Spectral optimization for constituent retrieval in Case 2 waters I: Implementation and performance

We describe in detail the implementation of the spectral optimization algorithm (SOA) for Case 2 waters for processing of ocean color data. This algorithm uses aerosol models and a bio-optical reflectance model to provide the top-of atmosphere (TOA) reflectance. The parameters of both models are then determined by fitting the modeled TOA reflectance to that observed from space, using non-linear optimization. The algorithm will be incorporated into the SeaDAS software package as an optional processing switch of the Multi-Sensor Level-1 to Level-2 code. To provide potential users with an understanding of the accuracy and limitations of the algorithm, we generated a synthetic data set and tested the performance of the SOA with both correct and incorrect bio-optical model parameters. Application of the SOA to actual SeaWiFS data in the Lower Chesapeake Bay (for which surface measurements were available) showed that 20% errors in the bio-optical model parameters still enabled retrieval of chlorophyll a and the total absorption coefficient of dissolved plus particulate detrital material at 443 nm with an error of less than 30% and 20%, respectively. In a companion paper we present a validation study of the application of the algorithm in the Chesapeake Bay.     

Evaluation of shortwave infrared atmospheric correction for ocean color remote sensing of Chesapeake Bay

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.     

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.     

Atmospheric correction of ENVISAT/MERIS data over inland waters: Validation for European lakes

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.     

The atmospheric correction of water colour and the quantitative retrieval of suspended particulate matter in Case II waters: Application to MERIS

The remote sensing of turbid waters (Case II) using the Medium Resolution Imaging Spectrometer (MERIS) requires new approaches for atmospheric correction of the data. Unlike the open ocean (Case I waters) there are no wavelengths where the water-leaving radiance is zero. A coupled hydrological atmospheric model is described here. The model solves the water-leaving radiance and atmospheric path radiance in the near-infrared (NIR) over Case II turbid waters. The theoretical basis of this model is described, together with its place in the proposed MERIS processing architecture. Flagging procedures are presented that allow seamless correction of both Case I waters, using conventional models, and Case II waters using the proposed model. Preliminary validation of the model over turbid waters in the Humber estuary, UK is presented using Compact Airborne Spectrographic Imager (CASI) imagery to simulate the MERIS satellite sensor. The results presented show that the atmospheric correction scheme has superior performance over the standard single scattering approach, which assumes that water-leaving radiance in the NIR is zero. Despite problems of validating data in such highly dynamic tidal waters, the results show that retrievals of sediments within 50% are possible from algorithms derived from the theoretical models.     

Neural network estimation of chlorophyll a from MERIS full resolution data for the coastal waters of Galician rias (NW Spain)

In typical Case 2 waters, accurate remote sensing retrieval of chlorophyll a (chla) is still a challenging task. In this study, focusing on the Galician rias (ΝW Spain), algorithms based on neural network (NN) techniques were developed for the retrieval of chla concentration in optically complex waters, using Medium Resolution Imaging Spectrometer (MERIS) data. There is considerable interest in the accurate estimation of chla for the Galician rias, because of the economic and social importance of the extensive culture of mussels, and the high frequency of harmful algal events. Fifteen MERIS full resolution (FR) cloud-free images paired with in situ chla data (for 2002-2004 and 2006-2008) were used for the development and validation of the NN. The scope of NN was established from the clusters obtained using fuzzy c-mean (FCM) clustering techniques applied to the satellite-derived data. Three different NNs were developed: one including the whole data set, and two others using only points belonging to one of the clusters. The input data for these latter two NNs was chosen depending on the quality level, defined on the basis of quality flags given to each data set. The fitting results were fairly good and proved the capability of the tool to predict chla concentrations in the study area. The best prediction was given for the NN trained with high-quality data using the most abundant cluster data set. The performance parameters in the validation set of this NN were R² = 0.86, mean percentage error (MPE) = − 0.14, root mean square error (RMSE) = 0.75 mg m^− 3, and relative RMSE = 66%. The NN developed in this study detected accurately the peaks of chla, in both training and validation sets. The performance of the Case-2-Regional (C2R) algorithm, routinely used for MERIS data, was also tested and compared with our best performing NN and the sea-truthing data. Results showed that this NN outperformed the C2R, giving much higher R² and lower RMSE values.This study showed that the combination of in situ data and NN technology improved the retrieval of chla in Case 2 waters, and could be used to obtain more accurate chla maps. A local-based algorithm for the chla retrieval from an ocean colour sensor with the characteristics of MERIS would be a great support in the quantitative monitoring and study of harmful algal events in the coastal waters of the Rias Baixas. The limitations and possible improvements of the developed chla algorithms are also discussed.     

