Mapping of total suspended matter concentration (TSM) can be achieved from space-based optical sensors and has growing applications related to sediment transport. A TSM algorithm is developed here ...for turbid waters, suitable for any ocean colour sensor including MERIS, MODIS and SeaWiFS. Theory shows that use of a single band provides a robust and TSM-sensitive algorithm provided the band is chosen appropriately. Hyperspectral calibration is made using seaborne TSM and reflectance spectra collected in the southern North Sea. Two versions of the algorithm are considered: one which gives directly TSM from reflectance, the other uses the reflectance model of
Park and Ruddick (2005) to take account of bidirectional effects.
Applying a non-linear regression analysis to the calibration data set gave relative errors in TSM estimation less than 30% in the spectral range 670–750
nm. Validation of this algorithm for MODIS and MERIS retrieved reflectances with concurrent
in situ measurements gave the lowest relative errors in TSM estimates, less than 40%, for MODIS bands 667
nm and 678
nm and for MERIS bands 665
nm and 681
nm. Consistency of the approach in a multisensor context (SeaWiFS, MERIS, and MODIS) is demonstrated both for single point time series and for individual images.
Ocean color remote sensing has been shown to be a useful tool to map turbidity (T) and suspended particulate matter (SPM) concentration in turbid coastal waters. Different algorithms to retrieve T ...and/or SPM from water reflectance already exist, however there are important questions as to whether these algorithms need to be calibrated specifically for different regions. In the present work the potential generality of a semi-empirical single band turbidity retrieval algorithm using the near infrared (NIR) band at 859nm in highly turbid waters is assessed. For completeness the use of 645nm in medium to low turbidity waters is also proposed. Radiative transfer simulations and in situ measurements from various European and South American coastal and shallow estuarine environments characterized by high concentrations of suspended sediments are analyzed. Reflectance and turbidity measurements were performed in the southern North Sea (SNS) and French Guyana (FG) coastal waters, and Scheldt (SC), Gironde (GIR) and Río de la Plata (RdP) estuaries. Simulations showed that uncertainty for turbidity estimation associated with different particle types and bidirectional effects is typically less than 6%. When applied to field data from the five different sites, the semi-analytical algorithm performed well: turbidity estimates were within 12% and 22% of in situ values. A good performance was also found when the entire database was analyzed (n=106) with a mean relative error of 13.7% and bias of 4.8%. The good performance of the algorithm for all these regions, despite differences in sediment characteristics, and the results of the radiative transfer simulations suggest the global applicability of the algorithm to map turbidity up to 1000FNU. Consequently regional algorithms to retrieve SPM concentration from reflectance can be designed by combining this global algorithm to retrieve T from water reflectance with a regional relationship to convert T to SPM. This has the very practical advantage that the measurements needed to calibrate the latter T/SPM conversion for any new region are much easier and cheaper than in situ reflectance measurements.
•Generality of a single-band turbidity algorithm from water reflectance is analyzed•A sensitivity analysis and algorithm's uncertainties are calculated•A good performance of the algorithm in different regions using field data is found•Algorithm's global applicability to map turbidity between 1–1000FNU is suggested•Advantage of global T algorithm to estimate SPM is discussed
Spatio-temporal variability of turbidity in the Río de la Plata (RdP) estuary (Argentina) at seasonal and inter-annual timescales is analyzed from 15 years (2000–2014) of MODIS data and explained in ...terms of river discharges and the El Niño Southern Oscillation (ENSO). Satellite estimates were first validated using in situ turbidity measurements and then the time series of monthly averages were analyzed to assess the seasonal and inter-annual variability of turbidity. A strong seasonal variability was found in the upper and middle estuary with high turbidity from March to May and low turbidity from June to January. It was found that this variability is highly correlated to the seasonal cycle of the water discharge of the Bermejo river with a one-month delay between its peak and turbidity in the upper RdP estuary. On inter-annual time scales the influence of ENSO shows low turbidity amplitudes in the upper and middle estuary during moderate El Niño years, while the opposite pattern is observed in some La Niña years. A dilution effect during El Niño years is observed given that the main tributaries, which provide ∼92% of the liquid discharge, show water discharge increases due to excess in rain, while the Bermejo river, which provides ∼70% of the solid discharge and only 2% of the liquid discharge to the RdP, does not show this inter-decadal periodicity. In turn, increased turbidities are observed when negative RdP water discharge anomalies occurred, but this is not always related to La Niña events, since these events are not the only predictor for drought in this region.
