Land Surface Temperature - 2016

The goal of the updated map is to show the heat differences across the region and is not intended to show the maximum temperature that any specific area can reach. An updated timestamp (2022 vs. 2016) and finer grained resolution (10m vs. 30m) improves the utility of the data for planning purposes. The LST dataset from 2016 can be accessed via the Minnesota Geospatial Commons. High resolution (10 meter) land surface temperature (LST) from September 1, 2022 is mapped for the seven-county metropolitan region of the Twin Cities. The goal of the map is to show the heat differences across the region and is not intended to show the maximum temperature that any specific area can reach. The raster dataset was computed at 30 meters using satellite imagery from Landsat 9 and downscaled to 10 meters using Copernicus Sentinel-2. These datasets were integrated using techniques modified from Ermida et al. 2020 and Onačillová et al. 2022). Open water was removed using ancillary data from OpenStreetMap and 2020 Generalized Land Use for the Twin Cities (Metropolitan Council).  First, Landsat 9 imagery taken at 11:59 am CDT on September 01, 2022 was processed into 30-meter resolution LST (based on Ermida et al. 2020). At this time, the air temperature was 88° F at the Minneapolis-St. Paul International Airport (NOAA). A model predicting LST based on spectral indices of Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI) was created and applied to 10-meter Sentenel-2 imagery. Sentinel-2 imagery was also taken on September 1, 2022, and this resulted in a 10-meter downscaled LST image (based on Onačillová et al. 2022). To account for anomalies in NDVI on the primary image date of September 1 (e.g., recently harvested agricultural fields), maximum NDVI occurring between July 1, 2022 and September 1, 2022 was used for both Landsat and Sentinel image processing. Water bodies were removed for all processing steps (OpenStreetMap 2023, Metropolitan Council 2021).  This dataset is an update to the 2016 LST data for the Twin Cities Region (Metropolitan Council).  The code to create and processes this dataset is available at: https://github.com/Metropolitan-Council/extreme.heat  Sources:  Ermida, S.L., Soares, P., Mantas, V., Göttsche, F.-M., Trigo, I.F., 2020. Google Earth Engine open-source code for Land Surface Temperature estimation from the Landsat series. Remote Sensing, 12 (9), 1471; https://doi.org/10.3390/rs12091471.  Metropolitan Council. 2021. Generalized Land Use 2020. Minnesota Geospatial Commons. https://gisdata.mn.gov/dataset/us-mn-state-metc-plan-generl-lnduse2020  Metropolitan Council. 2017. Land Surface Temperature for Climate Vulnerability Analysis. Minnesota Geospatial Commons. https://gisdata.mn.gov/dataset/us-mn-state-metc-env-cva-lst2016  NOAA, National Oceanic and Atmospheric Administration, National Centers for Environmental Information, station USW00014922. September 1, 2022.  Onačillová, K., Gallay, M., Paluba, D., Péliová, A., Tokarčík, O., Laubertová, D. 2022. Combining Landsat 8 and Sentinel 2 data in Google Earth Engine to derive higher resolution land surface temperature maps in urban environment. Remote Sensing, 14 (16), 4076. https://doi.org/10.3390/rs14164076.  OpenStreetMap contributors. 2023. Retrieved from https://planet.openstreetmap.org on April 12, 2023.

