Suspended Sediment Concentration Retrieval Research Articles

Spatiotemporal variability of suspended sediment concentration in the coastal waters of Yellow River Delta: Driving mechanism and geomorphic implications

Satellite images have revealed a significant decline in the suspended sediment concentration (SSC) near surface in the coastal waters of Yellow River Delta (YRD) in recent years. However, there is limited information on the spatial disparity in SSC distributions and its dynamic mechanism. In this study, we utilize a well-calibrated SSC retrieval algorithm and a depth-averaged numerical modeling approach to investigate the delta and sub-delta scale variation trends and driving mechanisms of SSC in the coastal waters of YRD over the past few decades. The results of this study demonstrate the existence of high turbidity zones in coastal waters of the northern abandoned delta and the active lobe of the YRD. These zones have experienced general decreasing trends of SSC at the interannual timescale. Additionally, the distribution of SSC is significantly higher in dry seasons compared to that in wet seasons. The magnitude and varying trends of SSC are strongly influenced by the total bottom shear stress (BSS) caused by combined effects of waves and currents, although the wave-induced BSS is found to be less significant that the current-induced one, but vary more significantly due to wind forcing in the Bohai Sea. Furthermore, the grain size of seabed sediments plays a dominant role in the sediment mobilization and resuspension processes over interannual and longer timescales. The distribution of high BSS and sediment mobility zones align with the active areas for morphological changes with the YRD exhibiting net accretion in the active river mouth and erosion in the abandoned delta lobes. These findings highlight the multiscale variations and the close relationship between SSC and BSS, providing valuable insights into sediment dynamics with geomorphic evolution in highly human-interfered deltaic systems.

Retrieval of suspended sediment concentrations using remote sensing and machine learning methods: A case study of the lower Yellow River

Monitoring suspended sediment concentrations (SSCs) of channel waters is essential for evaluating important issues such as erosion, sediment transport and deposition, reservoir siltation, and water pollution throughout watersheds. Remote sensing technology provides a new approach to large-scale SSC monitoring, but the accuracy of existing retrieval methods based on remote sensing technology still needs to be improved for complex environments. This study proposes a new hyperparameter and globally adaptive retrieval (HGAR) method for river SSC retrieval that combines a remote sensing technique and machine learning methods. The HGAR method uses the light gradient-boosting machine method as the basic model for SSC retrieval, and the tree-structured Parzen estimator method, coefficient of determination, and residual prediction deviation are combined to optimize the basic model’s hyperparameters. The experiment was conducted in the lower Yellow River, where 24,918 Landsat images from 1997 to 2020 were acquired using Google Earth Engine, and 1,772 monthly cloud-free images were obtained in the end. The sequential backward floating selection method was adopted to determine the optimal modeling features. Two regression and three machine learning methods were compared with the proposed method. The results demonstrate that the proposed HGAR method significantly outperforms other retrieval methods in terms of retrieval accuracy (as evidenced by a correlation coefficient of 0.626 and a Nash-Sutcliffe efficiency coefficient of 0.613), generalizability, and robustness. Finally, the model explanation results show that the HGAR method can successfully capture the key information for SSC retrieval and has higher flexibility than traditional statistical models. In addition, we found that the overall fit and the fit of local extreme values are inconsistent in the lower Yellow River, and the proposed improved accuracy indicator balances the two aspects well. In conclusion, the proposed HGAR method can be used effectively to estimate channel SSC and is expected to provide technical support for long-term and large-scale monitoring of the channel surface SSC.

Enhancing suspended sediment concentration retrieval by integrating thermal infrared and optical bands of Landsat-8 and machine learning algorithms