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•MODIS Aqua & Terra turbidity agree well with field measurements in Río de la Plata.•Seasonal variability in turbidity is highly correlated to Bermejo river discharge.•Interannual variability in turbidity is related to ENSO cycle: low T during El Niño.•Long time series of remote sensing data are useful to monitor sediment dynamics.•Developed methodology can be used in other estuaries and river plumes.
The launch of several new satellites such as Sentinel-2, Sentinel-3, HyspIRI, EnMAP and PRISMA in the very near future, opens new perspectives for the inland and coastal water community. The ...monitoring of the water quality closer to the coast, within estuaries or small lakes with satellite data will become feasible. However for these inland and nearshore coastal waters, adjacency effects may hamper the correct retrieval of water quality parameters from remotely sensed imagery. Here, we present a sensor-generic adjacency pre-processing method, SIMilarity Environment Correction (SIMEC). The correction algorithm estimates the contribution of the background radiance based on the correspondence with the Near-INfrared (NIR) similarity spectrum. The performance of SIMEC was tested on MERIS FR images both above highly reflecting waters with high SPM loads, as well as dark lake waters with high CDOM absorption. The results show that SIMEC has a positive or neutral effect on the normalized remote sensing reflectance above optically-complex waters, retrieved with the MERIS MEGS or C2R processor.
•A sensor-generic adjacency pre-processing method ‘SIMEC’ is presented.•SIMEC is applied to MERIS FR data covering different land–water environments.•In situ match-up points in this study were only used for validation purposes.•Comparisons with in situ indicate a significant decrease in RMSE.
In ocean colour remote sensing, the use of Near Infra Red (NIR) spectral bands for the retrieval of Total Suspended Matter (TSM) concentration in turbid and highly turbid waters has proven to be ...successful. In extremely turbid waters (TSM>100mgL−1) however, these bands are less sensitive to increases in TSM. Here it is proposed to use Short Wave Infra Red (SWIR) spectral bands between 1000 and 1300nm for these extreme cases. This SWIR spectral region is subdivided into two regions, SWIR-I (1000nm to 1200nm) and SWIR-II (1200nm to 1300nm) which correspond to local minima in the pure water absorption spectrum. For both spectral regions the water reflectance signal was measured in situ with an ASD spectrometer in three different extremely turbid estuarine sites: Scheldt (Belgium), Gironde (France), and Río de la Plata (Argentina), along with the TSM concentration. A measurable water reflectance was observed for all sites in SWIR-I, while in the SWIR-II region the signal was not significant compared to the Signal-to-Noise Ratio (SNR) of current Ocean Colour (OC) sensors. For the spectral band at 1020nm (present in Ocean and Land Colour Instrument — OLCI, onboard Sentinel-3) and at 1071nm, an empirical single band TSM algorithm is defined which is valid for both the Gironde and Scheldt estuarine sites. This means that a single algorithm can be applied for both sites without expensive recalibration. The relationship between TSM and SWIR reflectance at 1020 and 1071nm is linear and did not show any saturation for the concentrations measured here (up to 1400mgL−1), while saturation was observed for the NIR wavelengths, as expected. Hence, for extremely turbid waters it is advised to switch from NIR to SWIR-I wavelengths to estimate TSM concentration. This was demonstrated for an airborne hyperspectral dataset (Airborne Prism Experiment, APEX) from the Gironde estuary having several spectral bands in the SWIR-I. The empirical single band SWIR TSM algorithm was applied to the atmospherically corrected scene providing a TSM concentration map of the Gironde from mouth to more upstream with concentrations expected in this region ranging from a few to several hundreds mgL−1. These results, i.e. the existence of a single relationship for the Scheldt and Gironde, not showing any decrease of sensitivity, highlights the importance of having SWIR bands in future ocean colour sensors for studying extremely turbid rivers, coastal areas and estuaries in the world. A further implication of these results is that there is a TSM limit for application of atmospheric correction algorithms which assume zero SWIR marine reflectance. That limit is defined here as function of wavelength and sensor noise level.
