A Normalized Difference Vegetation Index (NDVI) was applied to the source NAIP 2022 60cm imagery. NDVI=(NearIR-Red)/(NearIR+Red). The color ramp (produced by ESRI) goes from brown (less healthy vegetation) to red to green (healthier vegetation or more "greenness").This service is offered by the California Department of Fish and Wildlife (CDFW). For more information about CDFW map services, please visit: https://wildlife.ca.gov/Data/GIS/Map-Services

This dataset contains mean Normalized Difference Vegetation Index (NDVI) values from 1982 to 2018 and their future predictions based on CHELSA bioclimatic variables, specifically for the region of Türkiye. The data is provided in .asc format and includes both historical and projected NDVI values under different climate scenarios.

Contents:

• Historical NDVI Data (1982-2018): Mean NDVI values derived from remote sensing data.
• Future NDVI Predictions: NDVI projections for the periods 2011-2040, 2041-2070, and 2071-2100 under three Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP3-7.0, and SSP5-8.5.

Methodology:

1. Model Training:
   • A Random Forest Regressor was used to model the relationship between NDVI and the selected bioclim variables.
   • The model achieved an R² of 0.9341, Mean Absolute Error of 0.0275, and Root Mean Squared Error of 0.0499.
2. Future Predictions:
   • Future NDVI values were predicted using the trained model and future CHELSA bioclim projections.
   • Predictions were made for three future periods (2011-2040, 2041-2070, 2071-2100) under three SSPs (SSP1-2.6, SSP3-7.0, SSP5-8.5).

Data Specifications:

• Extent: Covers the geographical area of Türkiye and adjacents.

Sources:

• NDVI Data:
  • Ma, Z., Dong, C., Lin, K., Yan, Y., Luo, J., Jiang, D., & Chen, X. (2022). A Global 250-m Downscaled NDVI Product from 1982 to 2018. Remote Sensing, 14(15), 3639.
• CHELSA Bioclim Data:
  • Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017). Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122
  • Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4

This layer includes Landsat 8 and 9 imagery rendered on-the-fly as NDVI Colorized for use in visualization and analysis. This layer is time enabled and includes a number of band combinations and indices rendered on demand. The imagery includes eight multispectral bands from the Operational Land Imager (OLI) and two bands from the Thermal Infrared Sensor (TIRS). It is updated daily with new imagery directly sourced from the USGS Landsat collection on AWS.Geographic CoverageGlobal Land Surface.Polar regions are available in polar-projected Imagery Layers: Landsat Arctic Views and Landsat Antarctic Views.Temporal CoverageThis layer is updated daily with new imagery.Working in tandem, Landsat 8 and 9 revisit each point on Earth's land surface every 8 days.Most images collected from January 2015 to present are included.Approximately 5 images for each path/row from 2013 and 2014 are also included.Product LevelThe Landsat 8 and 9 imagery in this layer is comprised of Collection 2 Level-1 data.The imagery has Top of Atmosphere (TOA) correction applied.TOA is applied using the radiometric rescaling coefficients provided the USGS.The TOA reflectance values (ranging 0 – 1 by default) are scaled using a range of 0 – 10,000.Image Selection/FilteringA number of fields are available for filtering, including Acquisition Date, Estimated Cloud Cover, and Product ID.To isolate and work with specific images, either use the ‘Image Filter’ to create custom layers or add a ‘Query Filter’ to restrict the default layer display to a specified image or group of images.Visual RenderingDefault rendering is NDVI Colorized, calculated as (b5 - b4) / (b5 + b4) with a colormap applied.Raster Functions enable on-the-fly rendering of band combinations and calculated indices from the source imagery.The DRA version of each layer enables visualization of the full dynamic range of the images.Other pre-defined Raster Functions can be selected via the renderer drop-down or custom functions can be created.This layer is part of a larger collection of Landsat Imagery Layers that you can use to perform a variety of mapping analysis tasks.Pre-defined functions: Natural Color with DRA, Agriculture with DRA, Geology with DRA, Color Infrared with DRA, Bathymetric with DRA, Short-wave Infrared with DRA, Normalized Difference Moisture Index Colorized, NDVI Raw, NDVI Colorized, NBR Raw15 meter Landsat Imagery Layers are also available: Panchromatic and Pansharpened.Multispectral BandsThe table below lists all available multispectral OLI bands. NDVI Colorized consumes bands 4 and 5.BandDescriptionWavelength (µm)Spatial Resolution (m)1Coastal aerosol0.43 - 0.45302Blue0.45 - 0.51303Green0.53 - 0.59304Red0.64 - 0.67305Near Infrared (NIR)0.85 - 0.88306SWIR 11.57 - 1.65307SWIR 22.11 - 2.29308Cirrus (in OLI this is band 9)1.36 - 1.38309QA Band (available with Collection 1)*NA30*More about the Quality Assessment BandTIRS BandsBandDescriptionWavelength (µm)Spatial Resolution (m)10TIRS110.60 - 11.19100 * (30)11TIRS211.50 - 12.51100 * (30)*TIRS bands are acquired at 100 meter resolution, but are resampled to 30 meter in delivered data product.Additional Usage NotesImage exports are limited to 4,000 columns x 4,000 rows per request.This dynamic imagery layer can be used in Web Maps and ArcGIS Pro as well as web and mobile applications using the ArcGIS REST APIs.WCS and WMS compatibility means this imagery layer can be consumed as WCS or WMS services.The Landsat Explorer App is another way to access and explore the imagery.This layer is part of a larger collection of Landsat Imagery Layers.Data SourceLandsat imagery is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). Data is hosted by the Amazon Web Services as part of their Public Data Sets program.For information, see Landsat 8 and Landsat 9.

