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.     

An approach for estimating absorption and backscattering coefficients from MERIS in the Bohai Sea

Distribution of absorption and backscattering coefficients (a(560) and b_b(550)) is important for characterizing the marine optical environment. Satellite remote sensing is a useful tool for investigating the absorption and backscattering coefficients in coastal waters. A simple semi-analytical algorithm (SAA_BS) was developed for estimating a(560) and b_b(550) in the Bohai Sea from Medium Resolution Imaging Spectrometer (MERIS) images. Using field measurements, the SAA_BS model attained root-mean-square (RMS) values of 13.25% and 12.75% for a(560) and b_b(550), respectively. The SAA_BS model was also used to retrieve a(560) and b_b(550) from the MERIS image. The match-up analysis results indicate that the RMS values of a(560) and b_b(550) retrievals are 18.75% and 17%, respectively. These findings suggested that if the atmospheric correction scheme is available, the SAA_BS model may be used for the quantitative monitoring of the absorption and backscattering coefficients in the Bohai Sea from the MERIS images.     

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.     

Satellite-based water quality monitoring in Lake Vänern,Sweden

Lake Vänern, Sweden, is one of Europe’s largest lakes and has a historical, cultural, ecological as well as economic importance. Lake water quality monitoring is required by national and international legislations and directives, but present programmes are insufficient to meet the requirements. To complement in situ based monitoring, the possibility to obtain reliable information about spatial and temporal water quality trends in Lake Vänern from the ENVISAT mission’s MERIS instrument was evaluated. The complete archive (2002–2012) of MERIS (Medium Resolution Imaging Spectrometer) full resolution data was processed using the water processor developed by Free University Berlin (FUB) to derive aerosol optical thickness (AOT), remote-sensing reflectance (R_rs) and water quality parameters: chlorophyll-a (chl-a) concentration, coloured dissolved organic matter absorption at 443 nm (CDOM), and total suspended matter (TSM) concentration. The objective was to investigate if, either, FUB reflectance products in combination with potential lake-specific band ratio algorithms for water quality estimation, or directly, FUB water quality products, could complement the existing monitoring programme.

Application of lake-specific band ratio algorithms requires high-quality reflectance products based on correctly estimated AOT. The FUB reflectance and AOT products were evaluated using Aerosol Robotic Network – Ocean Color (AERONET-OC) match-up data measured at station Pålgrunden in Lake Vänern. The mean absolute percentage differences (MAPDs) of the final reflectance retrievals at 413, 443, 490, 555, and 665 nm were 510%, 48%, 33%, 34%, and 33%, respectively, corresponding to a large positive bias in 413 nm, positive bias in 443–555 nm, and a negative bias in 665 nm. AOT was strongly overestimated in all bands.

The FUB water quality products were evaluated using match-up in situ data of chl-a, filtered absorbance (Abs_F(420)) and turbidity as Abs_F(420) is related to CDOM and turbidity is strongly related to TSM. The in situ data was collected within the Swedish national and regional monitoring programmes. In order to widen the range of water constituents and add more data to the analysis, data from four large Swedish lakes (Vänern, Vättern, Mälaren, and Hjälmaren) was included in the analysis. High correlation (r ≥ 0.85) between in situ data and MERIS FUB derived water quality estimates were obtained, but the absolute levels were over- (chl-a) or under- (CDOM) estimated. TSM was retrieved without bias.

Calibration algorithms were established for chl-a and CDOM based on the match-up data from all four lakes. After calibration of the MERIS FUB data, realistic time series could be derived that were well in line with in situ measurements. The MAPDs of the final retrievals of chl-a, Abs_F(420) and Turbidity in Lake Vänern were 37%, 15%, and 35%, respectively, corresponding to mean absolute differences (MADs) of 0.9 µg l^?1, 0.17 m^?1, and 0.32 mg l^?1 in absolute values.

