An Algorithm to Estimate Suspended Particulate Matter Concentrations and Associated Uncertainties from Remote Sensing Reflectance in Coastal Environments

Ocean Color Remote Sensing of Suspended Sediments along a Continuum from Rivers to River Plumes: Concentration, Transport, Fluxes and Dynamics

This study investigates the capability of high and medium spatial resolution ocean color satellite data to monitor the transport of suspended particulate matter (SPM) along a continuum from river to river mouth to river plume. An existing switching algorithm combining the use of green, red and near-infrared satellite wavebands was improved to retrieve SPM concentrations over the very wide range (from 1 to more than 1000 g.m−3) encountered over such a continuum. The method was applied to time series of OLI, MSI, and MODIS satellite data. Satisfactory validation results were obtained even at the river gauging station. The river liquid discharge is not only related to the SPM concentration at the gauging station and at the river mouth, but also to the turbid plume area and SPM mass estimated within the surface of the plume. The overall results highlight the potential of combined field and ocean color satellite observations to monitor the transport and fluxes of SPM discharged by rivers into the coastal ocean.

Remote Sensing Inversion of Suspended Matter Concentration Using a Neural Network Model Optimized by the Partial Least Squares and Particle Swarm Optimization Algorithms

Suspended matter concentration is an important index for the assessment of a water environment and it is also one of the core parameters for remote sensing inversion of water color. Due to the optical complexity of a water body and the interaction between different water quality parameters, the remote sensing inversion accuracy of suspended matter concentration is currently limited. To solve this problem, based on the remote sensing images from Gaofen-2 (GF-2) and the field-measured suspended matter concentration, taking a section of the Haihe River as the study area, this study establishes a remote sensing inversion model. The model combines the partial least squares (PLS) algorithm and the particle swarm optimization (PSO) algorithm to optimize the back-propagation neural network (BPNN) model, i.e., the PLS-PSO-BPNN model. The partial least squares algorithm is involved in screening the input values of the neural network model. The particle swarm optimization algorithm optimizes the weights and thresholds of the neural network model and it thus effectively overcomes the over-fitting of the neural network. The inversion accuracy of the optimized neural network model is compared with that of the partial least squares model and the traditional neural network model by determining the coefficient, the mean absolute error, the root mean square error, the correlation coefficient and the relative root mean square error. The results indicate that the root mean squared error of the PLS-PSO-BPNN inversion model was 3.05 mg/L, which is higher than the accuracy of the statistical regression model. The developed PLS-PSO-BPNN model could be widely applied in other areas to better invert the water quality parameters of surface water.

Global Estimation of Suspended Particulate Matter From Satellite Ocean Color Imagery

The suspended particulate matter (SPM) concentration (unit: mg l^-1) in surface waters is an essential measure of water quality and clarity. Satellite remote sensing provides a powerful tool to derive the SPM with synoptic and repeat coverage. In this study, we developed a new global SPM algorithm utilizing the remote sensing reflectance (R _rs (λ)) at near-infrared (NIR), red, green, and blue bands (NIR-RGB) as input. The evaluations showed that the NIR-RGB algorithm could predict SPM with the median absolute percentage difference of ∼35%-39% over a wide range from ∼0.01 to >2,000 mg l^-1. The uncertainty is smaller (29%-37%) for turbid waters where R _rs (671) ≥ 0.0012 sr^-1 and slightly higher (41%-44%) for clear waters where R _rs (671) < 0.0012 mg l^-1. The algorithm was implemented with the global R _rs (λ) data from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. We provided a brief characterization of the spatial distribution and temporal trends of the SPM products in global waters based on the monthly SPM composites. Case studies of the SPM time series in coastal and inland waters suggest that the satellite SPM estimations registered spatial and seasonal variation and episodic events in regional scales as well. The VIIRS-generated global SPM maps provide a valuable addition to the existing ocean color products for environmental and climate applications.

Lockdown effects on total suspended solids concentrations in the Lower Min River (China) during COVID-19 using time-series remote sensing images

The COVID-19 pandemic in China in the winter-spring of 2019-2020 has decreased and even stopped many human activities. This study investigates whether there were any changes in the water quality of the Lower Min River (China) during the lockdown period. The time-series remote sensing images from November 2019 to April 2020 was used to examine the dynamics of the river's total suspended solids (TSS) concentrations in the period. A new remote sensing-based prototype was developed to recalibrate an existing algorithm for retrieving TSS concentrations in the river. The Nechad and the Novoa algorithms were used to validate the recalibrated algorithm. The results show that the recalibrated algorithm is highly consistent with the two algorithms. All of the three algorithms indicate significant fluctuation in TSS concentrations in the Lower Min River during the study period. February (COVID-19 lockdown period) has witnessed a 48% fall in TSS concentration. The TSS in March-April showed a progressive and recovery back to normal levels of pre-COVID-19. The spatiotemporal change of TSS has worked as a good indicator of human activities, which revealed that the decline of TSS in the lockdown period was due largely to the substantially-reduced discharges from industrial estates, densely-populated city center, and river's shipping. Remote sensing monitoring of the spatiotemporal changes of TSS helps understand important contributors to the water-quality changes in the river and the impacts of anthropogenic activities on river systems.

Comparison of In Situ and Remote-Sensing Methods to Determine Turbidity and Concentration of Suspended Matter in the Estuary Zone of the Mzymta River, Black Sea

The paper presents the results of a comparison of water turbidity and suspended particulate matter concentration (SPM) obtained from quasi-synchronous in situ and satellite remote-sensing data. Field measurements from a small boat were performed in April and May 2019, in the northeastern part of the Black Sea, in the mouth area of the Mzymta River. The measuring instruments and methods included a turbidity sensor mounted on a CTD (Conductivity, Temperature, Depth), probe, a portable turbidimeter, water sampling for further laboratory analysis and collecting meteorological information from boat and ground-based weather stations. Remote-sensing methods included turbidity and SPM estimation using the C2RCC (Case 2 Regional Coast Color) and Atmospheric correction for OLI ‘lite’ (ACOLITE) ACOLITE processors that were run on Landsat-8 Operational Land Imager (OLI) and Sentinel-2A/2B Multispectral Instrument (MSI) satellite data. The highest correlation between the satellite SPM and the water sampling SPM for the study area in conditions of spring flooding was achieved using C2RCC, but only for measurements undertaken almost synchronously with satellite imaging because of the high mobility of the Mzymta plume. Within the few hours when all the stations were completed, its boundary could shift considerably. The ACOLITE algorithms overestimated by 1.5 times the water sampling SPM in the low value range up to 15 g/m3. For SPM over 20–25 g/m3, a high correlation was observed both with the in situ measurements and the C2RCC results. It was demonstrated that quantitative turbidity and SPM values retrieved from Landsat-8 OLI and Sentinel-2A/2B MSI data can adequately reflect the real situation even using standard retrieval algorithms, not regional ones, provided the best suited algorithm is selected for the study region.