Variability of apparent and inherent optical properties of sediment-laden waters in large river basins - lessons from in situ measurements and bio-optical modeling

Effects of spectral variability due to sediment and bottom characteristics on remote sensing for suspended sediment in shallow rivers

Advances in remote sensing techniques for water environments have led to acquisition of abundant data on suspended sediment concentration (SSC). However, confounding factors, such as particle sizes, mineral properties, and bottom materials, have not been fully studied, despite their substantial interference with the detection of intrinsic signals of suspended sediments. Therefore, we investigated the spectral variability arising from the sediment and bottom using laboratory and field-scale experiments. In the laboratory experiment, we focused on measuring spectral characteristics of suspended sediment according to particle size and sediment type. The laboratory experiment was conducted under conditions of completely mixed sediment and non-bottom reflectance using a specially designed rotating horizontal cylinder. To investigate the effects of different channel bottoms under sediment-laden flow conditions, we performed sediment tracer tests in field-scale channels comprising sand and vegetated bottoms. Based on experimental datasets, we performed spectral analysis and multiple endmember spectral mixture analysis (MESMA) to quantify the effect of spectral variability of sediment and bottom on the relationship between hyperspectral data and SSC. The results showed that optimal spectral bands were precisely estimated under non-bottom reflectance conditions, and the effective wavelengths depended on the sediment type. The fine sediments had a higher backscattering intensity compared to the coarse sediments, and the reflectance difference according to the particle size difference increased as the SSC increased. However, in the field-scale experiment, the bottom reflectance substantially decreased the R2 in the relationship between the hyperspectral data and SSC. Nevertheless, MESMA can quantify the contribution of suspended sediment and bottom signals as fractional images. Moreover, the suspended sediment fraction had a clear exponential relationship with SSC in all cases. We conclude that MESMA-driven sediment fractions could be an important alternative for estimating SSC in shallow rivers, as it quantifies the contributions of each factor and then minimizes the bottom effect.

Nanoparticles containing hazardous elements and the spatial optics of the Sentinel-3B OLCI satellite in Amazonian rivers: a potential tool to understand environmental impacts

The Amazon River is the longest river in the world. The Tapajós River is a tributary to the Amazon. At their junction, a marked decrease in water quality is evident from negative impacts from the constant activity of clandestine gold mining in the Tapajós River watershed. The accumulation of hazardous elements (HEs), capable of compromising environmental quality across large regions is evident in the waters of the Tapajós. Sentinel-3B OLCI (Ocean Land Color Instrument) Level-2 satellite imagery with Water Full Resolution (WFR) of 300 m was utilized to detect the highest potential for the absorption coefficient of detritus and gelbstoff in 443 m^-1 (ADG443_NN), chlorophyll-a (CHL_NN) and total suspended matter concentration (TSM_NN), at 25 points in the Amazon and Tapajós rivers (in 2019 and 2021). Physical samples of riverbed sediment collected in the field at the same locations were analyzed for NPs and ultra-fine particles to verify the geospatial findings. The riverbed sediment samples collected in the field were studied by Transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM), with selected area electron diffraction (SAED), following laboratory analytical procedures. The Sentinel-3B OLCI images, based on the Neural Network (NN) were calibrated by the European Space Agency (ESA), with a standard average normalization of 0.83 µg/mg, containing a maximum error of 6.62% applied to the sampled points. The analysis of the riverbed sediment samples revealed the presence of the following hazardous elements: As, Hg, La, Ce, Th, Pb, Pd, among several others. The Amazon River has significant potential to transport ADG443_NN (55.475 m^-1) and TSM_NN (70.787 gm^-3) in sediments, with the possibility of negatively impacting marine biodiversity, in addition to being harmful to human health over very large regions.