The partly inaccurate reflectance estimations in combination with both positive and negative bias imply that successful application of band ratio algorithms is unlikely. The high correlation between MERIS FUB water quality products and in situ data, on the other hand, shows a potential to complement present water quality monitoring programmes and improve the understanding and representability of the temporally and spatially sparse in situ observations. The monitoring potential shown in this study is applicable to the Sentinel-3 mission’s OLCI (Ocean Land Colour Instrument), which was launched by the European Space Agency (ESA) in February 2016 as a part of the EC Copernicus programme.     

Absorption properties of shoal-dominated waters in the Atchafalaya Shelf,Louisiana, USA

Spectral absorption coefficients of coloured dissolved organic matter (a _CDOM(λ)) and particulate matter (a _p(λ)) (phytoplankton (a _PHY(λ)) plus non-algal particles (a _NAP(λ)), measured on the shoal-dominated region off the Atchafalaya River (AR) Shelf, Louisiana, USA, are analysed, and their effect on chlorophyll-a retrievals from ocean-colour sensors examined. Compared to a _CDOM(λ) and a _NAP(λ), a _PHY(λ) is relatively constant, with a _CDOM(λ) and a _NAP(λ) varying by approximately 1.2 and 1.8 times as much as a _PHY(λ) at 443 nm, respectively. The specific a _PHY(λ) (a*_PHY(λ)) ranges from 0.006 to 0.0612 m^?2(mg chla)^?1 at 443 nm, which indicates a pigment-packaging effect or a variation in pigment composition. The a _NAP(λ) accounts for approximately 3–93% of a _p(λ) at 443 nm, with a higher contribution to a _p(λ) during an October 2007 cruise (62–93%) as compared to an August 2007 cruise (31–89%). Our results indicate that a _CDOM(λ) and a _NAP(λ) collectively dominate light absorption, even at higher wavelengths where their effect is expected to be minimal. In situ and satellite data match-up of chlorophyll-a yield root-mean square errors of 2.17 and 2.62 for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the Medium Resolution Imaging Spectrometer (MERIS), respectively. The non-covarying a _CDOM(λ) and a _NAP(λ), along with variable a*_PHY(λ), greatly influenced the remote retrieval of biogeochemical variables using satellite ocean-colour algorithms in this region.     

A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation

Accurate assessment of phytoplankton chlorophyll-a (chla) concentrations in turbid waters by means of remote sensing is challenging due to the optical complexity of case 2 waters. We have applied a recently developed model of the form [R_rs^? 1(λ₁) ? R_rs^? 1(λ₂)] × R_rs(λ₃) where R_rs(λ_i) is the remote-sensing reflectance at the wavelength λ_i, for the estimation of chla concentrations in turbid waters. The objectives of this paper are (a) to validate the three-band model as well as its special case, the two-band model R_rs^? 1(λ₁) × R_rs(λ₃), using datasets collected over a considerable range of optical properties, trophic status, and geographical locations in turbid lakes, reservoirs, estuaries, and coastal waters, and (b) to evaluate the extent to which the three-band model could be applied to the Medium Resolution Imaging Spectrometer (MERIS) and two-band model could be applied to the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate chla in turbid waters.The three-band model was calibrated and validated using three MERIS spectral bands (660–670 nm, 703.75–713.75 nm, and 750?757.5 nm), and the 2-band model was tested using two MODIS spectral bands (λ₁ = 662–672, λ₃ = 743–753 nm). We assessed the accuracy of chla prediction in four independent datasets without re-parameterization (adjustment of the coefficients) after initial calibration elsewhere. Although the validation data set contained widely variable chla (1.2 to 236 mg m^? 3), Secchi disk depth (0.18 to 4.1 m), and turbidity (1.3 to 78 NTU), chla predicted by the three-band algorithm was strongly correlated with observed chla (r² > 0.96), with a precision of 32% and average bias across data sets of ? 4.9% to 11%. Chla predicted by the two-band algorithm was also closely correlated with observed chla (r² > 0.92); however, the precision declined to 57%, and average bias across the data sets was 18% to 50.3%. These findings imply that, provided that an atmospheric correction scheme for the red and NIR bands is available, the extensive database of MERIS and MODIS imagery could be used for quantitative monitoring of chla in turbid waters.     

