Performance and Applicability of Water Column Correction Models in Optically Complex Coastal Waters

Abstract

Maps of submerged aquatic vegetation (SAV) are of primary importance for the sustainable management of coastal areas and serve as a basis for fundamental ecological studies. Various water column correction (WCC) models are successfully applied in clear Case-1 waters to compensate for the variable water depth effect. The performance of the WCC in less clear Case-2 waters is rarely assessed. In this study, the performance and applicability of model-based WCC algorithms in the complex Baltic Sea were analyzed. The bottom reflectance was retrieved from the Compact Airborne Spectrographic Imager (CASI) water surface reflectance by applying the Maritorena and Lee WCC algorithms. The Maritorena model retrieved bottom spectra that showed large variations in reflectance magnitudes. The Lee model was more successful in retrieving reasonable spectral magnitudes, although only in a rather narrow wavelength region (550–600 nm). Shorter and longer spectral regions were significantly overcorrected, resulting in unrealistic spectral shapes. Sensitivity analysis indicated that slight under- or overestimation of water depth and water column constituents affect retrieval of correct bottom spectra in Case-2 waters. To assess the performance of WCC models in improving the SAV quantification, the surface reflectance, as well as the retrieved bottom reflectance, were correlated with the corresponding in situ estimated SAV percent cover (%SAV). Although the quality of the Lee WCC model was not considered high, the spectral region least affected by the input parameters variations (550–600 nm) can be used for the SAV quantification. Application of the Lee model provided better results in %SAV assessment than not performing the WCC correction.

On the possibility to map submerged aquatic vegetation cover with Sentinel-2 in low-transparency waters

“…Several researchers have found that the removal of the water column influence can improve the accuracy of SAV classification, as well as abundance quantification. 44,55 However, the application of WCC may also have an opposite effect, both in clear and turbid environments. For example, Marcello et al 56 and Mederos-Barrera et al 57 concluded that WCC did not improve the benthic classification results.…”

“…43 Detailed description of Maritorena WCC processing is provided by Vahtmäe et al. 44 In August, we had additional measurements of a and b b from ground-truth stations. The availability of a and b b allowed one to apply Lee WCC model 41 to August S2 imagery.…”

“…Our previous studies have shown that the Lee WCC model is more successful in retrieving reasonable R b values, especially in the green spectral range (550 to 600 nm). 44 Therefore, it was decided to apply the Lee model to August imagery for R b retrieval. Detailed description of Lee WCC processing is provided by Vahtmäe et al 24 and Vahtmäe et al 44 Analytical WCC models assume homogeneous water properties.…”

“…44 Therefore, it was decided to apply the Lee model to August imagery for R b retrieval. Detailed description of Lee WCC processing is provided by Vahtmäe et al 24 and Vahtmäe et al 44 Analytical WCC models assume homogeneous water properties. Although the water quality was highly variable across entire bay, the variability was somewhat lower in our focus area.…”

Accurate Monitoring of Submerged Aquatic Vegetation in a Macrophytic Lake Using Time-Series Sentinel-2 Images

“…Similar to other commonly used SAV extraction algorithms, our method cannot extract SAV in areas with large canopy depths and turbid water bodies throughout the year. Due to the high absorption of water molecules in the near-infrared band, SAV with a large canopy depth cannot be detected [30,31]. The water level in Baiyangdian Lake fluctuates greatly.…”