ABSTRACT Suspended Sediment Concentration (SSC) variabilities sizably affect the bio-optical conditions in water bodies. Remote sensing plays a key role in spatiotemporal monitoring of the SSC in inland and coastal waters. However, existing studies on remote sensing of SSC focus on optical domains, i.e. visible and near-infrared bands. Here, we assume thermal infrared bands can potentially contribute to retrieving SSC, inspired by sediments’ heat-trapping effect demonstrated in aquatic research. Thus, we propose to incorporate the thermal infrared bands of Landsat-8 along with the optical bands for the estimation of SSC using machine learning models. In this study, 85 Landsat-8 images collected from the Google Earth Engine (GEE), coupled with in-situ observations from the United States Geological Survey (USGS) gauging stations, were employed to estimate SSC variabilities in Missouri-Mississippi Rivers. The relationship between in-situ SSC and water temperature revealed a promising correlation (R = 0.7), motivating our assumption of incorporating thermal infrared bands. Therefore, we employed both optical and thermal infrared bands of Landsat-8 imagery as predictors for the first time to estimate SSC. Three machine learning algorithms of Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) were considered. The results demonstrated that incorporating thermal infrared bands improved the performance of the models (average improvements in R2 for ~ 9.6% and in RMSE for ~ 18.9%) with respect to the use of optical bands only.

Retrieval of suspended sediment concentration (SSC) in the Arabian Gulf water of arid region by Sentinel-2 data

Suspended sediment concentration (SSC) in water increases temperature and turbidity, limits the photosynthesis of aquatic plants, and reduces biologically available oxygen. It is important to study SSC in the coastal waters of the Arabian Gulf. Thus, this study mapped the SSC of coastal water between Al Arish and Al Ghariyah in northern Qatar using the spectral bands of the MultiSpectral Imager (MSI) of Sentinel-2 by calculating the Normalized Difference Suspended Sediment Index and Normalized Suspended Material Index. The results are studied using the Normalized Difference Turbidity Index and Modified Normalized Difference Water Index. The mapping of SSC in the water using NDSSI showed the presence of a high concentration of suspended sediments between Al Arish and Al Mafjar and a low concentration between Al Mafjar and Al Ghariyah. The mapping of NSMI showed values between 0.012 (clear water) and 0.430 (more suspended material) for the occurrence of suspended materials and supported the results of NDSSI. The study of turbidity using an NDTI image showed turbidity index values ranging from −0.44 (clear water) to 0.12 (high turbidity) and confirmed the occurrence and distribution of suspended sediments and materials in the water. The MNDWI image was able to discriminate clear water with bright pixels from silty sand and mud flats. The relationships between NDSSI, NSMI, and NDTI were correlated with in-situ measurements and studied to find suitable indices to map SSC. Regression analyses showed the strongest relationship between NSMI and NDTI (R2 = 0.95) next to NDSSI and NDTI, where NDTI had the strongest effect on NDSSI (R2 = 0.86). The satellite data results were evaluated by studying the physical parameters and spatial distribution of suspended sediments in the surface and bottom waters. In addition, the grain size distributions, mineral identification, and chemical element concentrations in the bottom sediment samples were studied.

Retrieval of Suspended Sediment Concentration from Bathymetric Bias of Airborne LiDAR.

In addition to depth measurements, airborne LiDAR bathymetry (ALB) has shown usefulness in suspended sediment concentration (SSC) inversion. However, SSC retrieval using ALB based on waveform decomposition or near-water-surface penetration by green lasers requires access to full-waveform data or infrared laser data, which are not always available for users. Thus, in this study we propose a new SSC inversion method based on the depth bias of ALB. Artificial neural networks were used to build an empirical inversion model by connecting the depth bias and SSC. The proposed method was verified using an ALB dataset collected through Optech coastal zone mapping and imaging LiDAR systems. The results showed that the mean square error of the predicted SSC based on the empirical model of ALB depth bias was less than 2.564 mg/L in the experimental area. The proposed method was compared with the waveform decomposition and regression methods. The advantages and limits of the proposed method were analyzed and summarized. The proposed method can effectively retrieve SSC and only requires ALB-derived and sonar-derived water bottom points, eliminating the dependence on the use of green full-waveforms and infrared lasers. This study provides an alternative means of conducting SSC inversion using ALB.

