Comparison of Lake Optical Water Types Derived from Sentinel-2 and Sentinel-3

Abstract

Inland waters play a critical role in our drinking water supply. Additionally, they areimportant providers of food and recreation possibilities. Inland waters are known to be opticallycomplex and more diverse than marine or ocean waters. The optical properties of natural waters areinfluenced by three different and independent sources: phytoplankton, suspended matter, andcolored dissolved organic matter. Thus, the remote sensing of these waters is more challenging.Different types of waters need different approaches to obtain correct water quality products;therefore, the first step in remote sensing of lakes should be the classification of the water types. Theclassification of optical water types (OWTs) is based on the differences in the reflectance spectra ofthe lake water. This classification groups lake and coastal waters into five optical classes: Clear,Moderate, Turbid, Very Turbid, and Brown. We studied the OWTs in three different Latvian lakes:Burtnieks, Lubans, and Razna, and in a large Estonian lake, Lake Võrtsjärv. The primary goal of thisstudy was a comparison of two different Copernicus optical instrument data for opticalclassification in lakes: Ocean and Land Color Instrument (OLCI) on Sentinel-3 and MultispectralInstrument (MSI) on Sentinel-2. We found that both satellite OWT classifications in lakes werecomparable (R2 = 0.74). We were also able to study the spatial and temporal changes in the OWTs ofthe study lakes during 2017. The comparison between two satellites was carried out to understandif the classification of the OWTs with both satellites is compatible. Our results could give us not onlya better overview of the changes in the lake water by studying the temporal and spatial variabilityof the OWTs, but also possibly better retrieval of Level 2 satellite products when using OWT guidedapproach.

Validation and Comparison of Water Quality Products in Baltic Lakes Using Sentinel-2 MSI and Sentinel-3 OLCI Data

“…We used the C2RCC and C2X atmospheric correction processors for MSI, and C2RCC, for OLCI due to the results and recommendations of the previous studies [29,32,61]. The comparison of the in-situ and satellite sensors reflectance spectra for each lake revealed MSI C2X as the best performer in most cases.…”

“…OLCI C2RCC largest errors come with the false-estimation of the Clear OWT class (in 14% of the cases from all the cases). These spectra might have been affected by the in-water vegetation, however, due to of the insufficient quality flagging and IdePix tool performance on OLCI data, they are not dismissed and are used as clear water pixels [61]. Without this misclassification of Clear OWT, the "large OWT difference" would be only 8%, instead of 22% ( Figure 5).…”

“…Firstly, in the case of low concentrations, the specific spectral shape for different optical properties might not be obvious and the spectra might miss the maximum absorption peaks necessary for the further calculations. Secondly, the errors in the classification of the OWTs, as a substantial proportion of the cases were falsely classified to Clear OWT, which comes either from the atmospheric correction issues over lakes with C2RCC [29,61] or from the poor clear water pixel masking of the processors and IdePix tool (especially for the OLCI data) [61].…”

Classification of Australian Waterbodies across a Wide Range of Optical Water Types

“…However, the atmospheric correction protocol implemented on DEA satellite data is based on standard terrestrial aerosol climatology model parameters. Atmospheric correction over water bodies is challenging because waterbodies are dark, and about 90% of the signal received by the sensor is not caused by the water itself [86,87]. The large positive bias in the shape of the reflectance in the blue bands over water targets, which is introduced by this model [73], renders the Landsat data unsuitable for the purpose of this study.…”

“…Targets with a higher albedo were more often matched to an OWT, suggesting that some of the unclassified observations in the deep, clear lakes in the alpine regions [84] may be due to low target albedo. The water leaving signal at the satellite sensor is a small part of the total measured signal [86,88]; this potentially constrains the ability of the Sentinel-2 sensor to record a sufficient amount of water leaving light through a set of atmospheric and air water interface conditions to allow a clearly distinguishable spectral signature that can be matched to an existing OWT.…”