Remote chlorophyll-a retrieval in turbid, productive estuaries: Chesapeake Bay case study

Accurate remote assessment of phytoplankton chlorophyll a (chla) concentration is particularly challenging in turbid, productive waters. Recently a conceptual model containing reflectance in three spectral bands in the red and near infra-red range of the spectrum was suggested for retrieving chla concentrations in turbid productive waters; it was calibrated and validated in lakes and reservoirs in Nebraska and Iowa. The objective of this paper is to evaluate the performance of this three band model as well as its special case, the two-band model to estimate chla concentration in Chesapeake Bay, as representative of estuarine Case II waters, and to assess the accuracy of chla retrieval. To evaluate the model performance, dual spectroradiometers were used to measure subsurface spectral radiance reflectance in the visible and near infra-red range of the spectrum. Water samples were collected concurrently and contained widely variable chla (9 to 77.4 mg/m³) and total suspended solids (7-65 mg/L dry wt). Colored dissolved organic matter (CDOM) absorption at 440 nm was 0.20 to 2.50 m^− 1; Secchi disk transparency ranged from 0.28 to 1.5 m. The two- and three-band models were spectrally tuned to select the spectral bands for most accurate chla estimation. Strong linear relationships were established between analytically measured chla and both the three-band model [R^− 1(675)-R^− 1(695)] × R(730) and the two-band model R(720)/R(670), where R(λ) is reflectance at wavelength λ. The three-band model accounted for 81% of variation in chla and allowed estimation of chla with a root mean square error (RMSE) of less than 7.9 mg/m³, whereas the two-band model accounted for 79% of chla variability and RMSE of chla estimation was below 8.4 mg/m³. The three-band model with MERIS spectral bands allows accurate chla estimation with RMSE below 9.1 mg/m³. Two-band model with SeaWiFS bands and MODIS 667 nm and 748 nm bands can estimate chla with RMSE below 11 mg/m³. The findings underlined the rationale behind the conceptual model and demonstrated the robustness of this algorithm for chla retrieval in turbid, productive estuarine waters.     

Retrieval of total suspended matter concentration in the Yellow and East China Seas from MODIS imagery

A retrieval algorithm, of total suspended matter (TSM) concentration in the Yellow Sea (YS) and East China Sea (ECS) was developed using observations made in the 2003 Spring and Autumn cruises over the YS and the ECS. Analysis of the in-situ backscattering coefficients of the suspended particles (b_bp) indicates that the accuracy becomes worse when the concentration of TSM (C_TSM) is higher than 20 mg/l. The accuracy of the b_bp is improved by using a bio-optical model in which b_bp is optimized with a non-linear least-square Levenberg-Marquardt method. The remote sensing reflectance (R_rs) is obtained by means of the optimization. The optimized R_rs for waters with C_TSM higher than 20 mg/l, together with the measured R_rs for waters with C_TSM lower than 20 mg/l, are used to establish the relationships between R_rs(748), R_rs(869) and R_rs(645), which are used in the iterative method for atmospheric correction. Two atmospheric correction algorithms are switched according to the water turbidity. The shortwave infrared wavelengths (SWIR) method is used for waters with high-turbidity, and the iterative method is used otherwise. Results of the atmospheric correction were then applied to the Tassan model modified in this paper to compute the C_TSM. Comparison between the retrieval results from MODIS imagery and the in-situ measurements indicates that the algorithms described in this paper can provide a reliable estimation of the C_TSM distributions in the YS and ECS.     

Operational algorithm for the retrieval of water quality in the Great Lakes

A new operational non-satellite-specific algorithm for the simultaneous retrieval from satellite data of phytoplankton chlorophyll content (chl), suspended minerals (sm), and dissolved organics (doc) in both clear and turbid waters is presented. It contains an array of neural networks providing input for the Levenberg-Marquardt multivariate optimization procedure as the final retrieval tool. With a given accuracy threshold, the developed algorithm is sufficiently robust for data with noise up to 15% for certain hydro-optical conditions. To avoid inadequate retrieval results, the algorithm identifies and eventually discards the pixels with inadequate atmospheric correction and/or water optical properties incompatible with the applied hydro-optical model. The validity of the developed algorithm was tested for Lake Michigan. Two dedicated field campaigns in the vicinity of the Kalamazoo River mouth have been conducted concurrently or quasi-concurrently with SeaWiFS and MODIS overpasses. In addition, some archival shipborne measurements of mostly chl and occasionally sm and doc were employed to validate the facility of the algorithm. Notwithstanding the aforementioned shipborne data limitations, the conducted comparison of the ground truth and retrieved data on the water quality parameters in Lake Michigan is strongly indicative of the algorithm's operational efficiency.     

