Integrating Landsat-7 Imagery with Physics-based Models for Quantitative Mapping of Coastal Waters near River Discharges

Abstract

Remote sensing has traditionally been used to retrieve water constituents by establishing a relationship between in- situ measured quantities and image-derived products. Motivated by the dramatically improved potential of the Landsat Data Continuity Mission (LDCM), this paper describes a different approach for water constituent retrieval where both thermal and visible spectral bands of the Enhanced Thematic Mapper Plus (ETM+) instrument on board Landsat-7 are utilized. In this effort, Landsat data is integrated with a 3D hydrodynamic model to obtain profiles of particles and dissolved matter in the near shore zone in the vicinity of two river discharges. The procedure is based upon performing many hydrodynamic simulations by adjusting input environmental/physical variables and generating Look-Up-Tables (LUTs). This is conducted in two phases, namely the model calibration and the constituent retrieval. In the calibration phase, the best model output is determined by searching the LUT for the optimal surface temperature map compared to the Landsat-derived surface temperature map. The profiles of particles and dissolved matter are retrieved in the second step by comparing several modeled surface reflectance maps with atmospherically compensated Landsat-7 imagery. Various case scenarios of simulated water constituent profiles drive an in-water radiative transfer code, i.e. Hydrolight, which simulates water-leaving reflectance ( d r ). The best match, obtained via optimization, demonstrated an average root-mean-squared-error (RMSE) of 0.68%, i.e., 0.0068 reflectance units, calculated over the two river plumes. It is concluded that calibrating a physics-based model using the Landsat-7 imagery can provide a more lucid insight into the dynamics of spatially non-uniform waters. Ongoing efforts show that, due to its enhanced radiometric fidelity, the LDCM should significantly improve our proposed approach for the retrieval of water constituents.