Validation of a SeaWiFS dust-correction methodology in the Mediterranean Sea: Identification of an algorithm-switching criterion

A spectral matching algorithm (SMA) that allows atmospheric correction in the presence of dust aerosols is applied to SeaWiFS imagery in the northwest Mediterranean Sea. The goal is to find criteria that could be used to identify SMA target pixels and to gain insights into the method's accuracy relative to the standard SeaWiFS processing scheme (STD). This work also represents the first validation of SMA using in situ data. The validation dataset includes water-leaving radiances collected from both a fixed buoy site and from a ship during the Advanced Optical Properties Experiment (AOPEX) cruise in August 2004. Supplementary information was provided by the ship LIDAR and coastal AERONET stations in Villefranche (France) and Blida (Algeria) that recorded aerosol conditions near the buoy and proximal to the dust sources, respectively. Backward aerosol transport trajectories were also available for the AERONET sites, allowing identification of potential dust sources, especially for aerosol layers observed by the LIDAR. Over the study period, four aerosol events affected the buoy vicinity, but SMA retrievals proved superior to standard processing results only when dust was dominant, rather than when dust was simply present. The conditions appropriate for an SMA application could be defined using AERONET parameters. They are a combination of high aerosol optical depth τ_a and low Ångström exponent α (or τ_a / α > 0.2). Similar results are obtained using the equivalent SeaWiFS parameters produced by the STD method although the threshold value is different. Since it is preferable to apply the criterion on a per-pixel basis prior to atmospheric correction to select SMA or STD processing, an analogous test using aerosol model-independent quantities derived from SeaWiFS data is proposed. Thus, SMA and STD processing can be applied to a single image, where appropriate.     

Development and validation of a technique for merging satellite derived aerosol optical depth from SeaWiFS and MODIS

Merging time series of satellite derived aerosol products from independent missions can support aerosol science by combining in a consistent way temporally overlapping data sets and by increasing data coverage. A merging technique applied to satellite aerosol optical depth τ_a is presented and tested with SeaWiFS and MODIS-Aqua data. The technique relies on least squares fitting of the available τ_a spectra onto a linear or second-order polynomial relation between log-transformed τ_a and wavelengths. First, the sensor specific products are compared with field observations collected by a sun-photometer installed on the Acqua Alta Oceanographic Tower in the northern Adriatic Sea. Mean absolute percentage differences are approximately 21% at 412 and 443 nm, and increase with wavelength, with large overestimates in the red and near-infrared bands. The mean absolute differences are typically 0.04. When inter-compared, the 2 satellite products agree well, with mean absolute percentage differences lower than 20% at all wavelengths and little bias. The results of the comparison of the merger outputs with the field data are well in line with the validation results of the sensor specific products, and are comparable for the various merging procedures. The benefits of merging in terms of data coverage are briefly illustrated for the Mediterranean basin.     

Evaluation of MODIS SWIR and NIR-SWIR atmospheric correction algorithms using SeaBASS data

Using the NASA maintained ocean optical and biological in situ data that were collected during 2002-2005, we have evaluated the performance of atmospheric correction algorithms for the ocean color products from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua. Specifically, algorithms using the MODIS shortwave infrared (SWIR) bands and an approach using the near-infrared (NIR) and SWIR combined method are evaluated, compared to the match-up results from the NASA standard algorithm (using the NIR bands). The in situ data for the match-up analyses were collected mostly from non-turbid ocean waters. It is critical to assess and understand the algorithm performance for deriving MODIS ocean color products, providing science and user communities with the important data quality information. Results show that, although the SWIR method for data processing has generally reduced the bias errors, the noise errors are increased due mainly to significantly lower sensor signal-noise ratio (SNR) values for the MODIS SWIR bands, as well as the increased uncertainties using the SWIR method for the atmospheric correction. This has further demonstrated that future ocean color satellite sensors will require significantly improved sensor SNR performance for the SWIR bands. The NIR-SWIR combined method, for which the non-turbid and turbid ocean waters are processed using the NIR and SWIR method, respectively, has been shown to produce improved ocean color products.     