The Harmonized Landsat and Sentinel-2 (HLS) project takes input data from the joint NASA/USGS Landsat 8 and Landsat 9 satellites and the European Space Agency's Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites to generate a harmonized, analysis-ready surface reflectance data product with observations every 1.6 days.  Intended Use: The frequent revisit times of HLS allow for time series of land surface applications at the field/plot scale, which supports a wide range of applications, including agricultural health, insect infestations, and natural hazard impacts. The HLSS30 and HLSL30 products are gridded to the same resolution and tiling system and thus are “stackable” for time series analysis. Key Properties: Geographic Coverage: Global Land Temporal Coverage: March 2026 - Present Spatial Resolution: 30-meter Revisit Time: ~1 Day Update Frequency: Daily Product Level: Level-3 Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84 Service Coordinate System: Web Mercator Auxiliary Sphere (EPSG:3857) Multispectral Bands NameDescriptionUnitsData TypeFill ValueScale FactorBand 1Coastal AerosolN/Aint16-99990.0001Band 2BlueN/Aint16-99990.0001Band 3GreenN/Aint16-99990.0001Band 4RedN/Aint16-99990.0001Band 5NIRN/Aint16-99990.0001Band 6SWIR 1N/Aint16-99990.0001Band 7SWIR 2N/Aint16-99990.0001Band 9CirrusN/Aint16-99990.0001Band 10TIRS 1N/Aint16-99990.01Band 11TIRS 2N/Aint16-99990.01FmaskQuality BitsBit Fielduint8255N/ASAASun Azimuth AngleDegreeuint16400000.01SZASun Zenith AngleDegreeuint16400000.01VAAView Azimuth AngleDegreeuint16400000.01VZAView Zenith AngleDegreeuint16400000.01Processing Templates Server-side renderings available on this layer (select via the Rendering / Image Display options). The default rendering is Natural Color. Band-combination templates are for visualization; index templates (NDVI, NDWI, NBR, NDBI) return values suitable for analysis. "DRA" applies dynamic range adjustment; "Cloud Mask" variants suppress pixels flagged by the Fmask quality band. NDVI Colormap — Normalized difference vegetation index (NDVI) with colormap. Dark green represents vigorous vegetation and brown represents sparse vegetation. It is computed as (b5 - b4) / (b5 + b4) and is suitable for vegetation, land cover and plant health monitoring. (also available: Cloud Mask) NDVI Raw — Normalized difference vegetation index (NDVI) computed as (b5 - b4) / (b5 + b4) and is suitable for vegetation, land cover and plant health monitoring. (also available: Cloud Mask) NDWI Raw — The NDWI index is computed as Green(Band03)-NIR(Band05)/ Green(Band03)+NIR(Band05) and is most suitable for mapping of water bodies, droughts, boundary evaluation. Water bodies have strong absorbability and low radiation within the visible to infrared spectral ranges and this index is based on this phenomenon. (also available: Cloud Mask) Natural Color — Natural Color bands red, green, blue(4, 3, 2) displayed with fixed stretch applied. (also available: Cloud Mask, DRA, DRA and Cloud Mask) Normalized Burn Ratio — The NBR index is computed as NIR(Band05)-SWIR(Band7)/NIR(Band05)+SWIR(Band7) and appropriate for highlighting burned areas and estimating fire severity. It highlights burnt areas in fire zones greater than 500 acres. You will need to generate a differenced NBR image by subtracting a post-fire image from a pre-fire image. The resulting darker pixels represent burned areas. (also available: Cloud Mask) Normalized Difference Built-Up Index — The NDBI index is computed as SWIR(Band6)-NIR(Band05)/ SWIR(Band6)+NIR(Band05) and is suitable for watershed runoff predictions, tracking urbananisation and land-use planning. It highlights urban areas with higher reflectance in the SWIR region, compared to the NIR region. This index also contains a cloud mask. (also available: Cloud Mask) Limitations of Use: HLSL30 v2.0 data contain several known limitations related to atmospheric correction, cloud masking, geolocation, and metadata that users should carefully consider before analysis. A full description of these known issues is available here. Latency: Imagery tiles are configured to render at a 150 meter scale. Dynamic Mosaic rendering can take an extended period of time.  Resource Links: Download Data User Guide General Documentation For inquiries about this service, please contact support@earthdata.nasa.gov or post/view questions on the Earthdata Forum Citation: Neigh, C., Ju, J., Roger, J.-C., Skakun, S., Vermote, E., Claverie, M., Dungan, J., Yin, Z., Freitag, B., & Justice, C. (2021). HLS Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m v2.0 [Dataset]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/HLS/HLSL30.002

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) LEOLSTCMG30 version 2 Climate Modeling Grid (CMG) product provides Land Surface Temperature (LST) derived from the Low Earth Orbit (LEO) satellite data record from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) instruments as well as LST error estimates for both day and night. The MEaSUREs LEOLST product is generated by regridding the monthly CMG products from Aqua MODIS (MYD21C3) and VIIRS (VNP21C3 and VJ121). The product is available on 0.25, 0.5, and 1 degree optimized climate grids with well characterized per-pixel uncertainties. A low-resolution browse is also available showing LST as an RGB (red, green, blue) image in PNG format.