Estimation of Suspended Sediment Concentration in the Yangtze Main Stream Based on Sentinel-2 MSI Data

Suspended sediment concentration (SSC) is an important indicator of water quality that affects the biological processes of river ecosystems and the evolution of floodplains and river channels. The in situ SSC measurements are costly, laborious and spatially discontinuous, while the spaceborne SSC overcome these drawbacks and becomes an effective supplement for in situ observation. However, the spaceborne SSC observations of rivers are more challenging than those of lakes and reservoirs due to their narrow widths and the broad range of SSCs, among other factors. We developed a novel SSC retrieval method that is suitable for the rivers. Water was classified as clear or turbid based on the Forel–Ule index, and optimal SSC models were constructed based on the spectral responses to SSCs in cases of different turbidity. The estimated SSC had a strong correspondence with in situ measurements, with a root mean squared error (RMSE) of 24.87 mg/L and a mean relative error (MRE) of 51.91%. Satellite-derived SSC showed good consistency with SSCs obtained from gauging stations (r2 > 0.79). We studied the spatiotemporal variation in SSC in the Yangtze main stream from 2017 to 2021. It increased considerably from May to October each year, with the peak generally occurring in July or August (ca. 200–300 mg/L in a normal year and 800–1000 mg/L in a flood year), while it remained stable and decreased to around 50 mg/L from November to April of the following year. It was high in the east and low in the west, with local maxima in Chongqing (ca. 80–150 mg/L) and in the lower Dongting Lake reaches (ca. 80–100 mg/L) and a local minima in the downstream of the Three Gorges Dam (ca. 1–20 mg/L). Case studies in the Yibin reach and Three Gorges Reservoir determined that local variation in SSCs is due to special hydrodynamic conditions and anthropogenic activities. The procedure applied to process Sentinel-2 imagery and the novel SSC retrieval method we developed supplement the deficiencies in river SSC retrieval.

Retrieval of suspended sediment concentration of the Chilika Lake, India using Landsat-8 OLI satellite data

To monitor sediment variations in the Chilika Lake, the Landsat-8 OLI data was used to calibrate suspended sediment concentration (SSC) model. The relationship between remote sensing reflectance of OLI bands and in-situ measured SSC were used to develop new site-specific algorithms. Four different models were calibrated in this study for retrieval of SSC using in-situ observation and remote sensing reflectance of OLI data. The multiband linear regression model provided better result (R2 = 0.6) as compared to the single-band regression model (R2 = 0.45, polynomial; R2 = 0.38, exponential and R2 = 0.39, linear). The Landsat-8 OLI image shows spatiotemporal variations of SSC during pre and post-monsoon season (2013–15) in the lake. It is observed that the SSC variation is predominantly influenced by three factors: monsoon effect, wind-induced re-suspension of bottom sediments and influx of river water into the lake. It is also observed that due to the impact of severe tropical cyclone Phailin, there was a rapid increase of SSC in the lake.

Estimating River Sediment Discharge in the Upper Mississippi River Using Landsat Imagery

With the decline of operational river gauges monitoring sediments, a viable means of quantifying sediment transport is needed. In this study, we address this issue by applying relationships between hydraulic geometry of river channels, water discharge, water-leaving surface reflectance (SR), and suspended sediment concentration (SSC) to quantify sediment discharge with the aid of space-based observations. We examined 5490 Landsat scenes to estimate water discharge, SSC, and sediment discharge for the period from 1984 to 2017 at nine gauging sites along the Upper Mississippi River. We used recent advances in remote sensing of fluvial systems, such as automated river width extraction, Bayesian discharge inference with at-many-stations hydraulic geometry (AMHG), and SSC-SR regression models. With 621 Landsat scenes available from all the gauging sites, the results showed that the water discharge and SSC retrieval from Landsat imagery can yield reasonable sediment discharge estimates along the Upper Mississippi River. An overall relative bias of −25.4, mean absolute error (MAE) of 6.24 × 104 tonne/day, relative root mean square error (RRMSE) of 1.21, and Nash–Sutcliffe Efficiency (NSE) of 0.49 were obtained for the sediment discharge estimation. Based on these statistical metrics, we identified three of the nine gauging sites (St. Louis, MO; Chester, IL; and Thebes, IL), which were in the downstream portion of the river, to be the best locations for estimating water and sediment discharge using Landsat imagery.