Comparing MODIS and AERONET aerosol optical depth over China

The newest daily and monthly Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depths (AOD or τ) dataset over land, C005, retrieved using the second-generation operational algorithm, were evaluated using a ground-based Aerosol Robotic Network (AERONET) dataset from 13 sites over China. The dataset covers the period 2003–2006. Daily MODIS C005 AODs over China were found to have a positive bias with a relationship of τ_MODIS?=?0.135?+?1.022τ_AERONET, for which the offset is larger than reported global validation results. However, the relationship τ_MODIS?=?0.021?+?0.929τ_AERONET showed that monthly MODIS C005 AODs were an overestimation for small AOD and underestimation for high AOD. Both daily and monthly MODIS AOD retrievals showed poor performance in extreme aerosol conditions, e.g. under dust events or heavy urban/industrial haze. Nevertheless, both daily and monthly MODIS C005 AOD datasets can be used for investigation of aerosol spatial distribution and temporal variation over China.     

An evaluation of MODIS and SeaWiFS bio-optical algorithms in the Baltic Sea

An extensive bio-optical data set from field measurements was used to evaluate the performance of standard Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) ocean color (in-water) algorithms in the Baltic Sea, which represents an example of optically complex Case 2 waters with high concentration of colored dissolved organic matter (CDOM). The data set includes coincident measurements of radiometric quantities, chlorophyll a concentration (Chl a), and absorption coefficient of CDOM, which were taken on 25 cruises between 1993 and 2001. The data cover a wide range of variability with Chl a in surface waters from about 0.3 to 100 mg m⁻³. All the MODIS pigment algorithms examined as well as the SeaWiFS OC4v4 algorithm showed a systematic and large overestimation in chlorophyll retrievals. The mean systematic and random errors based on our entire data set exceeded 150% or even 200% in some cases, making these standard algorithms inadequate for pigment determinations in the Baltic. Although new parameterization of the standard pigment algorithms based on our field measurements in the Baltic resulted in a significant reduction of errors, the overall performance of such regionally tuned algorithms remained unsatisfactory. For example, the mean normalized bias (MNB) for the regionally tuned MODIS chlor_a_2 algorithm was reduced to 26% (from over 200% for the standard algorithm), but the root mean square (RMS) error was still large (>100%). The MODIS K_490 algorithm for estimating the diffuse attenuation coefficient of downwelling irradiance showed the best performance among all the algorithms examined. With the new coefficients based on our field data, the regional version of this algorithm showed an acceptable level of errors, MNB=4% and RMS=30%. In addition to the apparent problems of the standard in-water bio-optical algorithms, we found that the atmospheric correction currently in use for MODIS and SeaWiFS imagery usually fails to retrieve upwelling radiances emerging from the Baltic Sea. The match-up comparisons of the coincident in situ and satellite determinations of normalized water-leaving radiances showed generally poor agreement, especially in the blue spectral region. It appears that new approaches for ocean color algorithms are required in the Baltic Sea.     

Validation of satellite ocean color primary products at optically complex coastal sites: Northern Adriatic Sea, Northern Baltic Proper and Gulf of Finland

The study presents and discusses the application of in situ data from the ocean color component of the Aerosol Robotic Network (AERONET-OC) to assess primary remote sensing products from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the AQUA platform and from the Sea-viewing Wide-Field-of-view Sensor (SeaWiFS) on the OrbView-2 spacecraft. Three AERONET-OC European coastal sites exhibiting different atmospheric and marine optical properties were considered for the study: the Acqua Alta Oceanographic Tower (AAOT) in the northern Adriatic Sea representing Case-1 and Case-2 moderately sediment dominated waters; and, the Gustaf Dalen Lighthouse Tower (GDLT) in the northern Baltic Proper and the Helsinki Lighthouse Tower (HLT) in the Gulf of Finland, both characterized by Case-2 waters dominated by colored dissolved organic matter (CDOM). The analysis of MODIS derived normalized water-leaving radiance at 551 nm, L_WN(551), has shown relatively good results for all sites with uncertainties of the order of 10% and biases ranging from − 1 to − 4%. Larger uncertainty and bias have been observed at 443 nm for the AAOT (i.e., 18 and − 7%, respectively). At the same center wavelength, results for GDLT and HLT have exhibited much larger uncertainties (i.e., 56 and 67%, respectively) and biases (i.e., 18 and 25%, respectively), which undermine the possibility of presently using remote sensing L_WN data at the blue center wavelengths for bio-optical investigations in the Baltic Sea. An evaluation of satellite derived aerosol optical thickness, τ_a, has shown uncertainties and biases of the order of tens of percent increasing with wavelength at all sites. Specifically, MODIS derived τ_a at 869 nm has shown an overestimate of 71% at the AAOT, 101% at GDLT and 91% at HLT, respectively. This result highlights the effects of a limited number of aerosol models for the atmospheric correction process, and might also indicate the need of applying a vicarious calibration factor to the remote sensing data at the 869 nm center wavelength to remove the effects of uncertainties in the atmospheric optical model and the space sensor radiometric calibration. Similar results have been obtained from the analysis of SeaWiFS data. Finally, in view of illustrating the possibility of increasing the accuracy of satellite regional radiometric products, AERONET-OC data have been applied to reduce systematic errors in MODIS and Medium Resolution Imaging Spectrometer (MERIS) L_WN data likely due to the atmospheric correction process. Results relying on MODIS match-ups for the Baltic Sites (i.e., GDLT and HLT) and MERIS matchups for the AAOT, have indicated a substantial reduction of both uncertainty and bias in the blue and red center wavelengths.     

