and MODTRAN radiative transfer models. In 75% of cases, the aerosol optical thickness, the Angstrom coefficient and the scattering asymmetry factor are corrected with a relative error below of 0.5%, 8% and 3%, respectively. To conclude with, and in view of future SIF monitoring satellite missions such as FLEX, the analysis of the apparent reflectance can entail a valuable quality indicator to detect and correct errors in the AC prior to the SIF estimation. [ABSTRACT FROM AUTHOR]

Microwave vegetation index (MVI) is a vegetation index defined in microwave bands. It has been developed based on observations from AMSR-E and widely used to monitor global vegetation. Recently, our study found that MVI was influenced by the atmosphere, although it was calculated from microwave bands. Ignoring the atmospheric influence might bring obvious uncertainty to the study of global vegetation. In this study, an atmospheric effect sensitivity analysis for MVI was carried out, and an atmospheric correction algorithm was developed to reduce the influence of the atmosphere. The sensitivity analysis showed that water vapor, clouds and precipitation were main parameters that had an influence on MVI. The result of the atmospheric correction on MVI was validated at both temporal and spatial scales. The validation showed that the atmospheric correction algorithm developed in this study could obviously improve the underestimation of MVI on most land surfaces. Seasonal patterns in the uncorrected MVI were obviously related to atmospheric water content besides vegetation changes. In addition, global maps of MVI showed significant differences before and after atmospheric correction in the northern hemisphere in the northern summer. The atmospheric correction will make the MVI more reliable and improve its performance in calculating vegetation biomass. [ABSTRACT FROM AUTHOR]

The Landsat derived differenced Normalized Burn Ratio (dNBR) is widely used for burn severity assessments. Studies of regional wildfire trends in response to climate change require consistency in dNBR mapping across multiple image dates, which may vary in atmospheric condition. Conversion of continuous dNBR images into categorical burn severity maps often requires extrapolation of dNBR thresholds from present fires for which field severity measurements such as Composite Burn Index (CBI) data are available, to historical fires for which CBI data are typically unavailable. Although differential atmospheric effects between image collection dates could lead to biased estimates of historical burn severity patterns, little is known concerning the influence of atmospheric effects on dNBR performance and threshold extrapolation. In this study, we compared the performance of dNBR calculated from six atmospheric correction methods using an optimality approach. The six correction methods included one partial (Top of atmosphere reflectance, TOA), two absolute, and three relative methods. We assessed how the correction methods affected the CBI-dNBR correlation and burn severity mapping in a Chinese boreal forest fire which occurred in 2010. The dNBR thresholds of the 2010 fire for each of the correction methods were then extrapolated to classify a historical fire from 2000. Classification accuracies of threshold extrapolations were assessed based on Cohen's Kappa analysis with 73 field-based validation plots. Our study found most correction methods improved mean dNBR optimality of the two fires. The relative correction methods generated 32% higher optimality than both TOA and absolute correction methods. All the correction methods yielded high CBI-dNBR correlations (mean R 2 = 0.847) but distinctly different dNBR thresholds for severity classification of 2010 fire. Absolute correction methods could substantially increase optimality score, but were insufficient to provide a consistent scale of radiometric condition between multi-temporal Landsat images, which resulted in lower severity classification accuracies (Kappa = 0.53) than those relative correction methods (Kappa = 0.72) for the 2000 fire. Consistent radiometric response in remote sensing datasets proved essential for accuracy in regional burn severity trends monitoring. Extrapolation of empirical dNBR thresholds to historical conditions without relative normalization will likely lead to biased burn severity classifications. [ABSTRACT FROM AUTHOR]

This study investigated the accuracy of the surface emissivity generated as an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) standard product applying the Temperature Emissivity Separation (TES) algorithm. We statistically compared the emissivity values using 61 daytime and 25 nighttime ASTER scenes at the Railroad Valley playa, USA from 2000 to 2012. The spectral emissivity was consistent within ±0.007 at bands 13 (10.60μm) and 14 (11.30μm), although the emissivity at bands 10 to 12 (8.30, 8.65, and 9.10μm) varied temporally, probably due to surface condition changes and errors in atmospheric correction. The atmospheric correction based on the climatology data resulted in poor accuracy in TES-retrieved emissivity for nighttime data because the contribution of the atmospheric effect on the at-sensor radiance increases due to decreased surface radiance in nighttime. For the National Centers for Environmental Prediction (NCEP) data, the agreement, which is comparable to the nominal accuracy (=0.015), between the emissivity based on the day and night ASTER data acquired on the same day suggested that NCEP data is sufficiently accurate to correct for the atmospheric effect on the ASTER data under nominally dry atmospheric condition. Although the spatial scales differ between ASTER data and laboratory data, the satellite and ground emissivity agreed within ±0.013. Despite the similarity of the spectral emissivity patterns, the ASTER emissivity was as much as 0.066 lower than the field emissivity at 8.30μm due perhaps to the roughness effect on the field emissivity. [ABSTRACT FROM AUTHOR]