An integrated optic and acoustic (IOA) approach for measuring suspended sediment concentration in highly turbid environments

Accurate measurement of suspended sediment concentration (SSC) in highly turbid environments has been a problem due to optical or acoustic signal saturation and attenuation. The saturation returns a limited measurement range, and the attenuation raises an ambiguity problem that a low optical or acoustic output could mean a low or a high SSC. In this study, an integrated optic and acoustic (IOA) approach is proposed to (i) overcome the ambiguity problem; (ii) increase the measurement range to high SSC values; and (iii) obtain high-resolution SSC profiles. The IOA approach is a combination of Argus Suspension Meter (ASM), Optical Backscatter Sensor (OBS) and Acoustic Doppler Velocimeter (ADV). In this approach, the ASM-derived SSC is preferred because of its lowest relative error, followed by OBS and ADV. The ASM can produce high-resolution (1 cm interval) SSC profiles when it is not saturated (usually SSC < 9 g/L). When ASM is saturated, the SSC is recovered by OBS. Since the ambiguity problem is solved, the measurement range of OBS and ADV can be extended up to 300 g/L. The best way to use an ADV, however, is to have a rough estimation first and assist in the OBS conversion, because its estimates contain large uncertainty. To further mitigate the impact of sediment particle size on SSC retrieval, we suggest the usage of in-situ sediment samples for sensor calibration. The IOA approach was verified in the Yangtze Estuary which is a highly turbid system. Comparison of the IOA approach outputs against water sampling results demonstrates the reliability of the IOA approach with a relative error of 17–34%. The observed high SSCs were up to 63 g/L. The field data show that high SSCs were confined in the benthic layer (within 2 m above the bed) in the wet season under a high river discharge, whereas the suspension was better mixed throughout the water column in the dry season. • A combination of ASM, OBS and ADV resolves the ambiguity problem of two possible SSC values for an OBS or ADV output. • The integrated optic and acoustic (IOA) approach enlarges the measurement range of OBS and ADV to 300 g/L. • The IOA approach provides high-resolution (1 cm) SSC profiles by ASM. • The IOA approach was successfully applied in the Yangtze Estuary with SSC > 10 g/L.

Retrieval of suspended sediment concentrations using Landsat-8 OLI satellite images in the Orinoco River (Venezuela)

In this study, 81 Landsat-8 scenes acquired from 2013 to 2015 were used to estimate the suspended sediment concentration (SSC) in the Orinoco River at its main hydrological station at Ciudad Bolivar, Venezuela. This gauging station monitors an upstream area corresponding to 89% of the total catchment area where the mean discharge is of 33,000m3·s−1. SSC spatial and temporal variabilities were analyzed in relation to the hydrological cycle and to local geomorphological characteristics of the river mainstream. Three types of atmospheric correction models were evaluated to correct the Landsat-8 images: DOS, FLAASH, and L8SR. Surface reflectance was compared with monthly water sampling to calibrate a SSC retrieval model using a bootstrapping resampling. A regression model based on surface reflectance at the Near-Infrared wavelengths showed the best performance: R2=0.92 (N=27) for the whole range of SSC (18 to 203mg·l−1) measured at this station during the studied period. The method offers a simple new approach to estimate the SSC along the lower Orinoco River and demonstrates the feasibility and reliability of remote sensing images to map the spatiotemporal variability in sediment transport over large rivers.

Evaluation of spatiotemporal differences in suspended sediment concentration derived from remote sensing and numerical simulation for coastal waters

Remote sensing and numerical models are often used to monitor the suspended sediment concentration (SSC) in coastal waters; however, the derived SSC varies between the two methods in both space and time. In this study, a method was proposed to assess the spatiotemporal differences in SSC derived from Moderate Resolution Imaging Spectroradiometer (MODIS) images and numerical simulation for coastal waters, using the Bohai Sea in China as an example. An empirical model for SSC retrieval from remote sensed images was initially established. A comparison of the temporal synchronicity over a single day period was performed between the observed data and the numerically simulated results. The range in the SSC at different observation sites was significantly different. Both the SSC values and their daily variation ranges were larger near the estuary of the Yellow River compared with the open area due to the concurrence of tidal flow and the introduction of fresh river water with high turbidity near the estuary. The areas that exhibited spatial differences were defined according to their differences in remotely sensed and numerically simulated SSC distribution patterns. Finally, the reasons for these spatiotemporal differences were discussed. The results provided understanding into the spatiotemporal differences that were introduced when multi-source data were used, thus improving the accuracy of the results when monitoring coastal environments for the management of coastal conservation.

