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

Abstract

<jats:p>Suspended Particulate Matter (SPM) is a major constituent in coastal waters, involved in processes such as light attenuation, pollutant propagation, and waterways blockage. The spatial distribution of SPM is an indicator of deposition and erosion patterns in estuaries and coastal zones and a necessary input to estimate the material fluxes from the land through rivers to the sea. In-situ methods to estimate SPM provide limited spatial data in comparison to the coverage that can be obtained remotely. Ocean color remote sensing complements field measurements by providing estimates of the spatial distributions of surface SPM concentration in natural waters, with high spatial and temporal resolution. Existing methods to obtain SPM from remote sensing vary between purely empirical ones to those that are based on radiative transfer theory together with empirical inputs regarding the optical properties of SPM. Most algorithms use a single satellite band that is switched to other bands for different ranges of turbidity. The necessity to switch bands is due to the saturation of reflectance as SPM concentration increases. Here we propose a multi-band approach for SPM retrievals that also provides an estimate of uncertainty, where the latter is based on both uncertainties in reflectance and in the assumed optical properties of SPM. The approach proposed is general and can be applied to any ocean color sensor or in-situ radiometer system with red and near-infra-red bands. We apply it to six globally distributed in-situ datasets of spectral water reflectance and SPM measurements over a wide range of SPM concentrations collected in estuaries and coastal environments (the focus regions of our study). Results show good performance for SPM retrieval at all ranges of concentration. As with all algorithms, better performance may be achieved by constraining empirical assumptions to specific environments. To demonstrate the flexibility of the algorithm we apply it to a remote sensing scene from an environment with highly variable sediment concentrations.</jats:p>

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

“…The strong fit between the Novoa and the Wen algorithms suggests that the switch of band and criteria of the Novoa algorithm allows the algorithm to be more flexible to different TSS levels since the algorithm integrates different bands and was tested with a wide TSS dataset (2.6–1579 mg/L). Balasubramanian et al, 2020 , Tavora et al, 2020 compared several TSS-estimate algorithms and both achieved good results for the Novoa algorithm.…”

“…The underestimation of Nechad (R) and Nechad (G) probably results from the relatively narrow TSS range of the in-situ data used for testing the algorithm (1.24–110.27 mg/L), which may have caused the saturation as TSS concentration rise. Tavora et al (2020) indicated that the reflectance in the visible spectral region could suffer from saturation in high TSS water conditions. In this Min River case, the Nechad (R) algorithm performed quite well in relatively low TSS concentration waters but not so well in high TSS waters.…”

“…Many remote sensing-based algorithms used to measure TSS from water reflectance have method/site-specific limitations ( Tavora et al, 2020 ). Dogliotti et al (2015) indicated that the existing algorithms faced a challenge, i.e., whether these algorithms could be applied to waters in other regions without calibration.…”

“…They suggested that SOLID had a potential for estimating TSS in global coastal and inland waters. Tavora et al (2020) indicated that the use of a single image band might lead to switching the band to another one for different ranges of turbidity because of the saturation of reflectance as TSS concentration varies. Therefore, they proposed a multi-band approach with red and NIR bands and applied it to different estuaries and coastal environments.…”

Global Estimation of Suspended Particulate Matter From Satellite Ocean Color Imagery

“…The estimated uncertainties for Han et al (2016) and Dogliotti et al (2015) are found to be much higher, especially in relatively clear waters. The Tavora et al (2020) algorithm is a semi-analytical approach; the relatively large uncertainties found here could be related to the default band setting considered (which only used the red and NIR bands) and the differences between the optical parameters used (e.g., the mass-specific absorption coefficients) and their true values. Both the Novoa et al (2017) andD'Sa et al (2007) algorithms are subject to larger uncertainties than those from the NIR-RGB algorithm.…”