Atmospheric correction is a fundamental process of ocean color remote sensing to remove the atmospheric effect from the top-of-atmosphere. Generally, Near Infrared (NIR) based algorithms perform well for clear waters, while Ultraviolet (UV) based algorithms can obtain good results for turbid waters. However, the latter tends to produce noisy patterns for clear waters. An ideal and practical solution to deal with such a dilemma is to apply NIR- and UV-based algorithms for clear and turbid waters, respectively. We propose a novel atmospheric correction method that integrates the advantages of UV- and NIR-based atmospheric correction (AC) algorithms for coastal ocean color remote sensing. The new approach is called UV-NIR combined AC algorithm. The performance of the new algorithm is evaluated based on match-ups between GOCI images and the AERONET-OC dataset. The results show that the values of retrieved Rrs (Remote Sensing Reflectance) at visible bands agreed well with the in-situ observations. Compared with the SeaDAS (SeaWiFS Data Analysis System) standard NIR algorithm, the new AC algorithm can achieve better precision and provide more available data. [ABSTRACT FROM AUTHOR]

Abstract: The NASA Moderate Resolution Imaging Spectroradiometer onboard the Aqua platform (MODIS-Aqua) provides a viable data stream for operational water quality monitoring of Chesapeake Bay. Marine geophysical products from MODIS-Aqua depend on the efficacy of the atmospheric correction process, which can be problematic in coastal environments. The operational atmospheric correction algorithm for MODIS-Aqua requires an assumption of negligible near-infrared water-leaving radiance, nL w(NIR). This assumption progressively degrades with increasing turbidity and, as such, methods exist to account for non-negligible nL w(NIR) within the atmospheric correction process or to use alternate radiometric bands where the assumption is satisfied, such as those positioned within shortwave infrared (SWIR) region of the spectrum. We evaluated a decade-long time-series of nL w(λ) from MODIS-Aqua in Chesapeake Bay derived using NIR and SWIR bands for atmospheric correction. Low signal-to-noise ratios (SNR) for the SWIR bands of MODIS-Aqua added noise errors to the derived radiances, which produced broad, flat frequency distributions of nL w(λ) relative to those produced using the NIR bands. The SWIR approach produced an increased number of negative nL w(λ) and decreased sample size relative to the NIR approach. Revised vicarious calibration and regional tuning of the scheme to switch between the NIR and SWIR approaches may improve retrievals in Chesapeake Bay, however, poor SNR values for the MODIS-Aqua SWIR bands remain the primary deficiency of the SWIR-based atmospheric correction approach. [ABSTRACT FROM AUTHOR]

Abstract: In order to obtain high quality data, the correction of atmospheric perturbations acting upon land surface reflectance measurements recorded by a space-based sensor is an important topic within remote sensing. For many years the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer model and the Simplified Method for Atmospheric Correction (SMAC) codes have been used for this atmospheric correction, but previous studies have shown that in a number of situations the quality of correction provided by the SMAC is low. This paper describes a method designed to improve the quality of the SMAC atmospheric correction algorithm through a slight increase in its computational complexity. Data gathered from the SEVIRI aboard Meteosat Second Generation (MSG) is used to validate the additions to SMAC, both by comparison to simulated data corrected using the highly accurate 6S method and by comparison to in-situ and 6S corrected SEVIRI data gathered for two field sites in Africa. The additions to the SMAC are found to greatly increase the quality of atmospheric correction performed, as well as broaden the range of atmospheric conditions under which the SMAC can be applied. When examining the Normalised Difference Vegetation Index (NDVI), the relative difference between SMAC and in-situ values decreases by 1.5% with the improvements in place. Similarly, the mean relative difference between SMAC and 6S reflectance values decreases by a mean of 13, 14.5 and 8.5% for Channels 1, 2 and 3 respectively. Furthermore, the processing speed of the SMAC is found to remain largely unaffected, with only a small increase in the time taken to process a full SEVIRI scene. Whilst the method described within this paper is only applicable to SEVIRI data, a similar approach can be applied to other data sources than SEVIRI, and should result in a similar accuracy improvement no matter which instrument supplies the original data. [Copyright &y& Elsevier]