The optical properties of river and floodplain waters in the Amazon River Basin: Implications for satellite‐based measurements of suspended particulate matter

AbstractSatellite images can now be used to assess river sediment discharge, and systematic studies over rivers and lakes are required to support such applications and document the variability of inland water optical properties at the watershed scale. The optical properties of the Amazon Basin waters were analyzed from in situ measurements of the remote sensing reflectance (Rrs) at 279 stations and downwelling diffuse attenuation coefficients (Kd) at 133 stations. Measurements of the apparent optical properties, suspended particulate matter (SPM) contents, and characteristics and colored dissolved organic matter (CDOM) absorption spectra were performed during 16 cruises along the main Amazonian Rivers draining the Andes and for some tributaries. Surface‐suspended sediment granulometry and mineralogy showed a stable distribution at the catchment scale, even over large distances and between tributaries. The particle number‐size distribution was best described using a segmented distribution with a slope of 2.2 for the fine range (1–15 µm), and the CDOM absorption coefficient at 440 nm varied from 1.8 to 7.9 m−1. Overall, both Rrs and Kd were strongly correlated with SPM, although strong CDOM absorption limited the use of the blue spectrum. Reflectance saturation from blue to red was observed at approximately 100 g m−3, whereas the near‐infrared (NIR) wavelength enabled the monitoring of the full SPM range (5–620 g m−3). In contrast, Kd showed no saturation for SPM from green to NIR, and a linear model was calculated. The use of the reflectance ratio was investigated and shown to improve the suspended sediment concentration retrieval performance.

A Semi-Analytical Model for Remote Sensing Retrieval of Suspended Sediment Concentration in the Gulf of Bohai, China

Suspended sediment concentration (SSC) is one of the most critical parameters in ocean ecological environment evaluation and it can be determined using ocean color remote sensing (RS). The purpose of this study is to develop a model that provides a reliable and sensitive evaluation of SSC retrieval using RS data. Data were acquired for and gathered from the Gulf of Bohai where SSC levels are relatively low with an average value below 30 mg·L−1. The study indicates that the most sensitive band to SSC levels in the study area is the NIR band of Landsat5 TM images. A quadratic polynomial semi-analytical model appears to be the best retrieval model based on the relationship between the inherent optical properties (IOPs) and apparent optical properties (AOPs) of water as described by the quasi-analytical algorithm (QAA). The model has a higher precision and effectiveness for SSC retrieval than data-driven statistical models, especially when SSC level is relatively high. The average relative error and the root mean square error (RMSE) are 12.32% and 4.53 mg·L−1, respectively, while the correlation coefficient between observed and estimated SSC by the model is 0.95. Using the proposed retrieval model and TM data, SSC levels of the entire study region in the Gulf of Bohai were estimated. These estimates can serve as the baseline for efficient monitoring of the ocean environment in the future.

Contribution of Raman Scattering in Remote Sensing Retrieval of Suspended Sediment Concentration by Empirical Modeling

In this paper, the author investigates the effect of Raman scattering on remotely sensed suspended sediment concentration (SSC) through the analysis of various SSC retrieval models in the coastal waters (Case 2) of the Bay of Bengal. A higher Raman contribution is observed in waters containing less SSC and chlorophyll-a concentration. Tassan's model, which used three wavelengths, i.e., 490, 555, and 670 nm, is able to account for Raman contribution at three emission wavelengths. The analysis showed the highest ~14% fraction in the computed SSC and 8.4% fraction in the reflectance (at 555 nm) due to Raman scattering. A fraction of ~14% in the SSC due to Raman scattering is significant and should be addressed in SSC retrieval models. More channels (wavelengths) are required to adequately account for the Raman contribution for estimating SSC from the remote sensing reflectance. The inclusion of blue and infrared wavelengths in the empirical models of SSC retrieval is suggested as a topic of future research, because the excitation and emission do occur at these respective wavelengths.