Evaluation of MODIS ocean colour products at a northeast United States coast site near the Martha's Vineyard Coastal Observatory

Moderate Resolution Imaging Spectroradiometer (MODIS) marine and atmospheric products were evaluated using match‐ups of MODIS and in situ measurements collected by an above‐water radiometric system, the SeaWiFS Photometer Revision for Incident Surface Measurements (SeaPRISM), deployed near the Martha's Vineyard Coastal Observatory from 2004 to 2005. The products evaluated include the normalized water‐leaving radiance L _wn in the visible and near‐infrared bands, and the aerosol optical thickness at 870 nm τ_a(870), and the Ångström exponent α(531). With a restricted match‐up criterion, the result shows that the MODIS‐retrieved L _wn at 488, 531 and 551 nm agree very well with SeaPRISM measurements, giving mean per cent differences δ(%) of 3–7%, absolute mean per cent differences |δ|(%) of ～16%, and coefficient of determination R ² of 0.84–0.88. However, the MODIS‐retrieved L _wn at 412 nm are underestimated significantly with δ(%), |δ|(%) and R ² of ?35%, 57% and 0.32, respectively, corresponding to a consistent overestimation and underestimation for the MODIS‐retrieved τ_a(870) and α(531), respectively. Temporal patterns of match‐ups revealing two distinct cases of the discrepancy of MODIS retrievals from in situ SeaPRISM measurements are discussed.     

Regional algorithms for remote-sensing estimates of total suspended matter in the Beaufort Sea

The large and variable riverine inflow to Arctic continental shelves strongly influences their chemical, biological, and optical properties. The Beaufort Sea receives the largest amount of suspended sediments amongst all Arctic shelves, with sediment-laden Mackenzie river waters strongly influencing bio-optical properties on the shelf. Here, we developed two regional algorithms for the estimation of total suspended matter (TSM) concentration using Medium Resolution Imaging Spectrometer (MERIS) spectral bands, based on in situ optical and suspended particulate data collected in the summer during the Canadian Arctic Shelf Exchange Study (CASES) in 2004 and during the Arctic Coastal Ecosystem Study (ACES) in 2010. The band ratio (where R _rs is remote-sensing reflectance) R _rs,560/R _rs,490 was best correlated with low TSM concentrations (less than 3.0 g m^?3), while higher TSM concentrations were well correlated to R _rs,681/R _rs,560. An empirical piecewise algorithm is thus proposed with the switch between the ratios being triggered by R _rs,681/R _rs,560 at a threshold value of 0.6. The second algorithm made use of support vector machines (SVMs) as a nonlinear transfer function between TSM concentrations and remote-sensing reflectance ratios R _rs,681/R _rs,560, R _rs,665/R _rs,560, and R _rs,560/R _rs,490. Results show that both algorithms perform better (31% and 25%, respectively) than other published TSM algorithms including the MERIS Case 2 water processor (C2R) neural network algorithm in the study area.     

New strategy to improve estimation of diffuse attenuation coefficient for highly turbid inland waters

The diffuse attenuation coefficient, K_d(λ), is an important water optical property. Detection of K_d(λ) by means of remote sensing can provide significant assistance in understanding water environment conditions and many biogeochemical processes. Even when existing algorithms exhibit good performance in clear open ocean and turbid coastal waters, accurate quantification of highly turbid inland water bodies can still be a challenge due to their bio-optical complexity. In this study, we examined the performance of two typical pre-existing K_d(490) models in inland water bodies from Lake Taihu, Lake Chaohu, and the Three Gorges Reservoir in China. On the basis of water optical classification, new K_d(490) models were developed for these waters by means of the support vector machine approach. The obtained results showed that the two pre-existing K_d(490) models presented relatively large errors by comparison with the new models, with mean absolute percentage error (MAPE) values above ~30%. More importantly, among the new models, type-specific models generally outperformed the aggregated model. For water classified as Type 1 + Type 2, the type-specific model produced validation errors with MAPE = 16.8% and RMSE = 0.98 m^?1. For water classified as Type 3, the MAPE and RMSE of the type-specific model were found to be 18.8% and 1.85 m^?1, respectively. The findings in this study demonstrate that water classification (prior to algorithm development) is needed for the development of excellent K_d(490) retrieval algorithms, and the type-specific models thus developed are an important supplement to existing K_d(490) retrieval models for highly turbid inland waters.     

