We implemented automated workflows using Jupyter notebooks for each state. The GIS processing, crucial for merging, extracting, and projecting GeoTIFF data, was performed using ArcPy—a Python package for geographic data analysis, conversion, and management within ArcGIS (Toms, 2015). After generating state-scale LES (large extent spatial) datasets in GeoTIFF format, we utilized the xarray and rioxarray Python packages to convert GeoTIFF to NetCDF. Xarray is a Python package to work with multi-dimensional arrays and rioxarray is rasterio xarray extension. Rasterio is a Python library to read and write GeoTIFF and other raster formats. Xarray facilitated data manipulation and metadata addition in the NetCDF file, while rioxarray was used to save GeoTIFF as NetCDF. These procedures resulted in the creation of three HydroShare resources (HS 3, HS 4 and HS 5) for sharing state-scale LES datasets. Notably, due to licensing constraints with ArcGIS Pro, a commercial GIS software, the Jupyter notebook development was undertaken on a Windows OS.

We recommend using an open-sourced Python-based workflow. Common packages used in the scripts here include Numpy, Scipy, Matplotlib, Rasterio, Pillow, and Pandas. For more detailed descriptions on software installation, see the related software packages. GeoTiff files can be visualized in the open-sourced QGIS software.

PROCESSING STEPS:Cuyahoga County Planning Commission:Extracted Band 3: Difference from County Average LST of 92.4℉Rounded to nearest degree differenceCustom symbology appliedCleveland State University:Robert Moore, M.S. Candidate, Cleveland State University, Department of Biological, Geological and Environmental SciencesRaster CRS: EPSG:4326Raster Width: 2214Raster Height: 1324Number of Bands: 3Data Type: float64NoData Value: NonePixel Size: 30mData Sources:Landsat - USGS Landsat 8 Level 2, Collection 2, Tier 1 - https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_L2ST_B10 is band utilized to calculate LSTMODIS - MOD11A1.061 Terra Land Surface Temperature and Emissivity Daily Global 1km - https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD11A1LST_Day_1km is band utilized to calculate LSTData Processing: Satellite scenes were compiled and averaged for summer months May 1 - September 30 for a 3-year period 2021-2023.Scenes with greater than 20% cloud cover were omitted from analysis using the CFMASK algorithm for Landsat scenes.MODIS scenes all met “clear-sky criteria” built into the MODIS algorithm.We utilized MODIS LST data to address missing pixels within Landsat data for Cuyahoga County.Workflow: The following Python data packages were utilized: Rasterio, NumPy, and matplotlib (package documentation below). Statistical Linear Regression was conducted between Landsat and MODIS LST values (94% R2 value) to calculate predicted Landsat LST values from MODIS values. Then using a mask, missing pixels values are replaced with their corresponding predicted Landsat LST values. 1.26% of land area was missing in Landsat data and replaced using this method.https://pypi.org/project/rasterio/https://pypi.org/project/numpy/https://pypi.org/project/matplotlib/ Each band holds LST values. Celsius (Band 1), Fahrenheit (Band 2), and Urban Heat Island Severity or the difference between the Observed LST (in ℉) and the County Average LST of 92.4℉ (Band 3)Coverage: Cuyahoga CountyUpdate Frequency: As new data becomes availableLast Update: August, 2024

Software presented here was developed using python v3.7, and requires installation the following packages: Rasterio Pandas Numpy Geopandas Jupyter Copy Matplotlib