George Z. Xian Hua Shi Chase Mueller Reza Hussain Saeed Arab Kristi Sayler Danny Howard 20231120 Raster Digital Data Set https://doi.org/10.5066/P9H6E1FZ. Surface Urban Heat Island (SUHI) intensity data is intended to quantify the difference between urban surface temperatures and the surrounding non-urban environment. The calculation takes the difference between a specific urban pixel’s maximum land surface temperature (MeanLST) and the mean of the cities non-urban MeanLST. Data are provided across 50 regions throughout the Continental U.S. using previously generated annual MeanLST – derived from Collection 1 Landsat U.S. Analysis Ready Data (ARD) for Surface Temperature. The data ranges from 1985-2020, and covers data within 5 km of each city. NOTE: While a previous version is available from the author, all datasets for pilot cities can be found in version 5.0. Landsat surface temperature data is an important geophysical variable for understanding the thermal, hydrological, and societal impacts of urbanization. However, due to Landsat’s limited temporal frequency and the additional impacts of cloud cover, significant processing of the data is required to begin leveraging Landsat’s resolution and historical record. This dataset aims to eliminate the processing step and provide analysis ready data for evaluating the surface urban heat island. 1985 2020 ground condition Complete Annually -127.9771 -65.2545 51.6199 22.7687 ISO 19115 Topic Category environment climatologyMeteorologyAtmosphere USGS Thesaurus image collections multispectral imaging land use change heat flow (earth) The National Map Product Extent Thesaurus Landsat ARD Marine Realms Information Bank (MRIB) keywords surface urban heat island (SUHI) land surface temperature (LST) Landsat Analysis Ready Data (ARD) surface urban heat island intensity (SUHII) mean land surface temperature (MeanLST) USGS Metadata Identifier USGS:656e2d2ad34e7ca10833fb69 Common geographic areas Albuquerque,NM; Atlanta,GA; Austin,TX; Baltimore,MD; Birmingham,AL; Boise,ID; Boston,MA; Charlotte,NC; Cheyenne,WY; Chicago,Il; Cincinnati,OH; Colorado Springs,CO; Columbus,OH; Dallas,TX; Denver,CO; Des Moines,IA; Detroit,MI; El Paso,TX; Fargo,ND; Houston,TX; Indianapolis,IN; Jacksonville,FL; Kansas City,MO; Las Vegas,NV; Little Rock,AR; Los Angeles,CA; Louisville,KY; Memphis,TN; Miami,FL; Milwaukee,WI; Minneapolis,MN; Missoula,MT; Nashville,TN; New Orleans,LA.; New York,NY; Oklahoma City,OK; Omaha,NE; Philadelphia,PA; Phoenix,AZ; Pittsburgh,PA; Portland,OR; Raleigh,NC; Sacramento,CA; Salt Lake City,UT; San Antonio,TX; San Diego,CA; San Francisco,CA; Seattle,WA; Sioux Falls,SD; Washington,DC. CONUS United States None Albuquerque,NM; Atlanta,GA; Austin,TX; Baltimore,MD; Birmingham,AL; Boise,ID; Boston,MA; Charlotte,NC; Cheyenne,WY; Chicago,Il; Cincinnati,OH; Colorado Springs,CO; Columbus,OH; Dallas,TX; Denver,CO; Des Moines,IA; Detroit,MI; El Paso,TX; Fargo,ND; Houston,TX; Indianapolis,IN; Jacksonville,FL; Kansas City,MO; Las Vegas,NV; Little Rock,AR; Los Angeles,CA; Louisville,KY; Memphis,TN; Miami,FL; Milwaukee,WI; Minneapolis,MN; Missoula,MT; Nashville,TN; New Orleans,LA.; New York,NY; Oklahoma City,OK; Omaha,NE; Philadelphia,PA; Phoenix,AZ; Pittsburgh,PA; Portland,OR; Raleigh,NC; Sacramento,CA; Salt Lake City,UT; San Antonio,TX; San Diego,CA; San Francisco,CA; Seattle,WA; Sioux Falls,SD; Washington,DC. CONUS United States Any downloading and use of these data signifies a user's agreement to comprehension and compliance of the USGS Standard Disclaimer. Ensure all portions of the metadata are read and clearly understood before using the data in order to protect both user and USGS interests. These data are all provisional products. There is no guarantee of warranty concerning the accuracy of the data. Users should be aware that these data were developed from models using python Gdal, which can contain local error. Also, temporal changes may have occurred since data were collected, resulting in discrepancies between data and actual surface temperature. Users should not use these data for critical applications without a full awareness of their limitations. Acknowledgement of the originating agencies would be appreciated in products derived from these data. Any user who modifies these data is obligated to describe the types of modifications they perform. User specifically agrees not to misrepresent the data, nor to imply that changes made were approved or endorsed by the U.S. Geological Survey. Please refer to https://www.usgs.gov/privacy.html for the USGS disclaimer. U.S. Geological Survey, LAND RESOURCES George Z Xian Research Physical Scientist mailing address

