Development of a new spatially complete and daily continuous lake surface water temperature dataset for Lake Vänern, Sweden

Lake surface water temperature (LSWT) is a physical property of lakes. LSWT is a critical parameter for evaluating lakes' water quality and biodiversity. The change in LSWT can also be an indicator of climate change. Therefore, it is crucial to monitor LSWT to improve our understanding of the spatiotemporal dynamics of LSWT for many applications. Conventionally and ideally, we can install in-situ gauge stations or monitoring sites to measure surface water temperature in lakes, and these in-situ measurements are generally the most accurate. However, in-situ measurements in lakes are often sparse and limited in terms of spatial coverage and temporal length, which leaves many lakes with no measurements or lacking long-term continuous measurements. For example, Lake Vänern (surface area of about 5,655 km², the largest lake in the European Union) has only two operational stations for measuring LSWT. The existing in-situ measurements are at irregular intervals (approximately bi-weekly) and have many data gaps. Many lakes globally have the same data situation as Lake Vänern. As a result, in-situ measurements cannot sufficiently capture the spatiotemporal dynamics of LSWT in large lakes.

Satellite remote sensing has emerged as an essential method to monitor LSWT. Thermal infrared satellite data have been widely used to estimate the surface temperature at relatively high spatial resolution (higher and up to 1 km resolution). One of the most used satellite products for surface temperature is the MODIS (Moderate Resolution Imaging Spectroradiometer) global land surface temperature product, which is available from 2000 at 1 km-daily spatial-temporal resolutions. However, many studies stressed that cloud influence could significantly degrade the quality and availability of satellite-derived surface temperature for certain lakes, calling for a dedicated investigation to address this issue. Besides MODIS data, there are many other satellite-derived LSWT products at different spatial-temporal resolutions and spatial coverage, e.g., the ones available at http://www.laketemp.net. In addition, the recent ERA5-Land, a state-of-the-art reanalysis dataset, can also provide spatially complete and temporally continuous land surface variables, including the lake temperature at 0.1 degree-hourly spatial-temporal resolutions from 1950 to present. Each of the aforementioned products has its own advantages and limitations.

Our initial analysis showed a significant data gap in LSWT from MODIS product for Lake Vänern due to cloud influence, which motivates us to conduct this study. This study aims to evaluate multiple existing LSWT products and, more importantly, to combine them with the advanced data fusion and bias correction method to develop a new spatially complete and temporally continuous LSWT dataset for Lake Vänern, Sweden. New in-situ measurements of LSWT will be collected from boats and drones at many locations of Lake Vänern; such measurements, together with existing data from the two stations, will be used to evaluate multiple LSWT products, the developed method, and the merged dataset. The newly developed LSWT dataset for Lake Vänern will benefit many applications, such as lake evaporation estimation, water balance analysis, air-lake interactions, and local climate forecasting.