Landsat 8 Collection 2 Tier 1 calibrated top-of-atmosphere (TOA) reflectance. Calibration coefficients are extracted from the image metadata. See Chander et al. (2009) for details on the TOA computation. Landsat scenes with the highest available data quality are placed into Tier 1 and are considered suitable for time-series processing analysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data that have well-characterized radiometry and are inter-calibrated across the different Landsat sensors. The georegistration of Tier 1 scenes will be consistent and within prescribed tolerances [<=12 m root mean square error (RMSE)]. All Tier 1 Landsat data can be considered consistent and inter-calibrated (regardless of sensor) across the full collection. See more information in the USGS docs. The T1_RT collection contains both Tier 1 and Real-Time (RT) assets. Newly-acquired Landsat 7 ETM+ and Landsat 8 OLI/TIRS data are processed upon downlink but use predicted ephemeris, initial bumper mode parameters, or initial TIRS line of sight model parameters. The data is placed in the Real-Time tier and made available for immediate download. Once the data have been reprocessed with definitive ephemeris, updated bumper mode parameters and refined TIRS parameters, the products are transitioned to either Tier 1 or Tier 2 and removed from the Real-Time tier. The transition delay from Real-Time to Tier 1 or Tier 2 is between 14 and 26 days.

Landsat 9 Collection 2 Tier 1 calibrated top-of-atmosphere (TOA) reflectance. Calibration coefficients are extracted from the image metadata. See Chander et al. (2009) for details on the TOA computation. Landsat scenes with the highest available data quality are placed into Tier 1 and are considered suitable for time-series processing analysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data that have well-characterized radiometry and are inter-calibrated across the different Landsat sensors. The georegistration of Tier 1 scenes will be consistent and within prescribed tolerances [<=12 m root mean square error (RMSE)]. All Tier 1 Landsat data can be considered consistent and inter-calibrated (regardless of sensor) across the full collection. See more information in the USGS docs. The T1_RT collection contains both Tier 1 and Real-Time (RT) assets. Newly-acquired Landsat 7 ETM+ and Landsat 8 OLI/TIRS data are processed upon downlink but use predicted ephemeris, initial bumper mode parameters, or initial TIRS line of sight model parameters. The data is placed in the Real-Time tier and made available for immediate download. Once the data have been reprocessed with definitive ephemeris, updated bumper mode parameters and refined TIRS parameters, the products are transitioned to either Tier 1 or Tier 2 and removed from the Real-Time tier. The transition delay from Real-Time to Tier 1 or Tier 2 is between 14 and 26 days.

For the needs of the “Portal for heritage buildings integration into the contemporary built environment”, in short, PERIsCOPE project, satellite observations were applied. This repository includes the results from the macro-scale analysis, for which thermal data, optical satellite images, and ready satellite products were exploited to provide multi-temporal information.

Satellite-based products estimate the temperature variations in a broader area for the selected urban testbeds (Limassol and Strovolos municipalities, Cyprus). Landsat 7 and 8 archives were downloaded through the EarthExplorer platform (“Landsat Collection 1 Level-1” for both “Landsat 7 Enhanced Thematic Mapper Plus (ETM+) Level-1” and “Landsat 8 OLI/TIRS C1 Level-1”). The Level-1 data downloaded from the platform were rescaled to the top of atmosphere (TOA) reflectance and radiance using radiometric rescaling coefficients provided in the metadata file delivered with the Level-1 product (metadata—MTL file).

More than 140 satellite images were selected (a cloud coverage filter was applied), downloaded, and processed, covering the period between 2013 and 2020. Specifically, 16 images during the Winter season, 30 images over Spring, 57 images for Summer, and 38 during Autumn were finally gathered for both case studies.

The Google Earth Engine cloud platform infrastructure was used to extract optical products, namely the Normalised Difference Vegetation Index (NDVI) and the Normalised Difference Built-up Index (NDBI), which characterise vegetated and built-up areas, respectively.

This repository concerns: (1) the mean Land Surface Temperature (LST) for both test sites (from 2013-2020), their standard deviation, and the maximum and minimum values. In addition, the (2) NDVI and (3) NDBI products per year (2013-2020) are provided. Lastly, (4) the seasonal variations are included.

The data are structured in both forms of ArcGIS Pro Geodatabase (.gdb), while individual .tiff formats are provided in each folder.

Further details can be found in the following references:

1. Agapiou, A.; Lysandrou, V. Observing Thermal Conditions of Historic Buildings through Earth Observation Data and Big Data Engine. Sensors 2021, 21, 4557. https://doi.org/10.3390/s21134557

2. Agapiou A., Lysandrou V., Cuca B., Copernicus earth observations for cultural heritage, Proceedings of the joint international event, 9th ARQUEOLÓGICA 2.0 & 3rd GEORES, Valencia (Spain). 26–28 April 2021, DOI: https://doi.org/10.4995/Arqueologica9.2021.12512