This dynamic World Elevation Terrain layer returns float values representing ground heights in meters and compiles multi-resolution data from many authoritative data providers from across the globe. Heights are orthometric (sea level = 0), and water bodies that are above sea level have approximated nominal water heights.Height units: MetersUpdate Frequency: QuarterlyCoverage: World/GlobalData Sources: This layer is compiled from a variety of best available sources from several data providers. To see the coverage and extents of various datasets comprising this service in an interactive map, see World Elevation Coverage Map.What can you do with this layer?Use for Visualization: This layer is generally not optimal for direct visualization. By default, 32 bit floating point values are returned, resulting in higher bandwidth requirements. Therefore, usage should be limited to applications requiring elevation data values. Alternatively, client applications can select from numerous additional functions, applied on the server, that return rendered data. For visualizations such as multi-directional hillshade, hillshade, elevation tinted hillshade, and slope, consider using the appropriate server-side function defined on this service.Use for Analysis: Yes. This layer provides data as floating point elevation values suitable for use in analysis. There is a limit of 5000 rows x 5000 columns.Note: This layer combine data from different sources and resamples the data dynamically to the requested projection, extent and pixel size. For analyses using ArcGIS Desktop, it is recommended to filter a dataset, specify the projection, extent and cell size using the Make Image Server Layer geoprocessing tool. The extent is factor of cell size and rows/columns limit. e.g. if cell size is 10 m, the extent for analysis would be less than 50,000 m x 50,000 m.Server Functions: This layer has server functions defined for the following elevation derivatives. In ArcGIS Pro, server function can be invoked from Layer Properties - Processing Templates.

Slope Degrees Slope Percent Aspect Ellipsoidal height Hillshade Multi-Directional Hillshade Dark Multi-Directional Hillshade Elevation Tinted Hillshade Slope Map Aspect Map Mosaic Method: This image service uses a default mosaic method of "By Attribute”, using Field 'Best' and target of 0. Each of the rasters has been attributed with ‘Best’ field value that is generally a function of the pixel size such that higher resolution datasets are displayed at higher priority. Other mosaic methods can be set, but care should be taken as the order of the rasters may change. Where required, queries can also be set to display only specific datasets such as only NED or the lock raster mosaic rule used to lock to a specific dataset.Accuracy: Accuracy will vary as a function of location and data source. Please refer to the metadata available in the layer, and follow the links to the original sources for further details. An estimate of CE90 and LE90 are included as attributes, where available.This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single request.This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