Monitoring turbidity in Tampa Bay using MODIS/Aqua 250-m imagery

We developed an approach to map turbidity in estuaries using a time series (May 2003 to April 2006) of 250-m resolution images from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Aqua satellite, using Tampa Bay as a case study. Cross-calibration of the MODIS 250-m data (originally designed for land use) with the well-calibrated MODIS 1-km ocean data showed that the pre-launch radiometric calibration of the 250-m bands was adequate. A simple single scattering atmospheric correction provided reliable retrievals of remote sensing reflectance at 645 nm (0.002 < R_rs(645) < 0.015 sr^− 1, median bias = − 7%, slope = 0.95, intercept = 0.00, r² = 0.97, n = 15). A more rigorous approach, using a multiple scattering atmospheric correction of the cross-calibrated at-sensor radiances, retrieved similar R_rs(645). R_rs(645) estimates, after stringent data quality control, showed a close correlation with in situ turbidity (turbidity = 1203.9 × R_rs(645)^1.087, 0.9 < turbidity < 8.0 NTU, r² = 0.73, n = 43). MODIS turbidity imagery derived using the developed approach showed that turbidity in Hillsborough Bay (HB) was consistently higher than that in other sub-regions except in August and September, when higher concentrations of colored dissolved organic matter seem to have caused underestimates of turbidity. In comparison, turbidity in Middle Tampa Bay (MTB) was generally lowest among the Bay throughout the year. Both Old Tampa Bay (OTB) and Low Tampa Bay (LTB) showed marked seasonal variations with higher turbidity in LTB during the dry season and in OTB during the wet season, respectively. This seasonality is linked to wind-driven bottom resuspension events in lower portion of the Bay and river inputs of sediments in the upper portion of the Bay. The Bay also experiences significant interannual variation in turbidity, which was attributed primarily to changes in wind forcing. Compared with the once-per-month, non-synoptic in situ surveys, synoptic and frequent sampling facilitated by satellite remote sensing provides improved assessments of turbidity patterns and thus a valuable tool for operational monitoring of water quality of estuarine and coastal waters such as in Tampa Bay.     

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.     

Remote sensing of cyanobacteria-dominant algal blooms and water quality parameters in Zeekoevlei, a small hypertrophic lake, using MERIS

Eutrophication and cyanobacterial algal blooms present an increasing threat to the health of freshwater ecosystems and to humans who use these resources for drinking and recreation. Remote sensing is being used increasingly as a tool for monitoring these phenomena in inland and near-coastal waters. This study uses the Medium Resolution Imaging Spectrometer (MERIS) to view Zeekoevlei, a small hypertrophic freshwater lake situated on the Cape Flats in Cape Town, South Africa, dominated by Microcystis cyanobacteria. The lake's small size, highly turbid water, and covariant water constituents present a challenging case for both algorithm development and atmospheric correction. The objectives of the study are to assess the optical properties of the lake, to evaluate various atmospheric correction procedures, and to compare the performance of empirical and semi-analytical algorithms in hypertrophic water. In situ water quality parameter and radiometric measurements were made simultaneous to MERIS overpasses. Upwelling radiance measurements at depth 0.66 m were corrected for instrument self-shading and processed to water-leaving reflectance using downwelling irradiance measurements and estimates of the vertical attenuation coefficient for upward radiance, K_u, generated from a simple bio-optical model estimating the total absorption, a(λ), and backscattering coefficients, b_b(λ). The normalised water-leaving reflectance was used for assessing the accuracy of image-based Dark Object Subtraction and 6S Radiative Transfer Code atmospheric correction procedures applied to MERIS. Empirical algorithms for estimating chlorophyll a (Chl a), Total Suspended Solids (TSS), Secchi Disk depth (z_SD) and absorption by CDOM (a_CDOM) were derived from simultaneously collected in situ and MERIS measurements. The empirical algorithms gave high correlation coefficient values, although they have a limited ability to separate between signals from covariant water constituents. The MERIS Neural Network algorithms utilised in the standard Level 2 Case 2 waters product and Eutrophic Lakes processor were also used to derive water constituent concentrations. However, these failed to produce reasonable comparisons with in situ measurements owing to the failure of atmospheric correction and divergence between the optical properties and ranges used to train the algorithms and those of Zeekoevlei. Maps produced using the empirical algorithms effectively show the spatial and temporal variability of the water quality parameters during April 2008. On the basis of the results it is argued that MERIS is the current optimal sensor for frequent change detection applications in inland waters. This study also demonstrates the considerable potential value for simple TOA algorithms for hypertrophic systems. It is recommended that regional algorithm development be prioritized in southern Africa and that remote sensing be integrated into future operational water quality monitoring systems.     

