A Combined Approach for Monitoring Monthly Surface Water/Ice Dynamics of Lesser Slave Lake Via Earth Observation Data

Surface water/ice dynamic monitoring is crucial for many purposes, such as water resource management, agriculture, climate change, drought, and flood forecasting. New advances in remote sensing satellite data have made it possible to monitor the surface water/ice dynamics both spatially and temporally. However, there are many challenges when using these data, such as the availability of valid imagery, cloud contamination issues for Landsat-8, and sensitivity of Sentinel-1 C-band to wind speed, topography, and others. A combined methodology using Landsat-8 and Sentinel-1 synthetic aperture radar (SAR) data was proposed to create monthly change maps at 30-m spatial resolution for the Lesser Slave Lake in Alberta, Canada, for the period 2017-2020. The potentials of multispectral indices for Landsat-8, such as the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and modified NDWI (MNDWI) as well as the Sentinel-1 SAR backscattering coefficients (VV-VH) and normalized difference polarized index (NDPI) for separating water/ice from the land were investigated. The results obtained from satellite data with historical discharge and water level measurements for the lake were compared. Furthermore, the results show that the MNDWI and VH are the most effective indices for creating the change maps. The overall accuracies achieved for MNDWI and VH are 92.10% and 68.86% for cold months and 99.88% and 98.49% for warm months, respectively.

Automated summary

Surface water/ice dynamic monitoring is crucial for various purposes and remote sensing satellite data have made it possible to monitor such dynamics. This study proposes a combined approach using Landsat-8 and Sentinel-1 data to create monthly change maps and investigate the separation of water/ice from land. The results show that the indices MNDWI and VH are the most effective for creating the change maps.