Hyperspectral retrievals of suspended sediment using cluster-based machine learning regression in shallow waters

Remote sensing of suspended sediment in shallow waters is challenging because of the increased optical variability of the water, resulting from the influence of suspended matter in the water column and the heterogeneous bottom properties. To overcome this limitation, in this study, we developed a novel framework called cluster-based machine learning regression for optical variability (CMR-OV), using the Gaussian mixture model (GMM) clustering technique and a random forest regressor (RFR). We evaluated the model using an optically complex dataset from a field-scale experiment. This experiment was conducted with four sediment types injected into an experimental meandering channel divided into two reaches with submerged vegetation and a natural sand bottom. We obtained high-resolution hyperspectral images using unmanned aerial vehicles (UAVs) and measured the in situ suspended sediment concentration using laser diffraction sensors. Based on optical similarity, we used CMR-OV to divide the hyperspectral dataset into several clusters. Then, we built separate RFR models for each cluster using the corresponding spectral bands that were selected using recursive feature elimination (RFE). Thus, we found that the proposed CMR-OV yielded superior results compared to the conventional RFR model, decreasing the total error score by 10.81%. The optical spectral bands of each cluster were distinguished from each other, indicating that the datasets that were spectrally discriminated from clustering enhanced the performance of the estimator. By comparing the clustered spectral dataset and physical factors, we proved the bottom type was the most critical factor in separating the clusters, even though the variability in the sediment properties also induced substantial spectral changes. Our findings demonstrated that CMR-OV accurately reproduced the spatiotemporal distribution of suspended sediment under optically complex conditions by addressing the heterogeneity of bottom reflectance in shallow water.

Optical Classification of Lower Amazon Waters Based on In Situ Data and Sentinel-3 Ocean and Land Color Instrument Imagery

Optical water types (OWTs) were identified from an in situ dataset of concomitant biogeochemical and optical parameters acquired in the Amazon River and its tributaries, in the Lower Amazon region, at different hydrological conditions from 2014 to 2017. A seasonal bio-optical characterization was performed. The k-means classification was applied to the in situ normalized reflectance spectra (rn(λ)), allowing the identification of four OWTs. An optical index method was also applied to the rn(λ) defining the thresholds of the OWTs. Next, level-3 Sentinel-3 Ocean and Land Color Instrument images representative of the seasonal discharge conditions were classified using the identified in situ OWTs as reference. The differences between Amazon River and clearwater tributary OWTs were dependent on the hydrological dynamics of the Amazon River, also showing a strong seasonal variability. Each OWT was associated with a specific bio-optical and biogeochemical environment assessed from the corresponding absorption coefficient values of colored dissolved organic matter (aCDOM) and particulate matter (ap), chlorophyll-a and suspended particulate matter (SPM) concentrations, and aCDOM/ap ratio. The rising water season presented a unique OWT with high SPM concentration and high relative contribution of ap to total absorption compared to the other OWTs. This bio-optical characterization of Lower Amazon River waters represents a first step for developing remote sensing inversion models adjusted to the optical complexity of this region.

Spatiotemporal Dynamics of Suspended Sediments in the Negro River, Amazon Basin, from In Situ and Sentinel-2 Remote Sensing Data

Monitoring suspended sediments through remote sensing data in black-water rivers is a challenge. Herein, remote sensing reflectance (Rrs) from in situ measurements and Sentinel-2 Multi-Spectral Instrument (MSI) images were used to estimate the suspended sediment concentration (SSC) in the largest black-water river of the Amazon basin. The Negro River exhibits extremely low Rrs values (<0.005 sr−1 at visible and near-infrared bands) due to the elevated absorption of coloured dissolved organic matter (aCDOM at 440 nm > 7 m−1) caused by the high amount of dissolved organic carbon (DOC > 7 mg L−1) and low SSC (<5 mg L−1). Interannual variability of Rrs is primarily controlled by the input of suspended sediments from the Branco River, which is a clear water river that governs the changes in the apparent optical properties of the Negro River, even at distances that are greater than 90 km from its mouth. Better results were obtained using the Sentinel-2 MSI Red band (Band 4 at 665 nm) in order to estimate the SSC, with an R2 value greater than 0.85 and an error less than 20% in the adjusted models. The magnitudes of water reflectance in the Sentinel-2 MSI Red band were consistent with in situ Rrs measurements, indicating the large spatial variability of the lower SSC values (0 to 15 mg L−1) in a complex anabranching reach of the Negro River. The in situ and satellite data analysed in this study indicates sedimentation processes in the lower Negro River near the Amazon River. The results suggest that the radiometric characteristics of sensors, like sentinel-2 MSI, are suitable for monitoring the suspended sediment concentration in large tropical black-water rivers.