47914 252nd Street

Sioux Falls SD 57198 United States 605-594-2599 605-594-6567 xian@usgs.gov U.S. Geological Survey (USGS) Environment as of Metadata Creation: Microsoft [Unknown] Version 6.2 (Build 9200) ; Esri ArcGIS 10.8.1 ; ERDAS imagine 2018; QGIS 3.8.2 ; (Build 9270) Service Pack N/A (Build N/A) George Xian Hua Shi Qiang Zhou Roger Auch Kevin Gallo Zhuoting Wu Michael Kolian 202111 publication n/a Elsevier BV ppg. 112803 https://doi.org/10.1016/j.rse.2021.112803 George Xian Hua Shi Roger Auch Kevin Gallo Qiang Zhou Zhuoting Wu Michael Kolian 20210412 publication GIScience & Remote Sensing vol. 58, issue 4 n/a Informa UK Limited ppg. 501-515 https://doi.org/10.1080/15481603.2021.1903282 George Z. Xian Hua Shi 2021 publication n/a US Geological Survey https://doi.org/10.3133/fs20213031 Hua Shi George Xian Roger Auch Kevin Gallo Qiang Zhou 20210818 publication Land vol. 10, issue 8 n/a MDPI AG ppg. 867 https://doi.org/10.3390/land10080867 George Xian Hua Shi Qiang Zhou Roger Auch Kevin Gallo Zhuoting Wu Michael Kolian 202202 publication Remote Sensing of Environment vol. 269 n/a Elsevier BV ppg. 112803 https://doi.org/10.1016/j.rse.2021.112803 The U.S. Historical Climatology Network (USHCN) and The Global Historical Climatology Network (GHCN) station based independent accuracy assessment was completed for this product. A total of 28,269 stations from across the 50 cities of CONUS were chosen within Celsius values of validation data. The relationship between the completed product and the validation dataset was analyzed using statistical formulas in Microsoft Excel and linear models. The result shows that mean surface temperature in the high intensity urban area significantly increased at a rate of 0.1-0.2 Celsius per year while no significant trend was found in surrounding non-urban areas. The SUHI intensity in the high intensity urban area significantly increased at a rate of 0.10-0.3 Celsius per year. The increasing trend gradually decreases from high intensity urban areas to low intensity urban areas. These values are various from different cities and fall within our expected ranges. All of existing test stations within the study area were used in the final accuracy assessments. The land surface temperatures were lower the farther they were away from the high-density urban areas of our CONUS study area. This provisional Landsat land surface temperature from Landsat Analysis Ready Data (ARD) in selected 50 cities of CONUS is the version dated 20230120. The data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details. A formal accuracy assessment of the horizontal positional information in the data set has not been conducted. A formal accuracy assessment of the vertical positional information in the data set has either not been conducted, or is not applicable. US CENSUS 20220101 vector digital data https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2022.html#list-tab-790442341 Digital and/or Hardcopy 20200101 ground condition City Boundaries provide city boundary for analysis and creating 5 km buffer zone. B. W. Pengra and et al 20210820 raster digital data https://doi.org/10.5066/P9QA5Q25 Digital and/or Hardcopy 19850101 20201231 ground condition LCMAP providing land cover classes from 1985-2020 and creating SUHI land cover (SUHI LC). Jon Dewitz 20230505 raster digital data https://doi.org/10.5066/P9JZ7AO3 Digital and/or Hardcopy 20010101 20201231 ground condition NLCD providing urban land cover classes and creating SUHI land cover (SUHI LC). G. Xian and et al 20220120 raster digital data https://doi.org/10.5066/P9H6E1FZ Digital and/or Hardcopy 19850101 20201231 ground condition MeanLST Proving SUHI annual mean land surface temperature. George Xian Hua Shi Qiang Zhou Roger Auch Kevin Gallo Zhuoting Wu Michael Kolian 202202 publication Remote Sensing of Environment vol. 269 n/a Elsevier BV ppg. 112803 https://doi.org/10.1016/j.rse.2021.112803 Digital and/or Hardcopy 19850101 20201231 ground condition Intensity calculating SUHI intensity for MeanLST Prepare Urban Centers using Census City Boundary shapefile data. Apply a 5 km buffer and merge overlapping regions. The region passing over the city identified in the filename is the primary urban center for which a mask is provided. City Boundaries 20221001 Clip annual MeanLST and land cover data to Urban Center shapefiles. MeanLST LCMAP NLCD 20230101 MeanLST Prepare land cover rasters by merging LCMAP and the nearest future year of NLCD data. If both datasets report urban, export as urban. If LCMAP has an urban confidence greater than or equal to 99, export as urban. If NLCD reports as non-urban while LCMAP reports as urban with a confidence of less than 99, export as Non-Urban. If none of the previous conditions are met, export the LCMAP class. NLCD LCMAP 20210801 Calculate the mean of the annual MeanLST for each non-urban land cover type. LCMAP NLCD MeanLST 20230801 MeanLST For each urban pixel calculate the difference between it’s reported MeanLST value and the mean of all non-urban pixels. MeanLST LCMAP NLCD 20230801 Intensity Vector G-polygon 1 Albers Conical Equal Area 29.5 45.5 -96.0 23.0 0.0 0.0 coordinate pair 0.6096 0.6096 meters WGS_1984 WGS_84 6378137.0 298.257223563 uhi_annual_meanlst_50_intensity.img Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell. Producer Defined -9999 No Data Producer defined -10 15 Celsius U.S. Geological Survey GS ScienceBase mailing address

Denver Federal Center, Building 810, Mail Stop 302

Denver CO 80225 United States 1-888-275-8747 sciencebase@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. Raster Digital Data Set https://doi.org/10.5066/P9H6E1FZ. None. No fees are applicable for obtaining the data set. 20231211 U.S. Geological Survey, LAND RESOURCES George Z Xian Research Physical Scientist mailing address

47914 252nd Street

Sioux Falls SD 57198 United States 605-594-2599 605-594-6567 xian@usgs.gov FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998