Assessment of SeaWiFS,MODIS, and MERIS ocean colour products in the South China Sea

The Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Medium Resolution Imaging Spectrometer (MERIS) remote-sensing radiometric and chlorophyll-a (chl-a) concentration products for the South China Sea (SCS) from October 2003 to May 2010 were assessed using in situ data. A strict spatiotemporal match-up method was used to minimize the temporal variability effects of atmosphere and seawater around the measurement site. A comparison of the remote-sensing reflectance (R_rs(λ)) of the three sensors with in situ values from the open waters of the SCS showed that the mean absolute percentage difference varied from 13% to 55% in the 412–560 nm spectral range. Generally, the MERIS radiometric products exhibited higher typical uncertainties and bias than the SeaWiFS and MODIS products. The R_rs(443) to R_rs(555/551/560) band ratios of the satellite data were in good agreement with in situ observations for these sensors. The SeaWiFS, MODIS, and MERIS chl-a products overestimated in situ values by 74%, 42%, and 120%, respectively. MODIS retrieval accuracy was better than those of the other sensors, with MERIS performing the worst. When the match-up criteria were relaxed, the assessment results degraded systematically. Therefore, strict spatiotemporal match-up is recommended to minimize the possible influences of small-scale variation in geophysical properties around the measurement site. Coastal and open-sea areas in the SCS should be assessed separately because their biooptical properties are different and the results suggest different atmospheric correction problems.     

Cross-site consistent in situ measurements for satellite ocean color applications: The BiOMaP radiometric dataset

The development and assessment of satellite ocean color products require quality assured in situ data representative of the variety of bio-optical regimes encountered in the different seas. The measurement program named Bio-Optical mapping of Marine Properties (BiOMaP) fulfills this requirement by using identical instruments and applying cross-site consistent methods for the characterization of seawater inherent and apparent optical properties in the various European seas. This work introduces the BiOMaP radiometric data and describes their application to the validation of primary ocean color products. Within this framework, the radiometric data are discussed through the spectral shape and amplitude of normalized water-leaving radiances (L_WN). Specifically, the spectral shape is expressed through the Principal Component Analysis of L_WN(λ)/L_WN(555) while the amplitude is represented by L_WN(555). The resulting distribution of BiOMaP data in a three dimensional feature space demonstrates a continuity of cases across the investigated marine regions confirming a wide representativity of bio-optical regimes. The application of BiOMaP data to the validation of remote sensing reflectance from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), indicates improved performance of the SeaWiFS Data Analysis System (SeaDAS, version 6.1) atmospheric correction. In particular, the comparison of satellite and in situ matchups in the blue spectral region shows biases of a few percent with respect to the much larger reported in studies relying on earlier SeaDAS versions. Matchup analyses, restricted to the Eastern Mediterranean, Black and Baltic Seas, indicate marked regional differences likely explained by the diversity of water and aerosol types.     

A new method for improving the retrieved aerosol fine-mode fraction from MODIS over ocean