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.     

Atmospheric correction of ENVISAT/MERIS data over case II waters: the use of black pixel assumption in oxygen and water vapour absorption bands

The Medium Resolution Imaging Spectrometer (MERIS) sensor, with its good physical design, can provide excellent data for water colour monitoring. However, owing to the shortage of shortwave-infrared (SWIR) bands, the traditional near-infrared (NIR)–SWIR algorithm for atmospheric correction in inland turbid case II waters cannot be extended to the MERIS data directly, which limits its applications. In this study, we developed a modified NIR black pixel method for atmospheric correction of MERIS data in inland turbid case II waters. In the new method, two special NIR bands provided by MERIS data, an oxygen absorption band (O₂ A-band, 761 nm) and a water vapour absorption band (vapour A-band, 900 nm), were introduced to keep the assumption of zero water-leaving reflectance valid according to the fact that both atmospheric transmittance and water-leaving reflectance are very small at these two bands. After addressing the aerosol wavelength dependence for the cases of single- and multiple-scattering conditions, we further validated the new method in two case lakes (Lake Dianchi in China and Lake Kasumigaura in Japan) by comparing the results with in situ measurements and other atmospheric correction algorithms, including Self-Contained Atmospheric Parameters Estimation for MERIS data (SCAPE-M) and the Basic ERS (European Remote Sensing Satellite) & ENVISAT (Environmental Satellite) (A)ATSR ((Advanced) Along-Track Scanning Radiometer) and MERIS (BEAM) processor. We found that the proposed method had acceptable accuracy in the bands within 560–754 nm (MERIS bands 5–10) (average absolute deviation (AAD) = 0.0081, average deviation (AD) = 0.0074), which are commonly used in the estimation models of chlorophyll-a (chl-a) concentrations. In addition, the performance of the new method was superior to that of the BEAM processor and only slightly worse than that of SCAPE-M in these bands. Considering its acceptable accuracy and simplicity both in principle and at implementation compared with the SCAPE-M method, the new method provides an option for atmospheric correction of MERIS data in inland turbid case II waters with applications aiming for chl-a estimation.     

In situ validation of MERIS marine reflectance off the southwest Iberian Peninsula: assessment of vicarious adjustment and corrections for near-land adjacency

