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On June 19, 2025 at 2:58:30 PM UTC, Gravatar Caterina Gilli:

  • Updated description of Thermal and Ecological spatiotemporal trends to assess extreme heat hazard in Euskadi from

    Land surface temperature (LST) is obtained from Landsat images using the widely used radiative transfer equation. The thermal and ecological conditions are evaluated by computing urban heat island (UHI) and urban thermal field variance index (UTFVI) from LST data. The influence of vegetation, built area, presence of waterbody, and bare soil on LST is examined using land cover indices through pixel-level multivariate linear regression analysis. (Ahmmed et al.,2021). Landsurface temperature (LST) is frequently used as an indicator for UHI and shows a positive correlation with the density of sealed surfaces while displaying a negative association with UGS (Aznarez et al., 2024; Rodríguez-Gómez et al., 2022). LST is used to quantify the extent and size of surface heat. LST is an integral variable in quantifying thermal hazard levels across cityscapes. Local topography, human activity, and specific urban heat island effects influence cities' dynamics and green spaces. ## Datasets generated for Euskadi ###Mean Land Surface Temperature and Normalized Difference Vegetation Index The LST data generation process was commenced by adapting a NASA-ARSET (2022) open-source code in Google Earth Engine (GEE). The initial script provided a foundational approach to retrieving daytime LST spatial data at 30 m pixels for the entire Eukadi region. Building upon this, the methodology was refined, incorporating robust techniques and additional parameters based on Rahman et al. (2021), enhancing the accuracy and applicability of the analysis to the Euskadi region. The LST data were obtained from Landsat 8 level 2 Surface Reflectance (SR) and Surface Temperature (ST) imagery (Collection 2 Tier 1), covering the hottest months (June, July, August, September) (Aznarez et al., 2024; Marquez-Torres et al., 2025) from 2020-2024. To ensure reliability, images considering minimal cloud cover (<10%) were selected (Ahmmed et al.,2021) and cloud/shadow pixels were masked using the QA_PIXEL band (Rahman et al.,2021; NASA-ARSET, 2022). The spatial context was defined using OpenStreetMap (OSM) within the administrative boundary of Euskadi. Vegetation-based emissivity correction was applied to improve LST estimation, following methods adapted from Sobrino et al. (2004) and Rahman et al. (2021). The Normalized Difference Vegetation Index (NDVI) was first calculated using the red (SR_B4) and near-infrared (SR_B5) bands from Landsat for the summer period (June 1st - September 30th) of 2020–2024 at 30 m of spatial resolution. NDVI is calculated as follows: In Landsat 8-9, NDVI = (Band 5—Band 4) / (Band 5 + Band 4). ``` $$ \displaystyle\sum_{k=3}^5 k^2=3^2 + 4^2 + 5^2 =50 $$ ``` ``` <mfrac>a/b <\mfrac> NDVI = \frac{NIR-RED}{NIR + RED}$$ ```
    to
    Land surface temperature (LST) is obtained from Landsat images using the widely used radiative transfer equation. The thermal and ecological conditions are evaluated by computing urban heat island (UHI) and urban thermal field variance index (UTFVI) from LST data. The influence of vegetation, built area, presence of waterbody, and bare soil on LST is examined using land cover indices through pixel-level multivariate linear regression analysis. (Ahmmed et al.,2021). Landsurface temperature (LST) is frequently used as an indicator for UHI and shows a positive correlation with the density of sealed surfaces while displaying a negative association with UGS (Aznarez et al., 2024; Rodríguez-Gómez et al., 2022). LST is used to quantify the extent and size of surface heat. LST is an integral variable in quantifying thermal hazard levels across cityscapes. Local topography, human activity, and specific urban heat island effects influence cities' dynamics and green spaces. ## Datasets generated for Euskadi ###Mean Land Surface Temperature and Normalized Difference Vegetation Index The LST data generation process was commenced by adapting a NASA-ARSET (2022) open-source code in Google Earth Engine (GEE). The initial script provided a foundational approach to retrieving daytime LST spatial data at 30 m pixels for the entire Eukadi region. Building upon this, the methodology was refined, incorporating robust techniques and additional parameters based on Rahman et al. (2021), enhancing the accuracy and applicability of the analysis to the Euskadi region. The LST data were obtained from Landsat 8 level 2 Surface Reflectance (SR) and Surface Temperature (ST) imagery (Collection 2 Tier 1), covering the hottest months (June, July, August, September) (Aznarez et al., 2024; Marquez-Torres et al., 2025) from 2020-2024. To ensure reliability, images considering minimal cloud cover (<10%) were selected (Ahmmed et al.,2021) and cloud/shadow pixels were masked using the QA_PIXEL band (Rahman et al.,2021; NASA-ARSET, 2022). The spatial context was defined using OpenStreetMap (OSM) within the administrative boundary of Euskadi. Vegetation-based emissivity correction was applied to improve LST estimation, following methods adapted from Sobrino et al. (2004) and Rahman et al. (2021). The Normalized Difference Vegetation Index (NDVI) was first calculated using the red (SR_B4) and near-infrared (SR_B5) bands from Landsat for the summer period (June 1st - September 30th) of 2020–2024 at 30 m of spatial resolution. NDVI is calculated as follows: In Landsat 8-9, NDVI = (Band 5—Band 4) / (Band 5 + Band 4). ``` $$ \displaystyle\sum_{k=3}^5 k^2=3^2 + 4^2 + 5^2 =50 $$ ``` ``` $$ NDVI = \frac{NIR-RED}{NIR + RED} $$ ```