MERIS satellite chlorophyll mapping of oligotrophic and eutrophic waters in the Laurentian Great Lakes

Chlorophyll-a (Chla) concentrations and ‘water-leaving’ reflectance were assessed along transects in Keweenaw Bay (Lake Superior) and in Green Bay (Lake Michigan) (two of the Laurentian Great Lakes, USA), featuring oligotrophic (0.4–0.8 mg Chla m^? 3) and eutrophic to hyper-eutrophic waters (11–131 mg Chla m^? 3), respectively. A red-to-NIR band Chla retrieval algorithm proved to be applicable to Green Bay, but gave mostly negative values for Keweenaw Bay. An alternative algorithm could be based on Chla fluorescence, which in Keweenaw Bay was indicated by enhanced reflectance near 680 nm. Bands 7, 8 and 9 of the Medium Resolution Imaging Spectrometer (MERIS) have been specifically designed to detect phytoplankton fluorescence in coastal waters. A quite strong linear relationship was found between Chla concentration and fluorescence line height (FLH) computed with these MERIS bands. The same relationship held for observations on oligotrophic waters elsewhere, but not for Green Bay, where the FLH diminished to become negative as Chla increased. The remote sensing application of the algorithms could be tested because a MERIS scene was acquired coinciding with the day of the field observations in Keweenaw Bay and one day after those in Green Bay. For Green Bay the pixel values from the red-to-NIR band algorithm compared well to the steep Chla gradient in situ. This result is very positive from the perspective of satellite use in monitoring eutrophic inland and coastal waters in many parts of the world. Implementation of the FLH relationship in the scene of Keweenaw Bay produced highly variable pixel values. The FLH in oligotrophic inland waters like Lake Superior appears to be very close to or below the MERIS detection limit. An empirical algorithm incorporating three MERIS bands in the blue-to-green spectral region might be used as an alternative, but its applicability to other regions and seasons remains to be verified. Moreover, none of the algorithms will be suitable for mesotrophic water bodies. The results indicate that Chla mapping in oligotrophic and mesotrophic areas of the Great Lakes remains problematic for the current generation of satellite sensors.     

Coupling and comparing a spatially- and temporally-detailed eutrophication model with an ecosystem network model: An initial application to Chesapeake Bay

Coastal waters are modeled for a variety of purposes including eutrophication remediation and fisheries management. Combining these two approaches provides insights which are not available from either approach independently. Coupling is confounded, however, by differences in model formulations and “currencies.” We present here an initial coupling of a spatially- and temporally-detailed eutrophication model, CE-QUAL-ICM, with a network fisheries model, Ecopath. We list commonalities between the models and present algorithms and software for the exchange of information. The models are applied to the central portion of Chesapeake Bay for a contemporary summer period. After comparison of the representations of Chesapeake Bay by the two models, an illustrative example one-way, off-line, coupling is presented. In an initial examination of a 20% increase in predation on phytoplankton by a small, highly-exploited fish (Atlantic menhaden, Brevoortia tyrannus), computed reduction in phytoplankton biomass is accompanied by increased production due to enhanced nutrient recycling. Minimal impact on the structure of the food web or on biomass of higher-trophic level organisms is computed. The algorithms and software can be adapted to alternate eutrophication models and Ecopath applications and provide the first, necessary, steps for subsequent coupling with the time-variable Ecosim model.     