Sentinel-2 Level-1C imagery with on-the-fly renderings for visualization. This imagery layer pulls directly from the Sentinel-2 on AWS collection and is updated daily with new imagery.Sentinel-2 imagery can be applied across a number of industries, scientific disciplines, and management practices. Some applications include, but are not limited to, land cover and environmental monitoring, climate change, deforestation, disaster and emergency management, national security, plant health and precision agriculture, forest monitoring, watershed analysis and runoff predictions, land-use planning, tracking urban expansion, highlighting burned areas and estimating fire severity.Geographic CoverageGlobalContinental land masses from 65.4° South to 72.1° North, with these special guidelines:All coastal waters up to 20 km from the shoreAll islands greater than 100 km2All EU islandsAll closed seas (e.g. Caspian Sea)The Mediterranean Sea Temporal CoverageThis layer includes a rolling collection of Sentinel-2 imagery acquired within the past 14 months.This layer is updated daily with new imagery.The revisit time for each point on Earth is every 5 days.The number of images available will vary depending on location. Product LevelThis service provides Level-1C Top of Atmosphere imagery.Alternatively, Sentinel-2 Level-2A is also available. Image Selection/FilteringThe most recent and cloud free images are displayed by default.Any image available within the past 14 months can be displayed via custom filtering.Filtering can be done based on attributes such as Acquisition Date, Estimated Cloud Cover, and Tile ID.Tile_ID is computed as [year][month][day]T[hours][minutes][seconds]_[UTMcode][latitudeband][square]_[sequence]. More… Visual RenderingDefault rendering is Natural Color (bands 4,3,2) with Dynamic Range Adjustment (DRA).The DRA version of each layer enables visualization of the full dynamic range of the images.Rendering (or display) of band combinations and calculated indices is done on-the-fly from the source images via Raster Functions.Various pre-defined Raster Functions can be selected or custom functions created.Available renderings include: Agriculture with DRA, Bathymetric with DRA, Color-Infrared with DRA, Natural Color with DRA, Short-wave Infrared with DRA, Geology with DRA, NDMI Colorized, Normalized Difference Built-Up Index (NDBI), NDWI Raw, NDWI - with VRE Raw, NDVI – with VRE Raw (NDRE), NDVI - VRE only Raw, NDVI Raw, Normalized Burn Ratio, NDVI Colormap. Multispectral BandsBandDescriptionWavelength (µm)Resolution (m)1Coastal aerosol0.433 - 0.453602Blue0.458 - 0.523103Green0.543 - 0.578104Red0.650 - 0.680105Vegetation Red Edge0.698 - 0.713206Vegetation Red Edge0.733 - 0.748207Vegetation Red Edge0.773 - 0.793208NIR0.785 - 0.900108ANarrow NIR0.855 - 0.875209Water vapour0.935 - 0.9556010SWIR – Cirrus1.365 - 1.3856011SWIR-11.565 - 1.6552012SWIR-22.100 - 2.28020Additional NotesOverviews exist with a spatial resolution of 150m and are updated every quarter based on the best and latest imagery available at that time.To work with source images at all scales, the ‘Lock Raster’ functionality is available. NOTE: ‘Lock Raster’ should only be used on the layer for short periods of time, as the imagery and associated record Object IDs may change daily.This ArcGIS Server dynamic imagery layer can be used in Web Maps and ArcGIS Desktop as well as Web and Mobile applications using the REST based Image services API.Images can be exported up to a maximum of 4,000 columns x 4,000 rows per request. Data SourceSentinel-2 imagery is the result of close collaboration between the (European Space Agency) ESA, the European Commission and USGS. Data is hosted by the Amazon Web Services as part of their Registry of Open Data. Users can access the imagery from Sentinel-2 on AWS, or alternatively access EarthExplorer or the Copernicus Data Space Ecosystem to download the scenes.For information on Sentinel-2 imagery, see Sentinel-2.

Drought and aridity hazard regional change summaries for the National Climate Risk Assessment (NCRA). The drought and aridity hazards have been summarised for boundaries relating to assessments of freshwater and terrestrial natural environments.

Australian Climate Service (ACS) drought and aridity hazard datasets (https://github.com/AusClimateService/hazards-drought) 1. AI - Aridity index 2. SPI3 - Standardised precipitation index 3

Each hazard dataset includes an ensemble of climate models for four global warming levels (GWLs), 1.2, 1.5, 2.0 and 3.0 degrees Celsius, above a pre-industrial mean for 1850 to 1900.

Either the absolute change (difference to GWL1.2) or the relative change (difference to GWL1.2 divided by GWL1.2) was calculated for each of the higher GWLs compared to GWL1.2.

Regions of interest boundaries 1. Australia 2. NCRA regions 3. Aggregate Ecological Groups (AEGs) 4. Combined NCRA regions and AEGs 5. Level 2 drainage basins from the Australian Hydrological Geospatial Fabric (AHGF) 6. Spatially combined AHGF Level 2 drainage basins and hazard dataset grid cells which intersect with perennial drainage lines from the AHGF Network Streams 7. Spatially combined AHGF Level 2 drainage basins and hazard dataset grid cells which intersect with ephemeral drainage lines from AHGF Network Streams

For each hazard/GWL-change combination, a set of statistics (the mean, 10th percentile, 50th percentile, 90th percentile and standard deviation) was calculated for each climate model in the ensemble over the regions of interest areas. From these, the median of each statistic from all the models in the ensemble was calculated.