Water-leaving reflectance (ρ_w) data from the European Space Agency ocean colour sensor Medium Resolution Imaging Spectrometer (MERIS) was validated with in situ ρ_w between October 2008 and November 2011, off Sagres on the southwest coast of the Iberian Peninsula. The study area is exceptional, since Stations A, B, and C at 2, 10, and 18 km offshore are in optically deep waters at approximately 40, 100, and 160 m, respectively. These stations showed consistently similar bio-optical properties, characteristic of Case 1 waters, enabling the evaluation of adjacency effects independent of the usual co-varying inputs of coastal waters. Using the third reprocessing of MERIS with the standard MEGS 8.1 processor, four different combinations of procedures were tested to improve the calibration between MERIS products and in situ data. These combinations included no vicarious adjustment (NoVIC), vicarious adjustment (VIC), and, for mitigating the effects of land adjacency on MERIS ρ_w, the improved contrast between ocean and land (ICOL) processor (version 2.7.4) and VIC + ICOL. Out of approximately 130 potential matchups for each station, 38–77%, 74–86%, and 88–90% were achieved at Stations A, B, and C, respectively, depending on which of the four combinations were used. Analyses of ρ_w comparing these various procedures, including statistics, scatter plots, histograms, and MERIS full-resolution images, showed that the VIC procedure compared with NoVIC produced minimal changes to the calibration. For example, at the oceanic Station C, the regression slope was closer to unity at all wavelengths with NoVIC compared to VIC, whereas, with the exception of wavelengths 412 and 443 nm, the intercept, mean ratio (MR), absolute percentage difference (APD), and relative percentage difference (RPD) were better with NoVIC. The differences for MR and APD indicate that there was marginal improvement for these two bands with VIC, and an over-adjustment with RPD. ICOL also showed inconsistent results for improving the retrieval of the near-shore conditions, but under some conditions, such as ρ_w at wavelength 560 nm, the improvement was striking. VIC + ICOL showed results intermediate between those of VIC and ICOL implemented separately. In relation to other validation sites, the offshore Station C at Sagres had much in common with the Mediterranean deep water, BOUSSOLE buoy, although the matchup statistics between MERIS ρ_w and in situ ρ_w were much better for Sagres than for BOUSSOLE. Strikingly, the matchup statistics for ρ_w at Sagres were very similar to those for the Acqua Alta Oceanographic Tower (AAOT), where the AAOT showed more scatter at 412 nm, probably because of the atmospheric correction where the aerosol optical thickness is higher at the AAOT. Conversely, Sagres showed much greater scatter at 665 nm in the red as the values were generally close to the limits of detection owing to the clearer waters at Sagres compared to the more turbid waters at the AAOT.     

Assessment of satellite ocean color products at a coastal site

A comprehensive set of bio-optical measurements collected at the Acqua Alta Oceanographic Tower site in the northern Adriatic Sea is used to assess satellite derived optical properties and concentrations of optically significant constituents. These include normalized water leaving radiance spectra L_WN, absorption spectra due to phytoplankton, non-pigmented particles and chromophoric dissolved organic matter, back-scattering spectra, concentrations of chlorophyll a, Chla, and total suspended matter, TSM, and diffuse attenuation coefficients, K_d, obtained with a diverse set of algorithms. A total of 81 and 21 match-ups are found for SeaWiFS and MODIS L_WN, respectively. For both sensor products, the match-ups show mean absolute percentage differences of approximately 30% at 412 nm, 20% at 443 nm, and 14% from 490 to 555 nm. Some dependence of these differences has been found with respect to the aerosol optical thickness and the single scattering albedo associated with the in-water constituents. However, the performance of the atmospheric correction scheme appears relatively robust with respect to angular and environmental conditions. The different Chla products generally show quite large uncertainties whereas a TSM product shows encouraging results. Three algorithms produce K_d (490) with a RMS uncertainty of 0.13 for log-transformed data. The comparison between in situ data and satellite derived absorption values yields varying levels of uncertainties for the three bio-optical algorithms considered here and for the different wavelengths. Preliminary improvements could be reached by reducing biases affecting the total absorption coefficient at various wavelengths. Another general result is that the bio-optical models are able to represent a larger part of the observed variability for phytoplankton absorption than for the absorption by dissolved and non-pigmented particulate matter. The assumed spectral shape of the latter absorption term appears logically as a strong determinant of the partition of the total absorption. The three sets of satellite derived back-scattering spectra compare favorably with in situ optical measurements, with mean RMS differences between 0.12 and 0.18. Importantly, the uncertainties obtained here for satellite match-ups of absorption coefficients are comparable to published estimates of the inherent uncertainties associated with the bio-optical algorithms.     