Numerical simulation-aided MODIS capture of sediment transport for the Bohai Sea in China

Traditionally, the establishment of a remote-sensing retrieval model depends on in situ data acquired by expensive and considerably labour-intensive field surveys, but now we can benefit from numerical simulation. In this study, after considering the advantages of numerical simulation in extending the spatial and temporal suspended sediment concentration (SSC), we propose an approach to establish an SSC retrieval model from remotely sensed images with the aid of a numerical model for the Bohai Sea in China. First, significant correlation, between simulated SSC and atmosphere-corrected water-leaving reflectance derived from Moderate Resolution Imaging Spectroradiometer (MODIS) band-1 images, was investigated to demonstrate a novel SSC retrieval model. The synchronous data from remote sensing and numerical simulations enabled the removal of spatial and temporal differences between satellite and in situ measurements. Based on this retrieval model, the SSC distribution pattern was simulated in two scenarios to separate river sediment discharge from the overall process of resuspension. The results indicate that the distribution pattern of the simulated surface SSC without sediment discharge from the Yellow River in most study areas generally matched that derived from MODIS images, except for the estuarine area. The surface SSC 30–40 km away from the estuaries are dominated by sediment discharge from rivers. The proposed method, the numerical simulation-aided SSC retrieval MODIS model, can efficiently capture the sediment transport in the Bohai Sea, which is helpful for operational monitoring of the water environment in estuary and coastal waters.

Empirical models for estimating the suspended sediment concentration in Amazonian white water rivers using Landsat 5/TM

Suspended sediment yield is a very important environmental indicator within Amazonian fluvial systems, especially for rivers dominated by inorganic particles, referred to as white water rivers. For vast portions of Amazonian rivers, suspended sediment concentration (SSC) is measured infrequently or not at all. However, remote sensing techniques have been used to estimate water quality parameters worldwide, from which data for suspended matter is the most successfully retrieved. This paper presents empirical models for SSC retrieval in Amazonian white water rivers using reflectance data derived from Landsat 5/TM. The models use multiple regression for both the entire dataset (global model, N=504) and for five segmented datasets (regional models) defined by general geological features of drainage basins. The models use VNIR bands, band ratios, and the SWIR band 5 as input. For the global model, the adjusted R2 is 0.76, while the adjusted R2 values for regional models vary from 0.77 to 0.89, all significant (p-value<0.0001). The regional models are subject to the leave-one-out cross validation technique, which presents robust results. The findings show that both the average error of estimation and the standard deviation increase as the SSC range increases. Regional models were more accurate when compared with the global model, suggesting changes in optical proprieties of water sampled at different sampling stations. Results confirm the potential for the estimation of SSC from Landsat/TM historical series data for the 1980s and 1990s, for which the in situ database is scarce. Such estimates supplement the SSC temporal series, providing a more comprehensive SSC temporal series which may show environmental dynamics yet unknown.

Remotely sensed variability of the suspended sediment concentration and its response to decreased river discharge in the Yangtze estuary and adjacent coast

Satellite observation is an excellent tool for exploring the variability of the suspended sediment concentration (SSC) of turbid estuarine and coastal waters. We used a recently developed semi-empirical radiative transfer model combined with a multi-wavelength switching algorithm for the SSC retrieval from MEdium Resolution Imaging Spectrometer (MERIS) satellite data. This method can successfully retrieve SSC from satellite data in turbid estuarine and coastal waters with a wide range of sediment concentrations (20–2500mgl−1) and is robust for quantifying realistic patterns of the surface sediment dynamics. The seasonal and annual variability of the MERIS-derived SSC from 2003 to 2010 were analysed in this work. Five regions-of-interest (ROIs) in the Yangtze estuary and coast are included in the analysis: the upper estuary, the lower estuary, the outer estuary, the Hangzhou Bay and the Qidong shore. The results reveal that the SSC of the upper estuary has significant seasonal and annual variations in response to seasonal cycling and annual fluctuation of the river discharge. A long-term continuing decrease of river discharge may cause an overall decline of the SSC in the entire estuary and adjacent areas. The existence of horizontal exchanges of the sediments between the Yangtze estuary and the Jiangsu coast implies that the decreased fluvial sediment loads of the estuary may partially be compensated by supplementing contributions from other origins.

