Water Quality Retrieval from Landsat-9 (OLI-2) Imagery and Comparison to Sentinel-2

Abstract

The Landsat series has marked the history of Earth observation by performing the longest continuous imaging program from space. The recent Landsat-9 carrying Operational Land Imager 2 (OLI-2) captures a higher dynamic range than sensors aboard Landsat-8 or Sentinel-2 (14-bit vs. 12-bit) that can potentially push forward the frontiers of aquatic remote sensing. This potential stems from the enhanced radiometric resolution of OLI-2, providing higher sensitivity over water bodies that are usually low-reflective. This study performs an initial assessment on retrieving water qual-ity parameters from Landsat-9 imagery based on both physics-based and machine learning mod-eling. The concentration of chlorophyll-a (Chl-a) and total suspended matter (TSM) are retrieved based on physics-based inversion in four Italian lakes encompassing oligo to eutrophic conditions. A neural network-based regression model is also employed to derive Chl-a concentration in San Francisco Bay. We perform a consistency analysis between the constituents derived from Land-sat-9 and near-simultaneous Sentinel-2 imagery. The Chl-a and TSM retrievals are validated using in situ matchups. The results indicate relatively high consistency among the water quality prod-ucts derived from Landsat-9 and Sentinel-2. However, the Landsat-9 constituent maps show less grainy noise, and the matchup validation indicates relatively higher accuracies obtained from Landsat-9 (e.g., TSM R2 of 0.89) compared to Sentinel-2 (R2= 0.71). The improved constituent re-trieval from Landsat-9 can be attributed to the higher signal-to-noise (SNR) enabled by the wider dynamic range of OLI-2. We performed an image-based SNR estimation that confirms this as-sumption.