Comparison of MERIS,MODIS, SeaWiFS-derived particulate organic carbon,and in situ measurements in the South China Sea

Medium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) particulate organic carbon (POC) concentration products for the South China Sea (SCS) were compared with in situ data collected from October 2007 to December 2013. Spectral remote-sensing reflectance (R_rs,λ) was also measured to help understand POC algorithm performance. A strict comparison of the satellite-derived POC and in situ measurements showed that MERIS, MODIS, and SeaWiFS underestimated in situ values by 29.1, 11.7, and 31.5%, respectively. Similar results were obtained with a relaxed matching criterion. Through analysis of the causes of product uncertainty, the results suggested that satellite retrieval of R_rs,λ and the global POC algorithm both have an impact on inversion accuracy. However, the formulation of the POC algorithm seems to be more critical. When a regional algorithm was developed to obtain satellite-derived POC, both the strict and relaxed comparison results showed significant improvement, but for coastal waters, both algorithms had larger errors. Other factors affecting the comparison are also discussed.     

A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: The case of Taihu Lake, China

Accurate estimation of phytoplankton chlorophyll a (Chla) concentration from remotely sensed data is particularly challenging in turbid, productive waters. The objectives of this study are to validate the applicability of a semi-analytical three-band algorithm in estimating Chla concentration in the highly turbid, widely variable waters of Taihu Lake, China, and to improve the algorithm using a proposed four-band algorithm. The improved algorithm is expressed as [Rrs(λ₁)^− 1 − Rrs(λ₂)^− 1][Rrs(λ₄)^− 1 − Rrs(λ₃)^− 1]^− 1. The two semi-analytical algorithms are calibrated and evaluated against two independent datasets collected from 2007 and 2005 in Taihu Lake. Strong linear relationships were established between measured Chla concentration and that derived from the three-band algorithm of [Rrs^− 1(660) − Rrs^− 1(692)]Rrs(740) and the four-band algorithm of [Rrs^− 1(662) − Rrs^− 1(693)][Rrs^− 1(740) − Rrs^− 1(705)]^− 1. The first algorithm accounts for 87% and 80% variation in Chla concentration in the 2007 and 2005 datasets, respectively. The second algorithm accounts for 97% of variability in Chla concentration for the 2007 dataset and 87% of variation in the 2005 dataset. The three-band algorithm has a mean relative error (MRE) of 43.9% and 34.7% for the 2007 and 2005 datasets. The corresponding figures for the four-band algorithm are 26.7% and 28.4%. This study demonstrates the potential of the four-band model in estimating Chla even in highly turbid case 2 waters.     

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.     

Using MERIS full resolution data to monitor coastal waters — A case study from Himmerfjärden, a fjord-like bay in the northwestern Baltic Sea

In this paper we investigate if MERIS full resolution (FR) data (300 m) is sufficient to monitor changes in optical constituents in Himmerfjärden, a fjord-like, north–south facing bay of about 30 km length and 4 km width. The MERIS FR products were derived using a coastal processor (FUB Case-2 Plug-In). We also compared the performance between FUB and standard processor (MEGS 7.4), using reduced resolution (RR) data (1 km resolution) from the open Baltic Sea, and compared the products to sea-truthing data. The optical variables measured for sea-truthing were chlorophyll, suspended particulate matter (SPM), as well as coloured dissolved organic matter (CDOM, also termed yellow substances), and the spectral diffuse attenuation coefficient, K_d(490). The comparison of the RR data to the sea-truthing data showed that, in the open Baltic Sea, the MERIS standard processor overestimated chlorophyll by about 59%, and SPM by about 28%, and underestimated yellow substance by about 81%, whereas the FUB processor underestimated SPM by about 60%, CDOM by about 78%, and chlorophyll a by about 56%. The FUB processor showed a relatively high precision for all optical components (standard deviation: 6–18%), whereas the precision for the MEGS 7.4 was rather low (standard deviation: 43–73%), except for CDOM (standard deviation: 13%). The analysis of the FR data showed that all FR level 2 water products derived from MERIS followed a polynomial decline in concentration when moving off-shore. The distribution of chlorophyll and SPM was best described by a 2nd order polynomial, and the distribution of CDOM by a 3rd order polynomial, verifying the diffusional model described in Kratzer and Tett [Kratzer, S. and Tett, P. (in press). Using bio-optics to investigate the extent of coastal waters — a Swedish case study. Hydrobiologia.]. A new K_d(490) and Secchi depth algorithm based on MERIS channel 3 (490 nm) and channel 6 (620 nm) each was derived from radiometric sea-truthing data (TACCS, Satlantic). Applying the K_d(490) algorithm to the MERIS FR data over Himmerfjärden, and comparing to sea-truthing data the results showed a strong correlation (r = 0.94). When comparing the FR data to the sea-truthing data CDOM and K_d(490) showed a low accuracy, but a high precision with a rather constant off-set. In summary, one may state that the precision of MERIS data improves by applying the FUB Case-2 processor and the accuracy improves with improved spatial resolution for chlorophyll and SPM. Furthermore, the FUB processor can be used off-the-shelf for open Baltic Sea monitoring, provided one corrects for the respective off-set from sea-truthing data which is most likely caused by an inaccuracy in the atmospheric correction. Additionally, the FR data can be used to derive CDOM, K_d(490) and Secchi depth in Himmmerfjärden if one corrects for the respective off-set. We will need to perform more comparisons between sea-truthing and MERIS FR data before the new K_d(490) algorithm can be made operational, including also scenes from other times of year. In order to provide a level 2 product that can be used reliably by the Baltic Sea user community, our recommendation to ESA is to include the spectral attenuation coefficient as a MERIS standard product.     