•A SWIR based TSM algorithm is defined for extremely turbid waters.•The relationship between TSM and SWIR reflectance did not show any saturation.•The relationship is at least valid for both Scheldt and Gironde study sites.•TSM limits are provided for atmospheric corrections assuming a SWIR black pixel.
This study follows up on the successful feasibility study of Neukermans et al. (2009) for mapping suspended matter in turbid waters from the SEVIRI sensor on board the METEOSAT geostationary weather ...satellite platform. Previous methodology is extended to the mapping of turbidity, T, and vertical attenuation of photosynthetically active radiation (PAR), KPAR. The spatial resolution of the SEVIRI products is improved from 3km×6.5km to 1km×2km using the broad high resolution visual band. The previous atmospheric correction is further improved and the uncertainties on marine reflectance due to digitization are considered. Based on a two year archive of SEVIRI imagery, available every 15min, the diurnal variability of T and KPAR is investigated during cloud free periods and validated using half-hourly T and KPAR data obtained from a system of moored buoys (SmartBuoys) in the southern North Sea. Based on numerous match-ups, 80% of SEVIRI derived T and KPAR are within 53% and 39% of SmartBuoy T and KPAR, respectively. Results further show that on cloud free days, the SEVIRI T and KPAR signals are in phase with the SmartBuoy data, with an average difference in the timing of the maximum T and KPAR of 11min and 23min, respectively. It is concluded that diurnal variability of T and KPAR can now be mapped by remote sensing offering new opportunities for improving ecosystem models and monitoring of turbidity. Limitations of the current SEVIRI sensor and perspectives for design of future geostationary sensors and synergy with polar orbiting satellites are discussed.
► First study on geostationary remote sensing of tidal variability of water turbidity. ► Assessment of atmospheric correction, digitization, and algorithm uncertainties. ► Validation of geostationary SEVIRI products using half-hourly moored buoy data. ► Good correspondence between SEVIRI remote sensing products and buoy data.
Reliable satellite estimates of chlorophyll-a concentration (Chl-a) are needed in coastal waters for applications such as eutrophication monitoring. However, because of the optical complexity of ...coastal waters, retrieving accurate Chl-a is still challenging. Many algorithms exist and give quite different performance for different optical conditions but there is no clear definition of the limits of applicability of each algorithm and no clear basis for deciding which algorithm to apply to any given image pixel (reflectance spectrum). Poor quality satellite Chl-a data can easily reach end-users. To remedy this and provide a clear decision on when a specific Chl-a algorithm can be used, we propose simple quality control tests, based on MERIS water leaving reflectance (ρw) bands, to determine on a pixel-by-pixel basis if any of three popular and complementary algorithms can be used. The algorithms being tested are: 1. the OC4 blue-green band ratio algorithm which was designed for open ocean waters; 2. the OC5 algorithm which is based on look-up tables and corrects OC4 overestimation in moderately turbid waters and 3. a near infrared-red (NIR-red) band ratio algorithm designed for eutrophic waters.