Collection data files The outputs are tabular data in comma-separated value (csv) files with one file per hazard/GWL-change combination. The columns are ID and region name that link to the regions of interest boundary datasets for visualising in GIS software, and the medians of the statistics. Rows are the individual region of interest polygons. Lineage: DATASETS Hazard data The National Climate Risk Assessment (NCRA) hazard data were supplied by the Australian Climate Service (ACS) from data stored on the National Computational Infrastructure (NCI) as part of Project ia39. The hazard data came in the form of an ensemble of climate models, with outputs for four global warming levels (GWLs) from each of the individual models. The GWLs are 1.2, 1.5, 2.0 and 3.0 degrees Celsius above a pre-industrial mean for 1850 to 1900.

Australian Climate Service (ACS) drought and aridity hazard datasets (https://github.com/AusClimateService/hazards-drought) used in this collection: 1. AI - Aridity index 2. SPI3 - Standardised precipitation index 3

Regions of interest boundaries The hazard data were summarized by regions of interest using polygon shape files for the following areas: 1. Australia - 2021 - Shapefile (https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs-edition-3/jul2021-jun2026/access-and-downloads/digital-boundary-files) 2. NCRA regions (https://www.acs.gov.au/datasets/4f8b960fd3694fc28d6ff3d1278e9e75_0/about) 3. Aggregate Ecological Groups (AEGs) derived from National Vegetation Information System (NVIS) data (https://data.csiro.au/collection/csiro:64128) 4. Spatially combined NCRA and AEG polygons (https://data.csiro.au/collection/csiro:64133) 5. Level 2 drainage basins from the Australian Hydrological Geospatial Fabric (AHGF) (http://www.bom.gov.au/water/geofabric/download.shtml) 6. Spatially combined AHGF Level 2 drainage basins and hazard dataset grid cells which intersect with perennial drainage lines from the AHGF Network Streams (where the Perennial attribute is "Perennial") (https://data.csiro.au/collection/csiro:64133; drainage lines from http://www.bom.gov.au/water/geofabric/download.shtml) 7. Spatially combined AHGF Level 2 drainage basins and hazard dataset grid cells which intersect with ephemeral drainage lines from AHGF Network Streams (where the Perennial attribute is "Non Perennial") (https://data.csiro.au/collection/csiro:64133; drainage lines from http://www.bom.gov.au/water/geofabric/download.shtml)

METHODS The drought and aridity hazard datasets were summarised for each set of regions of interest boundaries using methods and code from the ACS (https://github.com/AusClimateService/plotting_maps).

Either the absolute change (difference to GWL1.2) or the relative change (difference to GWL1.2 divided by GWL1.2) was calculated for each of the higher GWLs compared to GWL1.2.

Absolute change was calculated for AI and SPI3 using the Australia, NCRA, AEG and combined NCRA-AEG boundaries. Relative change was calculated for AI for the drainage basins, the combined drainage basins-perennial drainage lines, and the combined drainage basins-ephemeral drainage lines boundaries.

For each hazard/GWL-change combination, a set of statistics (the mean, 10th percentile, 50th percentile, 90th percentile and standard deviation) was calculated for each climate model in the ensemble over the regions of interest areas. From these, the median of each statistic from all the models in the ensemble was calculated. For a more detailed description of the methods, see the 'Spatial data processing methods' file in the Supporting Documentation section.

OUTPUTS - COLLECTION DATA FILES The outputs are tabular data in comma-separated value (csv) files with one file per hazard/GWL-change combination. The columns are ID and region name (linking to the boundary datasets for visualising in GIS software), and the medians for each of the statistics. Rows are the individual region of interest areas (polygons).

