Estimating water surface elevation for a wetland using integrated multi-sourced remote sensing data

Abstract Surface water plays an important role in understanding the hydrological behaviour of a wetland and is crucial for the sustainability of wetland ecosystems. Remote sensing increasingly is used for the estimation of surface water levels in larger inland waterbodies. However, there are few investigations that have employed multi-sourced remote sensing data for water level predictions in wetlands, which was the motivation for undertaking this study. Sentinel-2 and Landsat-8 are among the latest satellites providing optical imagery with high spatial resolution and coverage that are available in the public domain. Different water indices have been applied to estimate surface water levels using these satellite image sources; however, what index to use for a particular application requires thorough, site-specific analysis. In this study, the Normalized Difference Water Index (NDWI), two versions of the Modified Normalized Difference Water Index (MNDWI), and the Water Ratio Index (WRI) were used to estimate water levels in a constructed wetland, as part of an effort to better guide regulation and decision-making for a local management agency. The satellite data were complemented with high resolution aerial photogrammetric images and LiDAR data to assess the accuracy of water level predictions provided by the satellite images. The photogrammetric images were used as reference datasets while the LiDAR data supported the development of area-elevation curves for the wetland. Accuracy assessment between the satellite and reference images was performed using the Kappa co-efficient (K). MNDWI performed better than the other water indices for both satellite data sources; however, the optimum threshold was different for each satellite (− 0.35 for Sentinel-2 and − 0.25 for Landsat-8). K values for the optimum threshold ranged between 0.72 and 0.77 for Sentinel-2 and 0.73 and 0.87 for Landsat-8. The water levels estimated using the remotely sensed data were assessed against in situ, continuously measured water levels using multiple efficiency evaluation metrics including R 2 , RMSE, and SSE. Estimated water levels with Sentinel-2 and Landsat-8 resulted in an R 2 of 0.86 and 0.88, RMSE of 0.04 m and 0.06 m, and an SSE of 0.02 m and 0.06 m, respectively. These results show that even for a small wetland, it is possible to use satellite imagery to estimate water levels with high accuracy.