Using a dataset of 348 in situ Chl-a / MERIS matchups, the conditions for reliable performance of each of the selected algorithms are determined. The approach proposed here looks for the best compromise between the minimization of the relative difference between In situ measurements and satellite estimations and the number of pixels processed. Conditions for a reliable application of OC4 and OC5 depend on ρw412/ρw443 and ρw560, used as proxies of coloured dissolved organic matter and suspended particulate matter (SPM), as compared to ρw560/ρw490, used as a proxy for Chl-a. Conditions for reliable application of the NIR-red band ratio algorithm depend on Chl-a and SPM. These conditions are translated into pixel-based quality control (QC) tests with appropriately chosen thresholds. Results show that by removing data which do not pass QC, the performance of the three selected algorithms is significantly improved. After combining these algorithms, 70% of the dataset could be processed with a median absolute percent difference of 30.5%. The QC tests and algorithm merging methodology were then tested on four MERIS images of European waters. The OC5 algorithm was found to be suitable for most pixels, except in very turbid and eutrophic waters along the coasts where the NIR-red band ratio algorithm helps to fill the gap. Finally, a test was performed on an OLCI-S3A image. Although some validations of water reflectance are still needed for the OLCI sensors, results show similar behavior to the MERIS applications which suggests that when applied to OLCI data the present methodology will help to accurately estimate Chl-a in coastal waters for the next decade.
•Helps ocean colour users to identify reliable chlorophyll-a data in coastal water.•Reflectance based QC tests developed for three chlorophyll-a algorithms.•QC tests designed for MERIS and OLCI imagery.•Methodology based on relative error between in situ and satellite data.•Proposition of a merged Chl-a product with less than 35% error
Spectral relationships, reflecting the spectral dependence of water-leaving reflectance, ρw(λ), can be easily implemented in current AC algorithms with the aim to improve ρw(λ) retrievals where the ...algorithms fail. The present study evaluates the potential of spectral relationships to improve the MUMM Ruddick et al., 2006, Limnol. Oceanogr. 51, 1167-1179 and standard NASA Bailey et al., 2010, Opt. Express 18, 7521-7527 near infra-red (NIR) modeling schemes included in the AC algorithm to account for non-zero ρw(λNIR), based on in situ coastal ρw(λ) and simulated Rayleigh corrected reflectance data. Two modified NIR-modeling schemes are investigated: (1) the standard NASA NIR-modeling scheme is forced with bounding relationships in the red spectral domain and with a NIR polynomial relationship and, (2) the constant NIR ρw(λ) ratio used in the MUMM NIR-modeling scheme is replaced by a NIR polynomial spectral relationship. Results suggest that the standard NASA NIR-modeling scheme performs better for all turbidity ranges and in particular in the blue spectral domain (percentage bias decreased by approximately 50%) when it is forced with the red and NIR spectral relationships. However, with these new constraints, more reflectance spectra are flagged due to non-physical Chlorophyll-a concentration estimations. The new polynomial-based MUMM NIR-modeling scheme yielded lower ρw(λ) retrieval errors and particularly in extremely turbid waters. However, including the polynomial NIR relationship significantly increased the sensitivity of the algorithm to errors on the selected aerosol model from nearby clear water pixels.
The present study provides an extensive overview of red and near infra-red (NIR) spectral relationships found in the literature and used to constrain red or NIR-modeling schemes in current ...atmospheric correction (AC) algorithms with the aim to improve water-leaving reflectance retrievals, ρw(λ), in turbid waters. However, most of these spectral relationships have been developed with restricted datasets and, subsequently, may not be globally valid, explaining the need of an accurate validation exercise. Spectral relationships are validated here with turbid in situ data for ρw(λ). Functions estimating ρw(λ) in the red were only valid for moderately turbid waters (ρw(λNIR) < 3.10(-3)). In contrast, bounding equations used to limit ρw(667) retrievals according to the water signal at 555 nm, appeared to be valid for all turbidity ranges presented in the in situ dataset. In the NIR region of the spectrum, the constant NIR reflectance ratio suggested by Ruddick et al. (2006) (Limnol. Oceanogr. 51, 1167-1179), was valid for moderately to very turbid waters (ρw(λNIR) < 10(-2)) while the polynomial function, initially developed by Wang et al. (2012) (Opt. Express 20, 741-753) with remote sensing reflectances over the Western Pacific, was also valid for extremely turbid waters (ρw(λNIR) > 10(-2)). The results of this study suggest to use the red bounding equations and the polynomial NIR function to constrain red or NIR-modeling schemes in AC processes with the aim to improve ρw(λ) retrievals where current AC algorithms fail.
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