A new method for improving the retrieved aerosol fine-mode fraction (₅₅₀) based on the current Moderate Resolution Imaging Spectroradiometer (MODIS) ocean algorithm is proposed. In the current MODIS ocean algorithm, the top of the atmosphere (TOA) apparent reflectance needs calculation from lookup tables (LUTs). The weighting parameters used in the calculation show an obvious spectral dependence, which is not taken into account in the current algorithm. The main measure taken in this study is to consider the spectral dependence of the weighting parameters. The MODIS aerosol products and the Aerosol Robotic Network (AERONET) data of Hong Kong Hok Tsui, Midway Island, Martha’s Vineyard Coastal Observatory (MVCO) and COVE, Virginia, where aerosols exhibit different loading and size distribution, are used to test the new method. The results show that the new method improves the retrieved fine-mode fraction, which is underestimated in anthropogenic-dominated aerosol conditions and overestimated in the sea salt-dominated aerosol conditions by the current algorithm. The correlation of the retrieved fine-mode fraction between the new method and AERONET is much higher (correlation coefficient, r?=?0.92) than that between the current MODIS and AERONET (r?=?0.80). The retrieved aerosol optical depth (AOD) is also improved. More AODs retrieved from the new method lie within the expected error bars.     

Consistent merging of satellite ocean color data sets using a bio-optical model

While many (and more on the way) ocean color satellite sensors presently provide routine observations of ocean biological processes, limited concrete effort has taken place to demonstrate how these data can be used together in any systematic way. One obvious way is to merge these data streams together to provide robust merged climate data records with measurable uncertainty bounds. Here, we present and implement a formalism for merging global satellite ocean color data streams to produce uniform data products. Normalized water-leaving radiances (L_wN(λ)) from SeaWiFS and MODIS are used together in a semianalytical ocean color merging model to produce global retrievals of 3 biogeochemically relevant variables (chlorophyll, combined dissolved and detrital absorption coefficient, particulate backscattering coefficient). The model-based merging approach has various benefits over techniques that blend end products, such as chlorophyll concentrations; (1) merging at the level of water-leaving radiance ensures simultaneity and consistency of the retrievals, (2) it works with single or multiple data sources regardless of their specific bands, (3) it exploits band redundancies and band differences, (4) it can account for the uncertainties of the incoming L_wN(λ) data streams and, (5) it provides confidence intervals for the derived products. These features are illustrated through several examples of ocean color data merging using SeaWiFS and MODIS Terra and Aqua L_wN(λ) imagery. Compared to each of the original data source, the products derived from the merging procedure show enhanced global daily coverage and lower uncertainties in the retrieved variables.     

Vicarious calibration of GOCI for the SeaDAS ocean color retrieval

A key on-orbit calibration step for satellite remote sensing of ocean color is the vicarious calibration. This establishes the final gains for each spectral band on the sensor that minimize bias in the retrieved ocean color signal. The vicarious calibration is specific to the instrument and the atmospheric correction algorithm. The vicarious calibration gains for the Geostationary Ocean Color Imager (GOCI) are presented here, which were derived to optimize the performance of NASA’s standard atmospheric correction algorithm as implemented in the l2gen code and distributed through the SeaDAS open-source software package. Following NASA’s protocols, the near-infrared (NIR) bands were calibrated first, and the visible bands were then calibrated relative to this fixed NIR calibration. The gain for the 745-nm NIR band was derived using a fixed aerosol model, which was chosen based on the Angstrom Coefficients derived from MODIS on Aqua (MODISA). For the vicarious gains of the visible bands, two sources for the target water-leaving radiances were tested: matchups from MODISA and climatological data from SeaWiFS. A validation analysis using AERONET-OC data shows an improvement in sensor performance when compared with results using the current vicarious gains and results using no vicarious calibration. Good agreement was found in vicarious gains derived using both concurrent MODISA and climatological SeaWiFS as vicarious calibration data sources. These results support the use of a concurrent sensor for the vicarious calibration when in situ data are not available and demonstrate that using climatology from a well-calibrated sensor like SeaWiFS for the vicarious calibration is a valid alternative when it is not possible to use a concurrent sensor or in situ data. We recommend using the gains derived from concurrent GOCI matchups with MODISA for GOCI processing in SeaDAS/l2gen.     

Satellite observations of optical and biological properties in the Korean dump site of the Yellow Sea

