First Experiences in Mapping Lake Water Quality Parameters with Sentinel-2 MSI Imagery

By K. Toming, T. Kutser, Alo Laas, Margot Sepp, B. Paavel, T. Nõges. Remote Sensing, 2016

503 citations recorded by the source corpus.

The importance of lakes and reservoirs leads to the high need for monitoring lake water quality both at local and global scales. The aim of the study was to test suitability of Sentinel-2 Multispectral Imager’s (MSI) data for mapping different lake water quality parameters. In situ data of chlorophyll a (Chl a), water color, colored dissolved organic matter (CDOM) and dissolved organic carbon (DOC) from nine small and two large lakes were compared with band ratio algorithms derived from Sentinel-2 Level-1C and atmospherically corrected (Sen2cor) Level-2A images. The height of the 705 nm peak was used for estimating Chl a. The suitability of the commonly used green to red band ratio was tested for estimating the CDOM, DOC and water color. Concurrent reflectance measurements were not available. Therefore, we were not able to validate the performance of Sen2cor atmospheric correction available in the Sentinel-2 Toolbox. The shape and magnitude of water reflectance were consistent with our field measurements from previous years. However, the atmospheric correction reduced the correlation between the band ratio algorithms and water quality parameters indicating the need in better atmospheric correction. We were able to show that there is good correlation between band ratio algorithms calculated from Sentinel-2 MSI data and lake water parameters like Chl a (R2 = 0.83), CDOM (R2 = 0.72) and DOC (R2 = 0.92) concentrations as well as water color (R2 = 0.52). The in situ dataset was limited in number, but covered a reasonably wide range of optical water properties. These preliminary results allow us to assume that Sentinel-2 will be a valuable tool for lake monitoring and research, especially taking into account that the data will be available routinely for many years, the imagery will be frequent, and free of charge.

Subfields: Industrial and Manufacturing Engineering, Media Technology, Oceanography

View Paper: https://www.semanticscholar.org/paper/f2c3742d0ff2057f26321e6407536a179375591e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Media Technology

In Oceanography

Ronghua Ma

Citations in the corpus

Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach (2020, background): “…and Sentinel-3 with sensors capable of quantifying in-water optical properties have spurred the science community to strive to devise improved methods for retrievals of Chl a and other water constituents (Ansper and Alikas, 2019; Cazzaniga et al., 2019; Gernez et al., 2017; Toming et al., 2016).”
Super-Resolution of Sentinel-2 Images: Learning a Globally Applicable Deep Neural Network (2018, background): “Paul et al. (2016) map the extent of glaciers, while Toming et al. (2016) map lake water quality.”
Copernicus Sentinel-2A Calibration and Products Validation Status (2017, background): “Another study could not rely on reflectance measurements on ground in parallel to Sentinel-2 overpasses [53].”