Evaluating Landsat-8 and Sentinel-2 Data Consistency for High Spatiotemporal Inland and Coastal Water Quality Monitoring

Abstract

The synergy of fine-to-moderate-resolutin (i.e., 10–60 m) satellite data of the Landsat-8 Operational Land Imager (OLI) and the Sentinel-2 Multispectral Instrument (MSI) provides a possibility to monitor the dynamics of sensitive aquatic systems. However, it is imperative to assess the spectral consistency of both sensors before developing new algorithms for their combined use. This study evaluates spectral consistency between OLI and MSI-A/B, mainly in terms of the top-of-atmosphere reflectance (ρt), Rayleigh-corrected reflectance (ρrc), and remote-sensing reflectance (Rrs). To check the spectral consistency under various atmospheric and aquatic conditions, near-simultaneous same-day overpass images of OLI and MSI-A/B were selected over diverse coastal and inland areas across Mainland China and Hong Kong. The results showed that spectral data obtained from OLI and MSI-A/B were consistent. The difference in the mean absolute percentage error (MAPE) of the OLI and MSI-A products was ~8% in ρt and ~10% in both ρrc and Rrs for all the matching bands, whereas the MAPE for OLI and MSI-B was ~3.7% in ρt, ~5.7% in ρrc, and ~7.5% in Rrs for all visible bands except the ultra-blue band. Overall, the green band was the most consistent, with the lowest MAPE of ≤ 4.6% in all the products. The linear regression model suggested that product difference decreased significantly after band adjustment with the highest reduction rate in Rrs (NIR band) and Rrs (red band) for the OLI–MSI-A and OLI–MSI-B comparison, respectively. Further, this study discussed the combined use of OLI and MSI-A/B data for (i) time series of the total suspended solid concentrations (TSS) over coastal and inland waters; (ii) floating algae area comparison; and (iii) tracking changes in coastal floating algae (FA). Time series analysis of the TSS showed that seasonal variation was well-captured by the combined use of sensors. The analysis of the floating algae bloom area revealed that the algae area was consistent, however, the difference increases as the time difference between the same-day overpasses increases. Furthermore, tracking changes in coastal FA over two months showed that thin algal slicks (width < 500 m) can be detected with an adequate spatial resolution of the OLI and the MSI.

Satellite sensors as an emerging technique for monitoring macro- and microplastics in aquatic ecosystems

“…Such a bias may result in knowledge gaps and inconsistencies; the causes of this might be because of funding deficiencies and/or technological gaps in areas with limited reporting. This finding is corroborated by research carried out by Evans and Ruf (2021), who highlighted the fact that there is plastic under-sampling in the global south [24] . The global distribution pattern of plastic accumulation shows higher levels deposited in the North Pacific and the South Atlantic Gyres, as well as the Mediterranean Seas [57][58][59] .…”

“…As a result, the scope and focus of this study are to review the literature on using satellites to monitor macro-, meso-, micro-, and nanoplastics in water. Furthermore, hyperspectral, thermal imaging, and multispectral sensors have been successfully applied to monitoring macroplastic debris [15,16,29,[35][36][37][38][39][40][41][42] , and researchers recently discovered that SAR can be used for monitoring microplastics, a research domain still in its infancy [24] . The significance of monitoring global plastic regardless of size, occurrence, source, and location is emphasized by many researchers [42,50] .…”

“…Sentinel-2 MSI is the most frequently used satellite sensor for monitoring plastic debris in water [35][36][37][38]40,41,43,96,103,104,[107][108][109][110][111][112][113][114]118,119,121] , followed by WV [39,42,43,114,115,120,122,123] and SAR [24,107,116,118,121,124] used in 20, 9, and 6 studies, respectively [Figure 5]. Landsat-8 OLI is in fourth place, tied with PRecursore IperSpettrale della Missione Applicativa (PRISMA), which have both been used twice [41,116,125,126] , while the remaining satellites were only used once [41,94,121,123,124] .…”

“…Most aquatic plastic pollution monitoring data are obtained through numerical modeling and in situ sampling laboratory-based methods (e.g., micro Fourier transform infrared (μ-FTIR) spectroscopy, attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy, μ-Raman imaging microscope, microscope, energy-dispersive X-ray spectroscopy (EDS), and scanning electron microscope (SEM)) [21,22] . Numerical modeling [23] has the advantage of high spatial coverage, although its drawbacks include low sampling frequency and design faults that may result in under-or overestimation of the true values of plastic concentrations [24] . Traditional laboratory approaches are precise and accurate; however, they are constrained by manual sampling, which is laborious, time-consuming, and costly [25] .…”

“…Satellite remote sensing for the detection of plastic litter in water is still at the research and development stage, albeit there is huge promising potential to offset the drawbacks highlighted for the other methods [29] . Satellite sensors, including optical, synthetic aperture radar (SAR), hyperspectral, and thermal infrared (TIR) sensors, can either monitor plastics directly or simulate plastic concentrations based on proxy measurements [24] . Satellite observations provide high spatial resolution and long time series data that fill in missing values in all pixels, thus bridging data gaps between sparse sampling points and providing a uniform survey [30][31][32] .…”