With the standard near-infrared (NIR) atmospheric correction algorithm for ocean color data processing, a high chlorophyll-a concentration patch was consistently observed from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua platform in the middle of the Yellow Sea during the spring (end of March to early May). This prominent patch was not observed in the historical ocean color satellite imageries in late 1970s to early 1980s, and a location corresponding to this patch has been used as a Korean dump site since 1988. At the same time, MODIS chlorophyll-a concentrations derived using the shortwave infrared (SWIR) atmospheric correction algorithm developed for the ocean color satellite data in turbid coastal or high-productive ocean waters were significantly reduced.Comparison between in situ and MODIS chlorophyll-a measurements shows that the chlorophyll-a from the MODIS-Aqua products using the standard-NIR atmospheric correction algorithm is significantly overestimated. The images of the MODIS-derived normalized water-leaving radiance spectra and water diffuse attenuation coefficient data using the NIR-SWIR-based atmospheric correction approach show that absorption and scattering by organic and inorganic matter dumped in the Korean dump site have strongly influenced the satellite-derived chlorophyll-a data. Therefore, the biased high chlorophyll-a patch in the region is in fact an overestimation of chlorophyll-a values due to large errors from the standard-NIR atmospheric correction algorithm. Using the NIR-SWIR algorithm for MODIS-Aqua ocean color data processing, ocean color products from 2002 to 2008 for the Korean dump site region have been generated and used for characterizing the ocean optical and biological properties. Results show that there have been some important changes in the seasonal and interannual variations of phytoplankton biomass and other water optical and biological properties induced by colored dissolved organic matters, as well as suspended sediments.     

Improving GERB scene identification using SEVIRI: Infrared dust detection strategy

The combination of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the Geostationary Earth Radiation Budget (GERB) instruments on Meteosat-8 provides a powerful new tool for detecting aerosols and estimating their radiative effect at high temporal and spatial resolution. However, at present no specific aerosol treatment is performed in the GERB processing chain, severely limiting the use of the data for aerosol studies. A particular problem relates to the misidentification of Saharan dust outbreaks as cloud which can bias the shortwave and longwave fluxes. In this paper an algorithm is developed which employs multiple-linear regression, using information from selected thermal infrared SEVIRI channels, to detect dust aerosol over ocean and provide an estimate of the optical depth at 0.55 μm (τ₀₅₅). To test the performance of the algorithm, it has been applied to a number of dust events observed by SEVIRI during March and June 2004. The results are compared to co-located MODIS observations taken from the Terra and Aqua platforms, and ground based observations from the Cape Verde AERONET site. In terms of detection capability, employing the algorithm results in a notable improvement in the routine GERB scene identification. Locations identified by MODIS as being likely to be dust contaminated were originally classified as cloud in over 99.5% of the cases studied. With the application of the detection algorithm approximately 60-70% of these points are identified as dusty depending on the dust model employed. The algorithm is also capable of detecting dust in regions and at times which would be excluded when using shortwave observations, due for example to the presence of sun-glint, or through the night. We further investigate whether the algorithm is capable of generating useful information concerning the aerosol loading. Comparisons with co-located retrievals from the SEVIRI 0.6 μm solar reflectance band observations show a level of agreement consistent with that expected from the simulations, with rms differences of between 0.5 and 0.8, and a mean bias ranging from − 0.5 to 0.3 dependent on the dust representation employed in the algorithm. Temporally resolved comparisons with observations from the Capo Verde AERONET site through the months of March and June reinforce these findings, but also indicate that the algorithm is capable of discerning the diurnal pattern in aerosol loading. The algorithm has now been incorporated within the routine GERB processing in detection mode, and will be used to provide an experimental aerosol product for assessment by the scientific community.     

Validation of MODIS derived aerosol optical depth over the Yangtze River Delta in China

MODIS derived aerosol optical depths (AODs) at 550 nm are compared with sunphotometer CE318 measurements at 7 sites located at Yangtze River Delta (YRD) in China from July to October, 2007. The evaluation result indicates that MODIS AODs (Collection 5, C005) are in good agreement with those from CE318 in dense vegetation regions, but show more differences in those regions with complex underlying surface (such as at lake water and urban surface sites). Reasons for these differences are discussed after removing cases with significant errors caused by validation scheme. The final validation result shows that MODIS AODs are in good agreement with CE318 with a correlation coefficient of 0.85 and RMS of 0.15. 90% of MODIS cases fall in the range of Δτ = ± 0.05 ± 0.20τ, indicating MODIS aerosol retrieval algorithm, aerosol models and surface reflectance estimate are generally suitably reasonable for aerosol retrieval in YRD. However, MODIS AODs show a systemic errors with fitted line of y = 0.75x + 0.13, indicating underestimation of AOD when aerosol loadings are high. Aerosol models and surface reflectance estimations are dominant sources of MODIS aerosol retrieval errors.     

