# Investigating the Impact of Wildfires on Lake Water Quality Using Earth Observation Satellites
## 2 Materials and Methods  
### Datasets  
In this study, from the different variables from the Lakes_cci project [32], we considered Chl-a and turbidity as indicators of water quality for the period from 2017 to 2020 built from OLCI-Sentinel-3 A/B data. In addition, both the Lake Ice Cover (LIC) and Lake Surface Water Temperature (LSWT) variables were considered; the first to focus our analysis on ice-cover free periods, the second as an ancillary observation to evaluate water quality changes. We used the FireCCI51 Burned Area (BA) product, which shows the global spatio-temporal distribution of BA derived from MODIS satellite observations [31]. Burned area maps are produced using a hybrid algorithm that blends active fire derived from thermal channels with the highest resolution near-infrared (NIR) surface reflectance (-250 m) [33]. The product comprises monthly global maps covering the period 2001-2020 and burned pixels are recorded with the estimated Julian day (DOY) of detection and the land cover category of the burned pixels, extracted from the Land Cover_cci v2.0.7 product [34]. Burned pixels have been extracted from the FireCCI51 BA product for every catchment connected to any of the lakes included in the Lakes_cci database for the considered period. The extraction was performed using the R package \"exactextract\" [34], allowing for the retrieval of the exact coverage fraction of a pixel on the basin polygons.  
Lake and catchment area data were obtained from the HydroLAKES and HydroBASINS datasets. The HydroLAKES database [35] was used to obtain the following attribute information for the 2024 lakes investigated: lake surface area (km\({}^{2}\)), length of shoreline (km), shoreline development (ratio shoreline length/circumference of a circle with the same area), total lake volume (million cubic meters), mean water depth (m), mean long-term discharge flowing through a lake (m\({}^{3}\) s\({}^{-1}\)), mean water residence time (days), elevation of lake surface (m a.s.l.), lake latitude and longitude, area of the catchment associated with the lake (km\({}^{2}\)). Every lake is co-registered to a sub-catchment of the HydroBASINS database (via shared IDs). We obtained the hydrologic areas of the 2024 lakes from the HydroBASINS database [36] which provides vectorized polygon layers that show sub-basin boundaries at a global scale. Using the HydroBASINS dataset, sub-basins connected to each lake have been selected. The database allows us to navigate sub-basins upstream from the lake, and all the sub-basins connected within 50 steps (levels 0-50) from a lake have been assigned accordingly with this approach; the presence of a lake in the connection chain causes the interruption of the navigation. The basins within four steps (levels 0-4) represent the nearest to the lake basin, and are thus considered the closest catchment areas around a lake in our analysis.  
Land cover data at lake catchment level was extracted from the Copernicus Climate Change Service [37] for the period 2017-2020.  
We calculated the Standardized Precipitation Index (SPI; [38]) for each lake for the period 1980-2019 using ERA5 data of total precipitation for each lake watershed (sum of hourly values to obtain daily data) with the reference period set to 1980-2010. The SPI is a statistical indicator that compares the total (median) precipitation falling at a given location during a given period (12 months for this work) with the distribution of long-term precipitation for the same period. Negative index values identify moderately to extremely dry conditions, while positive values identify very to moderately wet conditions; values in the range [-1, 1] identify close to normal conditions with respect to the long-term trend. The Standardized Precipitation Index (SPI) mean, minimum, and maximum were calculated for each year and for the whole considered period (2017-2020).