Climate plays a crucial role in shaping species distribution and evolution over time. Dr. Elisabeth Vrba’s Resource-Use hypothesis posited that zones at the extremes of temperature and precipitation conditions should host a greater number of climate specialist species than other zones because of higher historical fragmentation. Here, we tested this hypothesis by examining climate-induced fragmentation over the past 5 million years. Our findings revealed that, as stated by Vrba, the number of climate specialist species increases with historical regional climate fragmentation, whereas climate generalist species richness decreases. This relationship is approximately 40% stronger than the correlation between current climate and species richness for climate specialist species and 77% stronger for generalist species. These evidences suggest that the effect of climate historical fragmentation is more significant than that of current climate conditions in explaining mammal biogeography. These r..., Climate Data and Classification In this study, we employed the Köppen-Geiger climate classification to categorize climate zones. This system relies on climatic parameters, specifically monthly mean temperature (ºC) and total precipitation (mm), to define climate types (Beck et al., 2018; Köppen, 1884). Given the close correlation between climate and vegetation, these climate zones tend to align closely with global biome patterns (Belda et al., 2014), providing a proxy for examining how climate shapes biome distributions (Mucina, 2019). The Köppen-Geiger climate classification recognises 23 distinct climate regimes, grouped into five major zones: Tropical, Arid, Temperate, Cold, and Polar (Figure 1A). These zones served as the basis for our analysis of the impact of climate change on environmental fragmentation. Climate data for the last 5 million years were obtained from the high-resolution paleoclimate emulator, PALEO-PGEM (Holden et al., 2019). This dataset offers monthly climate info..., , # Data from: Vrba was right: Historical climatic fragmentation, and not current climate, explains mammal biogeography

https://doi.org/10.5061/dryad.x69p8czsn

This dataset includes the information and an R code to run every analysis in the research paper.

Description of the data and file structure

Mammals_db.csv: Database indicating the presence (1) or absence (0) of each mammal species included in this study (N=5739) in each of the pixels that make up the world map at a resolution of 0.5º. Rows correspond to map pixels, while columns correspond to mammal species. The database also contains other information about each pixel such as which landmass and climate zone it belongs to.
ATTENTION: Large table size, may cause opening issues.

Reclassified_maps.zip: Maps displaying the planet's climate reclassified into the 5 main Köppen-Geiger categories on a global scale with a resolution of 0.5º. In TIFF format. Based on the PALEO-PGEM emulato...

A. SUMMARYThis data represents the boundaries of City-owned lands maintained in the City's Facility System of Record (FSR). Note: Not all lands are within the City and County proper. The City owns properties outside of its boundaries, including lands managed by SF Recreation and Parks, SF Public Utilities Commission, and other agencies. Certain lands are managed by following agencies which are not directly part of the City and County of San Francisco, but are included here for reference: San Francisco Housing Authority (SFHA), San Francisco Office of Community Investment and Infrastructure (OCII), and City College of San Francisco.B. HOW THE DATASET IS CREATEDThe Enterprise GIS program in the Department of Technology is the technical custodian of the FSR. This team creates and maintains this dataset in conjunction with the Real Estate Division and the Capital Planning Program of the City Administrator’s Office, who act as the primary business data stewards for this data. C. UPDATE PROCESSThere are a handful of events that may trigger changes to this dataset:1. The sale of a property2. The leasing of a property3. The purchase of a property4. The change in jurisdiction of a property (e.g. from MTA to DPW)5. The removal or improvement of the propertyEach of these changes triggers a workflow that updates the FSR. The Real Estate Division and Capital Planning make updates on an ongoing basis. The full dataset is reviewed quarterly to ensure nothing is missing or needs to be corrected. Updates to the data, once approved, are immediately reflected in the internal system and are updated here in the open dataset on a monthly basis.D. HOW TO USE THIS DATASETSee here for an interactive map of all the City lands in this dataset. To track the facilities on City lands, join this dataset to the City Facilities dataset using the land_id field. If you see an error in the data, you can submit a change request with the relevant information to dtis.helpdesk@sfgov.org. Please be as specific about the error as you can (including relevant land_id(s)). E. RELATED DATASETSCity FacilitiesData pushed to ArcGIS Online on November 1, 2025 at 6:00 AM by SFGIS.Data from: https://data.sfgov.org/d/gtnh-hgvsDescription of dataset columns:

 land_id
 Unique Identifier


 land_name
 Name of land. i.e., property owned by City and County of San Francisco


 dept_id
 Foreign key to Department table in Facility System of Record (FSR) database


 address
 Address of City Land


 city
 Address City


 zip
 Address ZIP Code


 category
 Category of department (useful for mapping purposes)


 department_name
 Name of Department with Jurisdiction


 shape
 Geometry of City Land encoded as multipolygon


 data_last_updated
 Timestamp when the record (row) was last updated in the source system


 data_as_of
 Timestamp when the record (row) was last refreshed in the source system


 data_loaded_at
 Timestamp when the record (row) was was last updated here (in the data portal)

Note: If no description was provided by DataSF, the cell is left blank. See the source data for more information.