Air-sea interacting effects to the sea surface temperature observation by NOAA/AVHRR

Abstract

Abstract. By combining the brightness temperatures by NOAA-9/AVHRR and the sea surface temperature by Mutsu Bay automatic marine monitoring buoy system, the total number of 390 match-ups was set up. The temporal and spatial coincidence in each match-up is within 30 minutes and one pixel resolution. Following to the split-window method, a regression function for the sea surface temperature estimation was calculated and its standard deviation of estimation errors was 0-59°C. The residues were carefully examined with respect to the sensor calibration data and the meteorological data at match-up collections. Then it was found that large errors appeared when differences between the air temperature and the buoy temperature were large. Those were estimated to be caused by the air-sea interacting effects. By removing the match-ups with larger errors, a selected data set with 334 match-ups was prepared and the SST estimation function was recalculated. The standard deviation of errors reduced to 0-34 °C.     

Water property monitoring and assessment for China's inland Lake Taihu from MODIS-Aqua measurements

We provide results of quantitative measurements and characterization for inland freshwater Lake Taihu from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellite Aqua. China's Lake Taihu, which is located in the Yangtze River delta in one of the world's most urbanized and heavily populated areas, contains consistently highly turbid waters in addition to frequent large seasonal algae blooms in various lake regions. Thus, satellite data processing requires use of the shortwave infrared (SWIR) atmospheric correction algorithm. Specifically for Lake Taihu, an iterative SWIR-based atmospheric correction algorithm has been developed and proven to provide reasonably accurate water-leaving radiance spectra data. Using MODIS-Aqua measurements, the blue-green algae bloom in Lake Taihu in 2007 has been studied in detail, demonstrating the importance and usefulness of satellite water color remote sensing for effectively monitoring and managing a bloom event.Seasonal and interannual variability, as well as spatial distributions, of lake water properties were studied and assessed using the MODIS-Aqua measurements from 2002 to 2008. Results show that overall waters in Lake Taihu are consistently highly turbid all year round, with the winter and summer as the most and least turbid seasons in the lake, respectively. Extremely turbid waters in the winter are primarily attributed to strong winter winds that lead to significant amounts of total suspended sediment (TSS) in the water column. In addition, MODIS-Aqua-measured water-leaving radiance at the blue band is consistently low in various bay regions in Lake Taihu, indicating high algae concentration in these regions. Climatological water property maps, including normalized water-leaving radiance spectra nL_w(λ), chlorophyll-a concentration, and water diffuse attenuation coefficient at the wavelength of 490 nm (K_d(490)), are derived from all MODIS-Aqua data from 2002 to 2008 for Lake Taihu, showing overall spatial distribution features for the lake water property.