Particle Size Parameters of Particulate Matter Suspended in Coastal Waters and Their Use as Indicators of Typhoon Influence

The power law particle size distribution (PSD) slope parameter is commonly used to characterize sediment fluxes, resuspension, aggregates, and settling rates in coastal and estuarine waters. However, particle size distribution metrics are also very useful for understanding sediment source and dynamic processes. In this study, a method was proposed to employ the particle size parameters commonly used in sedimentary geology (average particle size (ø), sorting, skewness, and kurtosis) as indicators of changes in sediment dynamic processes, and MODIS images were used to estimate these parameters. The particle size parameters were estimated using a Mie scattering model, Quasi-Analytical Algorithm (QAA) analysis algorithm, and least squares QR decomposition (LSQR) solution method based on the relationship between the power law distribution of the suspended particles and their optical scattering properties. The estimates were verified by field measurements in the Yellow Sea and Bohai Sea regions of China. This method provided good estimates of the average particle size (ø), sorting, and kurtosis. A greater number of wavebands (39) was associated with more accurate particle size distribution curves. Furthermore, the method was used to monitor changes in suspended particulate matter in the vicinity of the Heini Bay of China before and after the passage of a strong storm in August 2011. The particle size parameters represented the influence of a strong typhoon on the distribution of the near-shore sediment and, together with the PSD slope, comprehensively reflected the changes in the near-shore suspended particulate matter. This method not only established the relationship between remote sensing monitoring and the historical sediment record, it also extends the power law model to the application of sediment source and dynamic processes in coastal waters.

Linking flow-stream variability to grain size distribution of suspended sediment from a satellite-based analysis of the Tiber River plume (Tyrrhenian Sea)

Several coastal regions on Earth have been increasingly affected by intense, often catastrophic, flash floods that deliver significant amounts of sediment along shorelines. One of the critical questions related to the impact of these impulsive runoffs is "are flash floods more efficient in delivering non-cohesive sandy sediment along the coasts?" Here we relate flow stages (i.e., from erratic to persistent) to the grain size distribution of the suspended load, by performing a synergic analysis of in-situ river discharge and satellite-retrieved grain size distribution, from 2002 to 2014, covering the 2012 Tiber River (Italy) exceptional flood event. Our analysis shows novel and promising results regarding the capability of remote sensing in characterizing suspended sediment in terms of grain size distribution and reveals that erratic stages favour delivering of non-cohesive sandy sediment more than the persistent stages. This conclusion is supported by numerical simulations and is consistent with previous studies on suspended sediment rating curves.

Retrieving Total and Inorganic Suspended Sediments in Amazon Floodplain Lakes: A Multisensor Approach

