A CROSS-SENSOR-BASED APPROACH TO ESTIMATE DEPTH VALUES IN NEARSHORE COASTAL WATERS, CASE STUDY: NAYBAND BAY, PERSIAN GULF

Abstract. A cross-sensor-based approach using Landsat-8 OLI (L8/OLI) and Sentinel-2A MSI (S2A/MSI) imagers was examined to estimate bathymetric data in nearshore coastal waters. An L8/OLI image and an S2A/MSI image (Acquisition date: November 16, 2017) were selected from Nayband Bay, the southern region of Iran. In addition, precise bathymetric data for the studied area were used to calibrate the models and validate the results. Ratio together with traditional linear transform methods and a novel cross-sensor-based method were conducted to determine the depth values from both satellite images. Four bands of L8/OLI imager (Band No.1: Coastal/Aerosol [0.435–0.451 µm], Band No. 2: blue [0.452–0.512 µm], Band No. 3: green [0.533–0.590 µm], and Band No. 4: red [0.636–0.673 µm], spatial resolution: 30 m) were considered to create the aforementioned models while the three bands of S2A/ MSI imager were used (Band No. 2: blue [0.458–0.523 µm], Band No. 3: green [0.543-0.578 µm], and Band No. 4: red [0.650–0.680 µm], spatial resolution: 10 m). All models' accuracy was evaluated using comparing the calculated bathymetric information with field observed values. The statistical indicators including correlation coefficients (R²), root mean square errors (RMSE), and standard errors (SE) for validation points were computed for all models of two imagers. The final results demonstrated that although the spatial resolution of L8/OLI imagery is less than S2A/MSI, the precision of estimated depth is higher due to having more bands in the visible wavelength range. However, the integrated cross-sensor-based method including the bands of both sensors yielded the most accurate results (R² = 0.90, RMSE = 1.66 m, and SE = 1.29 m).