Abstract: Traditional methods for aerosol retrieval and atmospheric correction of remote sensing data over water surfaces are based on the assumption of zero water reflectance in the near-infrared. Another type of approach which is becoming very popular in atmospheric correction over water is based on the simultaneous retrieval of atmospheric and water parameters through the inversion of coupled atmospheric and bio-optical water models. Both types of approaches may lead to substantial errors over optically-complex water bodies, such as case II waters, in which a wide range of temporal and spatial variations in the concentration of water constituents is expected. This causes the water reflectance in the near-infrared to be non-negligible, and that the water reflectance response under extreme values of the water constituents cannot be described by the assumed bio-optical models. As an alternative to these methods, the SCAPE-M atmospheric processor is proposed in this paper for the automatic atmospheric correction of ENVISAT/MERIS data over inland waters. A-priori assumptions on the water composition and its spectral response are avoided by SCAPE-M by calculating reflectance of close-to-land water pixels through spatial extension of atmospheric parameters derived over neighboring land pixels. This approach is supported by the results obtained from the validation of SCAPE-M over a number of European inland water validation sites which is presented in this work. MERIS-derived aerosol optical thickness, water reflectance and water pigments are compared to in-situ data acquired concurrently to MERIS images in 20 validation match-ups. SCAPE-M has also been compared to specific processors designed for the retrieval of lake water constituents from MERIS data. The performance of SCAPE-M to reproduce ground-based measurements under a range of water types and the ability of MERIS data to monitor chlorophyll-a and phycocyanin pigments using semiempirical algorithms after SCAPE-M processing are discussed. It has been found that SCAPE-M is able to provide high accurate water reflectance over turbid waters, outperforming models based on site-specific bio-optical models, although problems of SCAPE-M to cope with clear waters in some cases have also been identified. [Copyright &y& Elsevier]

This paper presents a new method developed for the atmospheric correction of the images that will be acquired by the Venμs satellite after its launch expected in early 2010. Every two days, the Venμs mission will provide 10 m resolution images of 50 sites, in 12 narrow spectral bands ranging from 415 nm to 910 nm. The sun-synchronous Venμs orbit will have a 2-day repeat cycle, and the images of a given site will always be acquired from the same place, at the same local hour, with constant observation angles. Thanks to these characteristics, the directional effects will be considerably reduced since only the solar angles will slowly vary with time. The algorithm that will be implemented for the atmospheric correction of Venμs data is being developed using both radiative transfer simulations and the actual data acquired by the Formosat-2 satellite. Because of its one-day sun-synchronous repeat cycle, Formosat-2 acquires images with a sun-viewing geometry close to the one Venμs will offer. With this geometry, reflectance time series are free from directional effects on the short term, a feature which reduces the number of unknowns to retrieve. The atmospheric corrections algorithm exploits this feature and the two following assumptions: [–] Aerosol optical properties vary quickly with time but slowly with location. [–] Surface reflectances vary quickly with location but slowly with time. Consequently, the top of atmosphere reflectance short term variations (10 to 15 days) are mainly due to the variations of aerosol optical properties, and it is thus possible to use these variations to characterise the atmospheric aerosols and to retrieve surface reflectances. This paper first describes the aerosol inversion method we developed and its results when applied to simulations. In the second part, we show the first tests of the method against three data sets acquired by Formosat-2 images with constant observation angles. Aeronet sun photometers measurements were available on all sites. Formosat-2 estimates of optical thickness compare favourably with Aeronet in situ measurements, leading to a noticeable improvement of the smoothness of time series of surface reflectances after atmospheric correction. [Copyright &y& Elsevier]

Radiometric measurements near 19 and 37 GHz have been used for estimation of snow water equivalent (SWE) for many years. Most conventional SWE retrieval algorithms depend on the difference between the brightness temperatures (T b''s) at these frequencies. The effect of atmospheric absorption is generally assumed to be insignificant, and thus often not taken into account in such estimation. In this paper this effect is closely examined with the aid of AMSR-E and radiosonde data sets over two widely separated regions in the continental U.S.A. Results of the analysis show that even under a clear sky the atmospheric absorption could account for as much as ∼25–50% to the estimation of SWE. For example, the AMSR-E estimated SWE of 10 cm would become about 13.6 cm when measured at the ground level under the same atmospheric condition; the estimation based on surface emission alone (i.e., no atmosphere) would be about 15.2 cm. There is some regional dependence of this atmospheric absorption effect, but the effect of seasonal variation is negligibly small. Under cloudy conditions, the impact of liquid cloud absorption is significant and it appears necessary to perform either cloud screening or quantify the cloud effects on SWE estimation from 19 to 37 GHz radiometric measurements at ground level or satellite altitudes. [Copyright &y& Elsevier]

