## 5 Current Status and Limitations of Data Interoperability with Landsat and Sentinel
### Aquatic Applications: Potentials and Limitations  
Within the next decade, the Landsat-8 and Sentinel-2 virtual constellation will be the primary source of well-monitored Earth-observing datasets for monitoring water resources, including lakes, reservoirs, rivers, bays, and other nearshore coastal areas. Because of the improvements in the sensor technology, the image data from this constellation was anticipated to outperform heritage missions like Landsat-5 and -7 [53; 54; 55] or the SPOT. The primary advantages come from (a) the improved SNR and (b) the additional spectral bands at -443 nm and within the NIR region. The high data quality together with 2.9-day revisit time of the constellation revolutionizes the way satellite data are utilized for science algorithm development and/or monitoring applications in inland and nearshore coastal  
Figure 13: Illustrative time series of Landsat 8 OLI (filled circles) and Sentinel-2A MSI (open circles) atmospherically corrected nadir BRDF-adjusted reflectance (NBAR) for a single 30 m woody savanna pixel location in the 2016 dry season over Zambia (74 days). The open circles without and with an intersecting line show the MSI surface NBAR without and with respectively application of a spectral linear transformation used to adjust the MSI to OLI [52]. The location burned after day 208 and on or before day 214.  
waters. For either purpose, consistency in both TOA and atmospherically corrected products, i.e., remote-sensing reflectance (\(R_{rs}\)), is critical. The remote-sensing reflectance, defined as the ratio of water-leaving radiance to total downwelling irradiance just above water [56], plays a major role in enabling quantification of water constituents, including the concentrations of total suspended solids (TSS) and chlorophyll-a (Chl), the absorption by colored dissolved organic matter (CDOM), and other products like turbidity, clarity, or Secchi disk depth. From the ocean color literature, it is well-known that a 1% calibration offset in the blue bands yields a 10% error in \(R_{rs}\) over blue ocean waters [57; 58]. In optically complex inland/nearshore waters, a 1% calibration offset may translate to larger errors in \(R_{rs}\) (\(\lambda<\) 500 nm) in CDOM-rich waters in the blue bands. It is also possible that the uncertainties may be smaller in extremely eutrophic/turbid waters in \(R_{rs}\) (\(\lambda>\) 600 nm) than those over clear ocean waters in this spectral region. The sensitivity of algorithms to uncertainties in \(R_{rs}\) also determines the required accuracies/precision in TOA measurements, i.e., sensitivity to calibration performance may differ for different algorithms. For example, band ratio algorithms [59; 60] tend to be less susceptible to uncertainties in \(R_{rs}\). An example of a time-series chlorophyll-a product over an arbitrary location (marked by a triangle) in San Francisco Bay is illustrated in Figure 14. The products are derived from Landsat-8, Sentinel-2A, and Sentinel-2B using a blue-green band ratio. Note that while Landsat-8 and Sentinel-2A have been vicariously calibrated, Sentinel-2B data are processed \"as is\". For monitoring applications where anomalies in water quality conditions are sought, such uncertainties are of less importance. However, for science algorithm developments and the associated time-series applications, uncertainties in \(R_{rs}\) require major attention. Furthermore, to ensure various global and region-specific algorithms transfer minimal uncertainties to products like Chl or TSS, it is logical to maintain high-quality TOA observations with minimal calibration errors or instrument artifacts. Last, but not least, to enable robust monitoring of water quality indicators, it is essential to provide end-users with per-pixel uncertainty estimates. This goes beyond existing quality assurance (QA) flags and allows for providing a confidence level to each pixel enabling effective decision-making.