A detailed explanation of how this dataset was put together, including data sources and methodologies, follows below.Please see the "Terms of Use" section below for the Data DictionaryDATA ACQUISITION AND CLEANING PROCESSThis dataset was built from 5 separate datasets queried during the months of April and May 2023 from the Census Microdata System (link below):https://data.census.gov/mdat/#/All datasets include information on Property Value (VALP) by: Educational Attainment (SCHL), Gender (SEX), a specified race or ethnicity (RAC or HISP), and are grouped by Public Use Microdata Areas (PUMAS). PUMAS are geographic areas created by the Census bureau; they are weighted by land area and population to facilitate data analysis. Data also Included totals for the state of New Mexico, so 19 total geographies are represented. Datasets were downloaded separately by race and ethnicity because this was the only way to obtain the VALP, SCHL, and SEX variables intersectionally with race or ethnicity data. Datasets were downloaded separately by race and ethnicity because this was the only way to obtain the VALP, SCHL, and SEX variables intersectionally with race or ethnicity data. Cleaning each dataset started with recoding the SCHL and HISP variables - details on recoding can be found below.After recoding, each dataset was transposed so that PUMAS were rows and SCHL, VALP, SEX, and Race or Ethnicity variables were the columns.Median values were calculated in every case that recoding was necessary. As a result, all Property Values in this dataset reflect median values.At times the ACS data downloaded with zeros instead of the 'null' values in initial query results. The VALP variable also included a "-1" variable to reflect N/A values (details in variable notes). Both zeros and "-1" values were removed before calculating median values, both to keep the data true to the original query and to generate accurate median values.Recoding the SCHL variable resulted in 5 rows for each PUMA, reflecting the different levels of educational attainment in each region. Columns grouped variables by race or ethnicity and gender. Cell values were property values.All 5 datasets were joined after recoding and cleaning the data. Original datasets all include 95 rows with 5 separate Educational Attainment variables for each PUMA, including New Mexico State totals.Because 1 row was needed for each PUMA in order to map this data, the data was split by Educational Attainment (SCHL), resulting in 110 columns reflecting median property values for each race or ethnicity by gender and level of educational attainment.A short, unique 2 to 5 letter alias was created for each PUMA area in anticipation of needing a unique identifier to join the data with. GIS AND MAPPING PROCESSA PUMA shapefile was downloaded from the ACS site. The Shapefile can be downloaded here: https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/PUMA_TAD_TAZ_UGA_ZCTA/MapServerThe DBF from the PUMA shapefile was exported to Excel; this shapefile data included needed geographic information for mapping such as: GEOID, PUMACE. The UIDs created for each PUMA were added to the shapefile data; the PUMA shapfile data and ACS data were then joined on UID in JMP.The data table was joined to the shapefile in ARC GiIS, based on PUMA region (specifically GEOID text).The resulting shapefile was exported as a GDB (geodatabase) in order to keep 'Null' values in the data. GDBs are capable of including a rule allowing null values where shapefiles are not. This GDB was uploaded to NMCDCs Arc Gis platform. SYSTEMS USEDMS Excel was used for data cleaning, recoding, and deriving values. Recoding was done directly in the Microdata system when possible - but because the system is was in beta at the time of use some features were not functional at times.JMP was used to transpose, join, and split data. ARC GIS Desktop was used to create the shapefile uploaded to NMCDC's online platform. VARIABLE AND RECODING NOTESTIMEFRAME: Data was queried for the 5 year period of 2015 to 2019 because ACS