Mapping aerosol optical thickness and water-leaving signals using an improved NIR/SWIR switching atmospheric correction model: A case study in the Bohai Sea

In this study, the performance of the near-infrared & short wave infrared switching atmospheric correction (NSSAC) model in estimating remote sensing reflectance (R_rs(λ)) and aerosol optical thickness at 869 nm (τ_a(869)) were assessed by field measurements taken in the Bohai Sea. It was found that the NSSAC model had approximately 30% uncertainty for retrievals of R_rs(λ) in the green regions but provided approximately 50% uncertainty for estimations of τ_a(869) and R_rs(λ) at all other moderate resolution imaging spectroradiometer (MODIS) visible wavelengths. Therefore, an optimised method is proposed for optimizing the retrieval results of the NSSAC model; it was validated using the field measurements collected from the Oujiang River estuary. The results show that the performance of the NSSAC model for τ_a(869) and R_rs(λ) at the blue, red, and near-infrared bands was greatly improved by using the optimised NSSAC model. Moreover, the study also finds that the τ_a(869) shows a large variation in the Bohai Sea, decreasing from coastal to offshore regions. The monthly average τ_a(869) has a maximum at February and August. Due to the imperfect atmospheric correction procedure, the NSSAC model-derived R_rs(λ) is always larger than those of the field measurements. Future work is needed to minimise the detected water-leaving signals in the short wave infrared (SWIR) images.     

Simultaneous retrieval of oceanic and atmospheric parameters for ocean color imagery by spectral optimization: a validation

We report application and validation of a spectral optimization algorithm for processing SeaWiFS data in Case 1 waters. The algorithm couples a simplified aerosol model with a sophisticated water-reflectance model to simultaneously retrieve both atmospheric and ocean parameters. Two of the retrieved ocean properties—the absorption coefficient of colored detrital material and the chlorophyll a concentration—are validated by comparison with “surface” truth obtained with airborne and space-borne sensors. We show that employing a more complete water reflectance model significantly improves the decoupling between the oceanic and atmospheric optical signals. Methodologies for applying the algorithm to Case 2 waters and for delineating terrestrial vs. marine chromophoric dissolved organic matter (CDOM) are suggested.     

Impact of inter-mission differences and drifts on chlorophyll-a trend estimates

The chlorophyll-a (chl-a) concentration is an Essential Climate Variable, and the study of its variability at global scale requires a succession of satellite ocean colour missions to cover a period suitable for climate research. In the context of a multi-mission data record, inter-mission differences can introduce artefacts affecting trend evaluations, and the impact of the bias between the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) chl-a products is shown to be significant in a substantial part of the ocean. The assessment of trends can also be directly impacted by a drift in the chl-a time series resulting from sensor functions. These issues are addressed by a sensitivity analysis that compares slopes of linear regression obtained for varying levels of inter-mission bias and drift with respect to a 15-year reference series built with SeaWiFS and MODIS data. The relationship, constructed for a representative set of ocean provinces, between bias and the level of significance associated with the comparison of slopes shows that a bias on the order of ±5–6% generally induces a slope that is significantly different from the reference case, while a threshold on bias values not exceeding 2% largely alleviates this effect. Moreover, the study suggests that a drift larger than 2% per decade on the chl-a series can result in misleading conclusions from a trend analysis. All results have a clear regional dependence that needs to be taken into account in bias-correction and merging efforts. Low chl-a regions, such as the oligotrophic subtropical gyres, appear particularly sensitive to perturbations and require still higher levels of consistency and stability.     

AERONET-based surface reflectance validation network (ASRVN) data evaluation: Case study for railroad valley calibration site

The AERONET-based Surface Reflectance Validation Network (ASRVN) is an operational processing system developed for validation of satellite derived surface reflectance products at regional and global scales. The ASRVN receives 50 × 50 km² subsets of MODIS data centered at AERONET sites along with AERONET aerosol and water vapor data, and performs an atmospheric correction. The ASRVN produces surface bidirectional reflectance factor (BRF), albedo, parameters of the Ross-Thick Li-Sparse (RTLS) BRF model, as well as Hemispherical-Directional Reflectance Factor (HDRF), which is required for comparison with the ground-based measurements. This paper presents a comparison of ASRVN HDRF with the ground-based HDRF measurements collected during 2001-2008 over a bright calibration Railroad Valley, Nevada site as part of the MODIS land validation program. The ground measurements were conducted by the Remote Sensing Group (RSG) at the University of Arizona using an ASD spectrometer. The study reveals a good agreement between ASRVN and RSG HDRF for both MODIS Terra and Aqua with rmse ~ 0.01-0.025 in the 500 m MODIS land bands B1-B7. Obtained rmse is below uncertainties due to the spatial and seasonal variability of the bright calibration 1 km² area. While two MODIS instruments have a similar rmse in the visible bands, MODIS Aqua has a better agreement (lower rmse) with the ground data than MODIS Terra at wavelengths 0.87-2.1 μm. An independent overall good agreement of two MODIS instruments with the ground data indicates that the relative calibration of MODIS Terra and Aqua at medium-to-bright reflectance levels for the stated time period is significantly better than uncertainties of the ASRVN and ground data.     