The Harmonized Landsat and Sentinel-2 (HLS) project takes input data from the joint NASA/USGS Landsat 8 and Landsat 9 satellites and the European Space Agency's Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites to generate a harmonized, analysis-ready surface reflectance data product with observations every 1.6 days.  Intended Use: The frequent revisit times of HLS allow for time series of land surface applications at the field/plot scale, which supports a wide range of applications, including agricultural health, insect infestations, and natural hazard impacts. The HLSS30 and HLSL30 products are gridded to the same resolution and tiling system and thus are “stackable” for time series analysis. Key Properties: Geographic Coverage: Global Land Temporal Coverage: May 2026 - Present Spatial Resolution: 30-meter Revisit Time: ~1 Day Update Frequency: Daily Product Level: Level-3 Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84 Service Coordinate System: Web Mercator Auxiliary Sphere (EPSG:3857) Multispectral Bands Name Description Units Data Type Fill Value Scale Factor Band 1Coastal AerosolN/Aint16-99990.0001 Band 2BlueN/Aint16-99990.0001 Band 3GreenN/Aint16-99990.0001 Band 4RedN/Aint16-99990.0001 Band 5Red Edge 1N/Aint16-99990.0001 Band 6Red Edge 2N/Aint16-99990.0001 Band 7Red Edge 3N/Aint16-99990.0001 Band 8NIR BroadN/Aint16-99990.0001 Band 8ANIR NarrowN/Aint16-99990.0001 Band 9Water VaporN/Aint16-99990.0001 Band 10CirrusN/Aint16-99990.0001 Band 11SWIR 1N/Aint16-99990.0001 Band 12SWIR 2N/Aint16-99990.0001 FmaskQuality BitsBit Fielduint8255N/A SAASun Azimuth AngleDegreeuint16400000.01 Processing Templates Server-side renderings available on this layer (select via the Rendering / Image Display options). The default rendering is Natural Color. Band-combination templates are for visualization; index templates (NDVI, NDWI, NBR, NDBI) return values suitable for analysis. "DRA" applies dynamic range adjustment; "Cloud Mask" variants suppress pixels flagged by the Fmask quality band. NDVI Colormap — Normalized difference vegetation index (NDVI) with colormap. Dark green represents vigorous vegetation and brown represents sparse vegetation. It is computed as (b5 - b4) / (b5 + b4) and is suitable for vegetation, land cover and plant health monitoring. (also available: Cloud Mask) NDVI Raw — Normalized difference vegetation index (NDVI) computed as (b5 - b4) / (b5 + b4) and is suitable for vegetation, land cover and plant health monitoring. (also available: Cloud Mask) NDWI Raw — The NDWI index is computed as Green(Band03)-NIR(Band05)/ Green(Band03)+NIR(Band05) and is most suitable for mapping of water bodies, droughts, boundary evaluation. Water bodies have strong absorbability and low radiation within the visible to infrared spectral ranges and this index is based on this phenomenon. (also available: Cloud Mask) Natural Color — Natural Color bands red, green, blue(4, 3, 2) displayed with fixed stretch applied. (also available: Cloud Mask, DRA, DRA and Cloud Mask) Normalized Burn Ratio — The NBR index is computed as NIR(Band05)-SWIR(Band7)/NIR(Band05)+SWIR(Band7) and appropriate for highlighting burned areas and estimating fire severity. It highlights burnt areas in fire zones greater than 500 acres. You will need to generate a differenced NBR image by subtracting a post-fire image from a pre-fire image. The resulting darker pixels represent burned areas. (also available: Cloud Mask) Normalized Difference Built-Up Index — The NDBI index is computed as SWIR(Band6)-NIR(Band05)/ SWIR(Band6)+NIR(Band05) and is suitable for watershed runoff predictions, tracking urbananisation and land-use planning. It highlights urban areas with higher reflectance in the SWIR region, compared to the NIR region. This index also contains a cloud mask. (also available: Cloud Mask) Limitations of Use: HLSS30 v2.0 data contain several known limitations related to atmospheric correction, cloud masking, geolocation, and metadata that users should carefully consider before analysis. A full description of these known issues is available here. Latency: Imagery tiles are configured to render at a 150 meter scale. Dynamic Mosaic rendering can take an extended period of time.  Resource Links: Download Data User Guide General Documentation For inquiries about this service, please contact support@earthdata.nasa.gov or post/view questions on the Earthdata Forum Citation: Neigh, C., Ju, J., Roger, J.-C., Skakun, S., Vermote, E., Claverie, M., Dungan, J., Yin, Z., Freitag, B., & Justice, C. (2021). HLS Sentinel-2 Multi-spectral Instrument Surface Reflectance Daily Global 30m v2.0 [Dataset]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/HLS/HLSS30.002.

A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MOD11 LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MOD21C2 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free MOD21A1D and MOD21A1N daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MOD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). Known Issues * Users of MODIS LST products may notice an increase in occurrences of extreme high temperature outliers in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination example documentation. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

June, July, and August 2020-2023 for the area covering a city of interest and with less than 20% cloud cover. Pixels converted into points and later summarized by NPA to calculate mean, min, and max.