Leveraging Landsat-8/-9 underfly observations to evaluate consistency in reflectance products over aquatic environments

With an identical design and build, the Operational Land Imager-2 (OLI2) aboard Landsat-9 (L9) complements OLI observations by reducing the global revisit rate of Landsat to 8 days. This study takes advantage of near-coincident OLI2 and OLI observations obtained on 11–17 November 2021 to assess the relative quality of the standard United States Geological Survey (USGS) top-of-atmosphere (TOA) reflectance (ρt) and atmospherically corrected reflectance (aquatic reflectance; ρwAR) products over bodies of water. The TOA assessment was carried out for all the visible bands, including the panchromatic band, as well as the near-infrared (NIR) and shortwave infrared (SWIR) bands, whereas ρwAR products were analyzed in the 443, 482, 561, and 655 nm bands. The overlapping areas of OLI-OLI2 ρw product pairs were further analyzed for the rigor in corrections for the viewing geometry implemented in selected atmospheric correction (AC) processors, including SeaDAS, ACOLITE, and POLYMER, with products denoted as ρwAR, ρwac, and ρwpol, respectively. Overall, we found the OLI2-OLI ρt products to be consistent within 0.4% in the visible-near-infrared (VNIR) bands except in the green band (561 nm), where OLI2 records ∼0.8% larger values than OLI. The two SWIR bands (1610 and 2200 nm) were also found to agree within ∼2.2% and 2.1%, respectively, with OLI2 being lower in magnitude. The median differences in the standard ρw (ρwAR) were estimated to be ∼2.4%, 1.5%, 2.3%, and 2.5% in the 443, 482, 561, and 655 nm bands, respectively, which are well within the accepted differences in cross-mission data merging schemes. Further, we show that OLI2's signal-to-noise ratio (SNR) is 7–30% higher than that of OLI in all the bands, likely due to its 14-bit digitization rate, as compared to OLI's 12-bit digitization rate. Our self-consistency assessment of the AC processors for handling the differences in the view zenith angles (ΔVZA) and relative azimuth angles (ΔRAA) of OLI2 and OLI observations suggests that, overall, the processors account for the Sun-sensor geometry differently at different spectral bands. These differences (inconsistencies) amount to average median absolute percentage differences (MAPD) in ρw from 0.3 to 5.3% (e.g., ∆ρwAR443nmΔVZAΔRAA=ρwAR,OLI443+6.310−ρwAR,OLI2443+3.9−13<0.5%).More specifically, we found that SeaDAS provides the most optimal corrections for the angular variability as a function of VZA, ρwac are most consistent for different ranges of RAAs, and POLYMER performs best in the 443 nm band. We surmise that future (and other existing) AC methods should be tested with Landsat-8/-9 underfly imagery to quantify their performance for tackling the variability in imaging geometry and minimize associated uncertainties. With several missions planned for launch by the end of this decade, it is further emphasized that post-launch tandem maneuvers are essential to creating harmonized multi-mission data products and, therefore, similar operations should be considered and extended to ensure a broad range of environmental conditions are captured for comprehensive cross-mission analyses.