Estimation on Scale Error of SSC Retrieval Model Based on Scale Expansion Method

Although the variance of suspended sediment concentration (SSC) distribution is an important parameter for estimating scale error of SSC retrieval results, in the past it has not been possible to use this parameter because the requisite comprehensive oceanic optical data sets have not been available. In this study, the approaches of scale expansion, correlation analysis, and regression analysis were used to reveal the relationship between the variance of SSC distribution and the SSC of central pixel, average SSC, and pixel size in a moving window. According to the study results, it was found that the variance of SSC distribution was highly correlated to the SSC of central pixel and average SSC in a given scale window, while less correlated to pixel size. Additionally, the relationship model between the variance of SSC distribution and average SSC in a given scale window was used to correct the scale errors in retrieval results combined with a scale error model. The results showed that the greatest scale error of SSC retrieval results was up to 30%.

Remote sensing retrieval of suspended sediment concentration in shallow waters

The dynamics of coastal lagoons and estuarine areas is characterized by a delicate balance between biological and physical processes and the comprehension and monitoring of such processes require observations over a wide range of temporal and spatial scales. Remote sensing techniques in this context are very advantageous and potentially allow overcoming the spatial limitations of traditional in situ point observations, providing new opportunities for a better understanding of the relevant bio-geomorphological processes and for the calibration and validation of spatially-distributed hydrodynamic and transport models. Remote sensing of suspended particulate matter (SPM) concentration in shallow waters must, however, overcome the difficulties associated with i) the influence of bottom reflection, which may interfere with an accurate retrieval; ii) the necessity of accurately knowing the optical properties of the suspended matter, and iii) the importance of providing an assessment of the uncertainty associated with the estimates produced. This work presents a method to estimate SPM concentration in lagoon/estuarine waters by use of a simplified radiative transfer model. We use a calibration/validation method based on cross-validation and bootstrap techniques to provide a statistically sound determination of model parameters and an evaluation of the uncertainty induced by their inaccurate determination as well as by the uncertain knowledge of the bottom sediment reflectance. The method is applied to the Venice lagoon, using observations from a network of turbidity sensors and from several multispectral satellite sensors (LANDSAT, ASTER and ALOS AVNIR). The bootstrap and cross-validation procedures employed show that consistent estimates of SPM concentration can indeed be retrieved from satellite remote sensing, provided that sufficient in situ ancillary information for appropriate calibration is available. The quantification of the estimation uncertainty shows that retrievals obtained from remote sensing are accurate, robust and repeatable. The SPM concentration maps produced show a general coherence with known features in the Venice lagoon and, together with suitable biological information, point to the role played by benthic vegetation in the stabilization of the bottom sediment.

Satellite Estimates of Wide-Range Suspended Sediment Concentrations in Changjiang (Yangtze) Estuary Using MERIS Data

The Changjiang (Yangtze) estuarine and coastal waters are characterized by suspended sediments over a wide range of concentrations from 20 to 2,500 mg l−1. Suspended sediment plays important roles in the estuarine and coastal system and environment. Previous algorithms for satellite estimates of suspended sediment concentration (SSC) showed a great limitation in that only low to moderate concentrations (up to 50 mg l−1) could be reliably estimated. In this study, we developed a semi-empirical radiative transfer (SERT) model with physically based empirical coefficients to estimate SSC from MERIS data over turbid waters with a much wider range of SSC. The model was based on the Kubelka–Munk two-stream approximation of radiative transfer theory and calibrated using datasets from in situ measurements and outdoor controlled tank experiments. The results show that the sensitivity and saturation level of remote-sensing reflectance to SSC are dependent on wavelengths and SSC levels. Therefore, the SERT model, coupled with a multi-conditional algorithm scheme adapted to satellite retrieval of wide-range SSC, was proposed. Results suggest that this method is more effective and accurate in the estimation of SSC over turbid waters.