changed its definiton for and methods of collecting data on race and ethinicity in 2020. The change resulted in greater aggregation and les granular data on variables from 2020 onward.Note: All Race Data reflects that respondants identified as the specified race alone or in combination with one or more other races.VARIABLE:ACS VARIABLE DEFINITIONACS VARIABLE NOTESDETAILS OR URL FOR RAW DATA DOWNLOADRACBLKBlack or African American ACS Query: RACBLK, SCHL, SEX, VALP 2019 5yrRACAIANAmerican Indian and Alaska Native ACS Query: RACAIAN, SCHL, SEX, VALP 2019 5yrRACASNAsian ACS Query: RACASN, SCHL, SEX, VALP 2019 5yrRACWHTWhite ACS Query: RACWHT, SCHL, SEX, VALP 2019 5yrHISPHispanic Origin ACS Query: HISP ORG, SCHL, SEX, VALP 2019 5yrHISP RECODE: 24 original separate variablesThe Hispanic Origin (HISP) variable originally included 24 subcategories reflecting Mexican, Central American, South American, and Caribbean Latino, and Spanish identities from each Latin American counry. 7 recoded VariablesThese 24 variables were recoded (grouped) into 7 simpler categories for data analysis: Not Spanish/Hispanic/Latino, Mexican, Caribbean Latino, Central American, South American, Spaniard, All other Spanish/Hispanic/Latino Female. Not Spanish/Hispanic/Latino was not really used in the final dataset as the race datasets provided that information.SCHLEducational Attainment25 original separate variablesThe Educational Attainment (SCHL) variable originally included 25 subcategories reflecting the education levels of adults (over 18) surveyed by the ACS. These include: Kindergarten, Grades 1 through 12 separately, 12th grade with no diploma, Highschool Diploma, GED or credential, less than 1 year of college, more than 1 year of college with no degree, Associate's Degree, Bachelor's Degree, Master's Degree, Professional Degree, and Doctorate Degree.SCHL RECODE: 5 recoded variablesThese 25 variables were recoded (grouped) into 5 simpler categories for data analysis: No High School Diploma, High School Diploma or GED, Some College, Bachelor's Degree, and Advanced or Professional DegreeSEXGender2 variables1 - Male, 2 - FemaleVALPProperty Value1 variableValues were rounded and top-coded by ACS for anonymity. The "-1" variable is defined as N/A (GQ/ Vacant lots except 'for sale only' and 'sold, not occupied' / not owned or being bought.) This variable reflects the median value of property owned by individuals of each race, ethnicity, gender, and educational attainment category.PUMAPublic Use Microdata Area18 PUMAsPUMAs in New Mexico can be viewed here:https://nmcdc.maps.arcgis.com/apps/mapviewer/index.html?webmap=d9fed35f558948ea9051efe9aa529eafData includes 19 total regions: 18 Pumas and NM State TotalsNOTES AND RESOURCESThe following resources and documentation were used to navigate the ACS PUMS system and to answer questions about variables:Census Microdata API User Guide:https://www.census.gov/data/developers/guidance/microdata-api-user-guide.Additional_Concepts.html#list-tab-1433961450Accessing PUMS Data:https://www.census.gov/programs-surveys/acs/microdata/access.htmlHow to use PUMS on data.census.govhttps://www.census.gov/programs-surveys/acs/microdata/mdat.html2019 PUMS Documentation:https://www.census.gov/programs-surveys/acs/microdata/documentation.2019.html#list-tab-13709392012014 to 2018 ACS PUMS Data Dictionary:https://www2.census.gov/programs-surveys/acs/tech_docs/pums/data_dict/PUMS_Data_Dictionary_2014-2018.pdf2019 PUMS Tiger/Line Shapefileshttps://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2019&layergroup=Public+Use+Microdata+Areas Note 1: NMCDC attemepted to contact analysts with the ACS system to clarify questions about variables, but did not receive a timely response. Documentation was then consulted.Note 2: All relevant documentation was reviewed and seems to imply that all survey questions were answered by adults, age 18 or over. Youth who have inherited property could potentially be reflected in this data.Dataset and feature service created in May 2023 by Renee Haley, Data Specialist, NMCDC.

