Diurnal Patterns in Solute Concentrations Measured with In Situ UVVis Sensors: Natural Fluctuations or Artefacts? In situ spectrophotometers measuring in the UVvisible spectrum are increasingly used to collect highresolution data on stream water quality. This provides the opportunity to investigate shortterm solute dynamics, including diurnal cycling. This study reports unusual changes in diurnal patterns observed when such sensors were deployed in four tropical headwater streams in Kenya. The analysis of a 5year dataset revealed sensorspecific diurnal patterns in nitrate and dissolved organic carbon concentrations and different patterns measured by different sensors when installed at the same site. To verify these patterns, a second mobile sensor was installed at three sites for more than 3 weeks. Agreement between the measurements performed by these sensors was higher for dissolved organic carbon (r u003e 0.98) than for nitrate (r  0.43xe2x80x930.81) at all sites. Higher concentrations and larger amplitudes generally led to higher agreement between patterns measured by the two sensors. However, changing the position or level of shading of the mobile sensor resulted in inconsistent changes in the patterns. The results of this study show that diurnal patterns measured with UVVis spectrophotometers should be interpreted with caution. Further work is required to understand how these measurements are influenced by environmental conditions and sensorspecific properties.

Validation and Comparison of Water Quality Products in Baltic Lakes Using Sentinel2 MSI and Sentinel3 OLCI Data Inland waters, including lakes, are one of the key points of the carbon cycle. Using remote sensing data in lake monitoring has advantages in both temporal and spatial coverage over traditional insitu methods that are time consuming and expensive. In this study, we compared two sensors on different Copernicus satellites: Multispectral Instrument (MSI) on Sentinel2 and Ocean and Land Color Instrument (OLCI) on Sentinel3 to validate several processors and methods to derive water quality products with best performing atmospheric correction processor applied. For validation we used insitu data from 49 sampling points across four different lakes, collected during 2018. Level2 optical water quality products, such as chlorophylla and the total suspended matter concentrations, water transparency, and the absorption coefficient of the colored dissolved organic matter were compared against insitu data. Along with the water quality products, the optical water types were obtained, because in lakes onemethodtoall approach is not working well due to the optical complexity of the inland waters. The dynamics of the optical water types of the two sensors were generally in agreement. In most cases, the band ratio algorithms for both sensors with optical water type guidance gave the best results. The best algorithms to obtain the Level2 water quality products were different for MSI and OLCI. MSI always outperformed OLCI, with R2 0.84xe2x80x930.97 for different water quality products. Deriving the water quality parameters with optical water type classification should be the first step in estimating the ecological status of the lakes with remote sensing.