Two physical phenomena by which satellite remotely sensed ocean color data are contaminated by sea ice at high latitudes are described through simulations and observations: (1) the adjacency effect that occurs along sea ice margins and (2) the sub-pixel contamination by a small amount of sea ice within an ocean pixel. The signal at the top of the atmosphere (TOA) was simulated using the 6S radiative transfer code that allows modeling of the adjacency effect for various types of sea ice surrounding an open water area. In situ sea ice reflectance spectra used in the simulations were measured prior to and during the melt period as part of the 2004 Canadian Arctic Shelf Exchange Study (CASES). For sub-pixel contamination, the TOA signal was simulated for various surface reflectances obtained by linear mixture of both sea ice and water-leaving reflectances (ρw). The standard atmospheric correction algorithm was then applied to the simulated TOA spectra to retrieve ρw spectra from which chlorophyll a concentrations (CHL) and inherent optical properties (IOPs) were derived. The adjacency effect was associated with large errors (> 0.002) in the retrieval of ρw as far as 24 km from an ice edge in the blue part of the spectrum (443 nm). Therefore, for moderate to high CHL (> 0.5 mg m− 3), any pixel located within a distance of ∼ 10–20 km from the ice edge were unreliable. It was also found necessary to consider the adjacency effect when the total absorption coefficient (at) was to be retrieved using a semi-analytical algorithm. at(443) was underestimated by more than 35% at a distance of 20 km from an ice edge for CHL > 0.5 mg m− 3. The effect on the retrieval of the particle backscattering coefficient (bbp) was important only for clear waters (CHL ∼ 0.05 mg m− 3). In contrast, sub-pixel contamination by a small amount of sea ice produced systematic underestimation of ρw in the blue because of incorrect interpretation of enhanced reflectance in the near infrared that is attributed to higher concentrations of atmospheric aerosols. In general, sub-pixel contamination was found to result in overestimations of CHL and at, and underestimations of bbp. A simple method was proposed to flag pixels contaminated by adjacency effect. [Copyright &y& Elsevier]

Imaging spectrometers operating in the solar spectrum measure the upwelling reflected solar radiation, and are an important tool in the bio/geochemical characterization of the Earth system. Surface reflectance is usually the starting point for the retrieval of biophysical parameters from remote measurements. Reliable radiometric and spectral calibrations and accurate atmospheric correction are mandatory in the interpretation of the surface reflectance. A complete surface reflectance retrieval scheme specifically designed for ultra-fine spectral resolution (bandwidth from 10 to 2 nm) and spatial resolution (pixel size less than 10 m) imaging spectrometers is presented in this work. The assessment of the spectral calibration is coupled to the removal of the atmospheric distortion so that maps of surface reflectance are derived, as well as columnar water vapor (CWV) maps, estimations of aerosol optical thickness (AOT) and updated sensor gain coefficients and spectral calibration. Radiative transfer calculations are performed by an optimized version of the MODTRAN4 code, which is run before processing each image. The method is tested against Compact Airborne Spectrographic Imager (CASI) 1500 images acquired during the ESA SENtinel-2 and FLuorescence EXperiment (SEN2FLEX) campaigns held in the Barrax (La Mancha, Spain) study site during June and July 2005. A peak-to-peak spectral shift variation of up to 2.3 nm is detected in CASI. Concerning atmospheric products and surface reflectance retrievals, an extensive validation is performed using ground-based measurements. A good correlation between ground measurements and CASI-derived AOT and CWV is found, with a Pearson correlation coefficient r 2 up to 0.71 and 0.74, respectively. The subsequent surface reflectance retrievals also hold a good correspondence with ground-based measurements. [Copyright &y& Elsevier]

Atmospheric interaction distorts the surface signal received by a space-borne instrument. Images derived from visible channels appear often too bright and with reduced contrast. This hampers the use of RGB imagery otherwise useful in ocean color applications and in forecasting or operational disaster monitoring, for example forest fires. In order to correct for the dominant source of atmospheric noise, a simple, fast and flexible algorithm has been developed. The algorithm is implemented in Python and freely available in PySpectral which is part of the PyTroll family of open source packages, allowing easy access to powerful real-time image-processing tools. Pre-calculated look-up tables of top of atmosphere reflectance are derived by off-line calculations with RTM DISORT as part of the LibRadtran package. The approach is independent of platform and sensor bands, and allows it to be applied to any band in the visible spectral range. Due to the use of standard atmospheric profiles and standard aerosol loads, it is possible just to reduce the background disturbance. Thus signals from excess aerosols become more discernible. Examples of uncorrected and corrected satellite images demonstrate that this flexible real-time algorithm is a useful tool for atmospheric correction. [ABSTRACT FROM AUTHOR]