Beta Notice: This item is currently in beta and is intended for early access, testing, and feedback. It is not recommended for production use, as functionality and content are subject to change without notice.Sentinel-2, 10m Multispectral 13-band imagery, rendered on-the-fly. Available for visualization and analytics, this Imagery Layer pulls directly from the Sentinel-2 on AWS collection and is updated daily with new imagery.This imagery layer can be used for multiple purposes including but not limited to vegetation, land cover, plant health, deforestation and environmental monitoring.Geographic CoverageGlobalContinental land masses from 65.4° South to 72.1° North, with these special guidelines: All coastal waters up to 20 km from the shoreAll islands greater than 100 km2All EU islandsAll closed seas (e.g. Caspian Sea)The Mediterranean SeaNote: Areas of interest going beyond the Mission baseline (as laid out in the Mission Requirements Document) will be assessed, and may be added to the baseline if sufficient resources are identified. Temporal CoverageThe revisit time for each point on Earth is every 5 days.This layer is updated daily with new imagery.This imagery layer is designed to include imagery collected within the past 14 months. Custom Image Services can be created for access to images older than 14 months.The number of images available will vary depending on location.Image Selection/FilteringThe most recent and cloud free image, for any location, is displayed by default.Any image available, within the past 14 months, can be displayed via custom filtering.Filtering can be done based on Acquisition Date, Estimated Cloud Cover, and Tile ID.Tile_ID is computed as [year][month][day]T[hours][minutes][seconds]_[UTMcode][latitudeband][square]_[sequence]. More…NOTE: Not using filters, and loading the entire archive, may affect performance. Analysis ReadyThis imagery layer is analysis ready with TOA correction applied. Visual RenderingDefault rendering is NDVI Raw (Normalized Difference vegetation index) computed as NIR(Band8)-Red(Band4)/NIR(Band8)+Red(Band4). The Colorized version of this layer is NDVI Colormap.Rendering (or display) of band combinations and calculated indices is done on-the-fly from the source images via Raster Functions.Various pre-defined Raster Functions can be selected or custom functions created.Available renderings include: Agriculture with DRA, Bathymetric with DRA, Color-Infrared with DRA, Natural Color with DRA, Short-wave Infrared with DRA, Geology with DRA, NDMI Colorized, Normalized Difference Built-Up Index (NDBI), NDWI Raw, NDWI - with VRE Raw, NDVI – with VRE Raw (NDRE), NDVI - VRE only Raw, NDVI-Raw, Normalized Burn Ratio, NDVI Colormap.Multispectral Bands BandDescriptionWavelength (µm)Resolution (m)1Coastal aerosol0.433 - 0.453602Blue0.458 - 0.523103Green0.543 - 0.578104Red0.650 - 0.680105Vegetation Red Edge0.698 - 0.713206Vegetation Red Edge0.733 - 0.748207Vegetation Red Edge0.773 - 0.793208NIR0.785 - 0.900108ANarrow NIR0.855 - 0.875209Water vapour0.935 - 0.9556010SWIR – Cirrus1.365 - 1.3856011SWIR-11.565 - 1.6552012SWIR-22.100 - 2.28020Additional Notes Overviews exist with a spatial resolution of 150m and are updated every quarter based on the best and latest imagery available at that time.To work with source images at all scales, the ‘Lock Raster’ functionality is available.NOTE: ‘Lock Raster’ should only be used on the layer for short periods of time, as the imagery and associated record Object IDs may change daily.This ArcGIS Server dynamic imagery layer can be used in Web Maps and ArcGIS Desktop as well as Web and Mobile applications using the REST based Image services API.Images can be exported up to a maximum of 4,000 columns x 4,000 rows per request.Data SourceSentinel-2 imagery is the result of close collaboration between the (European Space Agency) ESA, the European Commission and USGS. Data is hosted by the Amazon Web Services as part of their Registry of Open Data. Users can access the imagery from Sentinel-2 on AWS , or alternatively access Sentinel2Look Viewer, EarthExplorer or the Copernicus Open Access Hub to download the scenes. For information on Sentinel-2 imagery, see Sentinel-2.