Remote sensing imagery are fundamental to increasing the knowledge about sediment dynamics in the middle-lower Amazon floodplains. Moreover, they can help to understand both how climate change and how land use and land cover changes impact the sediment exchange between the Amazon River and floodplain lakes in this important and complex ecosystem. This study investigates the suitability of Landsat-8 and Sentinel-2 spectral characteristics in retrieving total (TSS) and inorganic (TSI) suspended sediments on a set of Amazon floodplain lakes in the middle-lower Amazon basin using in situ Remote Sensing Reflectance (Rrs) measurements to simulate Landsat 8/OLI (Operational Land Imager) and Sentinel 2/MSI (Multispectral Instrument) bands and to calibrate/validate several TSS and TSI empirical algorithms. The calibration was based on the Monte Carlo Simulation carried out for the following datasets: (1) All-Dataset, consisting of all the data acquired during four field campaigns at five lakes spread over the lower Amazon floodplain (n = 94); (2) Campaign-Dataset including samples acquired in a specific hydrograph phase (season) in all lakes. As sample size varied from one season to the other, n varied from 18 to 31; (3) Lake-Dataset including samples acquired in all seasons at a given lake with n also varying from 17 to 67 for each lake. The calibrated models were, then, applied to OLI and MSI scenes acquired in August 2017. The performance of three atmospheric correction algorithms was also assessed for both OLI (6S, ACOLITE, and L8SR) and MSI (6S, ACOLITE, and Sen2Cor) images. The impact of glint correction on atmosphere-corrected image performance was assessed against in situ glint-corrected Rrs measurements. After glint correction, the L8SR and 6S atmospheric correction performed better with the OLI and MSI sensors, respectively (Mean Absolute Percentage Error (MAPE) = 16.68% and 14.38%) considering the entire set of bands. However, for a given single band, different methods have different performances. The validated TSI and TSS satellite estimates showed that both in situ TSI and TSS algorithms provided reliable estimates, having the best results for the green OLI band (561 nm) and MSI red-edge band (705 nm) (MAPE < 21%). Moreover, the findings indicate that the OLI and MSI models provided similar errors, which support the use of both sensors as a virtual constellation for the TSS and TSI estimate over an Amazon floodplain. These results demonstrate the applicability of the calibration/validation techniques developed for the empirical modeling of suspended sediments in lower Amazon floodplain lakes using medium-resolution sensors.

Optical wide-field tomography of sediment resuspension

We present a wide-field imaging approach to optically sense underwater sediment resuspension events. It uses wide-field multi-directional views and diffuse backlight. Our approach algorithmically quantifies the amount of material resuspended and its spatiotemporal distribution. The suspended particles affect the radiation that reaches the cameras, hence the captured images. By measuring the radiance during and prior to resuspension, we extract the optical depth on the line of sight per pixel. Using computed tomography (CT) principles, the optical depths yield estimation of the extinction coefficient of the suspension, per voxel. The suspended density is then derived from the reconstructed extinction coefficient.

Measurement uncertainties in PSICAM and reflective tube absorption meters

The nature and magnitude of measurement uncertainties (precision and accuracy) associated with two approaches for measuring absorption by turbid waters (b(532 nm) ranging from 0.20 m^-1 to 22.89 m^-1) are investigated here: (a) point source integrating cavity absorption meters (PSICAM), and (b) reflective tube absorption meters (AC-9 and AC-s - both WET Labs Inc., USA). Absolute measurement precision at 440 nm was quantified using standard deviations of triplicate measurements for the PSICAM and de-trended, bin averaged time series for the AC-9/s, giving comparable levels (< 0.006 m^-1) for both instruments. Using data collected from a wide range of UK coastal waters, PSICAM accuracy was assessed by comparing both total non-water absorption and absorption by coloured dissolved organic material (CDOM) measured on discrete samples by two independent PSICAMs. AC-9/s performance was tested by comparing total non-water absorption measured in situ by an AC-9 and an AC-s mounted on the same frame. Results showed that the PSICAM outperforms AC-9/s instruments with regards to accuracy, with average spread in the PSICAM total absorption data of 0.006 m^-1 (RMSE) compared to 0.028 m^-1 for the AC-9/s devices. Despite application of a state of the art scattering correction method, the AC-9/s instruments still tend to overestimate absorption compared to PSICAM data by on average 0.014 m^-1 RMSE (AC-s) and 0.043 m^-1 RMSE (AC-9). This remaining discrepancy can be largely attributed to residual limitations in the correction of AC-9/s data for scattering effects and limitations in the quality of AC-9/s calibration measurements.