Evaluation of atmospheric correction and high-resolution processing on SeaDAS-derived chlorophyll-a: an example from mid-latitude mesotrophic waters

Over the last 15 years, great effort has gone into the development of chlorophyll-a (chl-a) retrieval algorithms for case 2 waters, where variations in the water leaving radiance signal are not well correlated with concentrations of chl-a. In this study, we investigate the effectiveness of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived chl-a retrieval algorithms in the less productive coastal waters around Tasmania, Australia. Algorithms were evaluated using matches between satellite imagery and in-situ water samples (number of samples, n = 16–65) derived from a 604 sample data set collected over a 9-year period. Three aerosol correction models and three chl-a retrieval algorithms were evaluated using both standard and high-resolution processing procedures using the National Aeronatics and Space Adminstration’s SeaDAS software package. chl-a retrievals were evaluated in Bass Strait, where in-situ chl-a was less than 1 mg m^?3 and retrievals were less affected by coloured dissolved organic matter. chlor_a, the default SeaDAS chl-a product, with the Management unit of the North Sea Mathematical models aerosol correction algorithm performed best (root mean square error (RMSE) = 0.09 mg m^?3; mean absolute percentage error (MAPE) = 34%; coefficient of determination, R² = 0.75). The fluorescence line height algorithm using Rayleigh corrected top of atmosphere reflectances (RMSE = 0.11 mg m^?3, MAPE = 41%, R² = 0.61) may provide an alternative in waters where full atmospheric correction is problematic and the two-band red/near-infrared algorithm failed to provide a meaningful estimate of chl-a. High-resolution processing of MODIS imagery improved spatial resolution but reduced chl-a retrieval accuracy, reducing the agreement between measured and predicted levels by between 12% and 25% depending on the retrieval algorithm. The SeaDAS default chlor_a product proved superior to the alternatives in mid-latitude mesotrophic coastal waters with low chl-a concentrations. In addition, there appears little benefit in using MODIS high-resolution processing mode for chl-a retrievals.     

Regional and seasonal variability of chlorophyll-a in Chesapeake Bay as observed by SeaWiFS and MODIS-Aqua

Concentrations of the phytoplankton pigment chlorophyll-a (C_a) provide indicators of nutrient over-enrichment that has negatively affected Chesapeake Bay, U.S.A. C_a time-series from the National Aeronautics and Space Administration (NASA) Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer aboard the Aqua spacecraft (MODIS-Aqua) provide observations on temporal and spatial scales that far exceed current field and aircraft sampling strategies. These sensors provide consistent, frequent, and high density data to potentially complement ongoing Bay monitoring activities. We used the in situ Water Quality Monitoring Data set of the Chesapeake Bay Program to evaluate decade-long time-series of SeaWiFS and MODIS-Aqua C_a retrievals in the Bay. The accuracy of the retrievals generally degraded with increasing latitude as the optical complexity increases northward. C_a derived using empirical (“band ratio”) algorithms overestimated in situ measurements by 10-50 and 40-100% for SeaWiFS and MODIS-Aqua, respectively, but with limited variability. C_a derived using spectral-matching algorithms showed less bias for both sensors, but with significant variability and sensitivity to radiometric errors. Regionally-tuned empirical algorithms performed best throughout the Bay, offering a combination of reasonable accuracy and high spatial coverage. The radiometric spectral resolution used as input to the algorithms strongly influenced the quality of C_a retrievals from both sensors. These results establish a baseline quantification of algorithm and sensor performance in a variable and stressed ecosystem against which novel approaches might be compared.     

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.     

Fields of non-linear regression models for atmospheric correction of satellite ocean-color imagery

Remote sensing of ocean color from space, a problem that consists of retrieving spectral marine reflectance from spectral top-of-atmosphere reflectance, is considered as a collection of similar inverse problems continuously indexed by the angular variables influencing the observation process. A general solution is proposed in the form of a field of non-linear regression models over the set T of permitted values for the angular variables, i.e., as a map from T to some function space. Each value of the field is a regression model that performs a direct mapping from the top-of-atmosphere reflectance to the marine reflectance. Since the spectral components of the field take values in the same variable vector space, the retrievals in individual spectral bands are not independent, i.e., the solution is not just a juxtaposition of independent models for each spectral band. A scheme based on ridge functions is developed to approximate this solution to an arbitrary accuracy, and is applied to the retrieval of marine reflectance in Case 1 waters, for which optical properties are only governed by biogenic content. The statistical models are evaluated on synthetic data as well as actual data originating from the SeaWiFS instrument, taking into account noise in the data. Theoretical performance is good in terms of accuracy, robustness, and generalization capabilities, suggesting that the function field methodology might improve atmospheric correction in the presence of absorbing aerosols and provide more accurate estimates of marine reflectance in productive waters. When applied to SeaWiFS imagery acquired off California, the function field methodology gives generally higher estimates of marine reflectance than the standard SeaDAS algorithm, but the values are more realistic.     

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.     

Improving the SMAC atmospheric correction code by analysis of Meteosat Second Generation NDVI and surface reflectance data

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.