Comparison of atmospheric aerosol climatologies over southwestern Spain derived from AERONET and MODIS

The southwestern area of Spain, by its geographical and climatological conditions, is a key location for the characterization of atmospheric aerosol properties. The present study is aimed at evaluating the reliability of satellite-based aerosol climatologies, as inferred from level 2 standard aerosol products such as the Terra-MODIS (Moderate Resolution Imaging Radiometer) MOD04 aerosol product, with an application over this region during the period 2000-2008.This evaluation is carried out by means of comparison with ground-based data from the AERONET station of El Arenosillo (Spain, 37.1N, 6.7W), which has been providing continuous data since 2000. The focus of this paper is the climatology of two aerosol optical parameters: the aerosol optical depth (AOD) and the Ångström exponent.AERONET ground-based measurements give an annual mean value of 0.16 ± 0.12 and a median of 0.12 for the AOD, and a mean value of 1.20 ± 0.47 for the Ångström exponent. The seasonal pattern is characterized by two maxima, the most important maximum occurs in summer months, and the other one in late-winter/early-spring. Lowest values appear in fall and winter, however, a local minimum is observed in July which is only detected with the long-term data series.The mean climatological AOD based on AERONET exhibits complex seasonal patterns (i.e. with multiple local extrema), which are not always captured by MODIS-based climatology. MODIS only reproduces low values of the AOD in winter and high values in summer, as well as the local minimum of July which is sharper when using over-land retrievals. The time series of the AOD retrieved from MODIS both over land and ocean are in relatively good agreement with the ground-based measurements, with a monthly overestimation of about 30% on average, and higher differences in spring. Seasonal patterns from MODIS are better reproduced over land than over ocean. The agreement between daily AERONET and MODIS, as assessed by linear regression, gives correlation coefficients above 80% and an intercept bias below 0.03.     

Retrieval of high-resolution sea surface temperature data for Sendai Bay, Japan, using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)

We studied sea surface temperature (SST) retrieval algorithms for Sendai Bay, using output from the thermal-infrared channels of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on board Terra. While the highest resolutions of other satellite SST products are about 1 km, the ASTER thermal-infrared channels provide 90-m spatial resolution. To develop the ASTER algorithm, we employed statistical methods in which SSTs retrieved from the thermal-infrared measurements were tuned against the Moderate Resolution Imaging Spectroradiometer (MODIS) SST product with a 1-km spatial resolution. Terra also carries a MODIS sensor, which observed the same area as the ASTER sensor at the same time. The MODIS SST was validated around Sendai Bay, revealing a bias of −0.15 °C and root mean-square difference (RMSD) of 0.67 °C against in situ SSTs. Taking into account the spatial-resolution difference between ASTER and MODIS, match-up was generated only if the variability of ASTER brightness temperatures (T₁₃) was small in a pixel of MODIS SST (MP). The T₁₃ within one MP was about 121 pixels. The standard deviation (σ₁₃) of T₁₃ was calculated for each cloud-free MP, and the threshold of σ₁₃ for choosing match-up MPs was decided by analyzing the σ₁₃ histogram of one ASTER image. The 15 synchronous pairs of ASTER/MODIS images are separated into two groups of 8 pairs called set (A) and 7 pairs called set (B). Using the common procedure, the match-ups are generated for set (A) and set (B). The former is used for developing the ASTER Multi-Channel SST (MCSST) algorithm, and the latter for validation of the developed ASTER SST. Analysis of the whole 15 pairs indicated that ASTER SST does not depend on the satellite zenith angle. We concluded that, using Akaike's information criterion with set (A) match-ups, the multiple regression formula with all five thermal-infrared channels was adequate for the ASTER SST retrieval. Validation of ASTER SST using match-up set (B) indicated a bias of 0.101 °C and RMSD of 0.455 °C.