A Normalized Difference Vegetation Index (NDVI) was applied to the source NAIP 2018 imagery. NDVI=(NearIR-Red)/(NearIR+Red). The color ramp (produced by ESRI) goes from brown (less healthy vegetation) to red to green (healthier vegetation or more "greenness").This service is offered by the California Department of Fish and Wildlife (CDFW). For more information about CDFW map services, please visit: https://wildlife.ca.gov/Data/GIS/Map-Services

A Normalized Differential Vegetation Index (NDVI) was applied to the source 2005 1-meter resolution color infrared (CIR) imagery. NDVI=(NearIR-Red)/(NearIR+Red). The color ramp (produced by ESRI) goes from brown (less healthy vegetation) to red to green (healthier vegetation or more "greenness"). Lack of statewide color balancing of the source CIR imagery shows inconsistencies in NDVI results between adjacent areas. No access constraints, but there are use constraints (see source product metadata).

The source color infrared (CIR) imagery was acquired during NAIP 2005 flights. The imagery was purchased from the North West Group (NWG) by three state agencies (California Dept. of Fish and Game, California Dept. of Transportation, and California Dept. of Water Resources). No access constraints, but there are use constraints. CIR coverage was not available in all areas. THIS IMAGERY IS NOT A NAIP PRODUCT. Band1=NearIR, Band2=R, Band3=G.

This service is offered by the California Department of Fish and Wildlife (CDFW). For more information about CDFW map services, please visit: https://wildlife.ca.gov/Data/GIS/Map-Services

This Normalized Difference Vegetation Index (NDVI) layer features recent high-resolution (1-meter or better) aerial imagery for the continental United States, made available by the USDA Farm Production and Conservation Business Center (FPAC). The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental United States. Approximately half of the US is collected each year and each state is typically collected every other year.This imagery layer is updated annually as new imagery is made available. The NAIP program aims to make the imagery available to governmental agencies and to the public within a year of collection. The imagery is published in 4-bands (Red, Green, Blue, and Near Infrared) where available. Additional NAIP renderings include Natural Color and Color Infrared. Key PropertiesGeographic Coverage: Continental United States (Hawaii and Puerto Rico available for some years)Temporal Coverage: 2010 to 2023Spatial Resolution: 0.3-meter to 1-meterRevisit Time: Typically every other yearSource Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Analysis: Optimized for analysisMultispectral Bands:BandDescriptionSpatial Resolution (m)1Red0.3 - 12Green0.3 - 13Blue0.3 - 14Near Infrared0.3 - 1 Usage Tips and Best PracticesProcessing TemplatesThis layer includes a number of preconfigured processing templates (raster function templates) to provide on-the-fly data rendering and calculations for visualization and analysis. Each processing template includes labels and descriptions to characterize the intended usage. This may include for visualization, for analysis, or for both visualization and analysis. VisualizationThe default rendering on this layer is NDVI ((Red - Near Infrared) / (Red + Near Infrared)).If natural color visualization is your primary use case for NAIP, you might consider using the NAIP Imagery tile layer for optimal display performance.There are a number of on-the-fly renderings/processing templates designed specifically for data visualization.By default, the most recent imagery available for a given area is prioritized and dynamically fused into a single mosaicked image layer. To discover and isolate specific images for visualization in Map Viewer, try using the Image Collection Explorer. AnalysisIn order to leverage the optimization for analysis, the capability must be enabled by your ArcGIS organization administrator. More information on enabling this feature can be found in the ‘Regional data hosting’ section of this help doc.Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See this group for a complete list of imagery layers optimized for analysis.Using the "None" processing template option as input to analysis provides all bands with raw pixel values and is recommended for many use cases. Otherwise, only processing templates that include a "for analysis" designation should be used as input to analysis.Prior to running analysis, users should always provide some form of data selection with either a layer filter (e.g. for a specific year, year range, state, etc.) or by selecting specific images. To discover and isolate specific images for analysis in Map Viewer, try using the Image Collection Explorer. Data SourceNAIP imagery is credited to the United States Department of Agriculture (USDA) Farm Production and Conservation Business Center (FPAC). All imagery in this layer was is sourced from the NAIP Registry of Open Data on AWS.

A Normalized Difference Vegetation Index (NDVI) was applied to the source NAIP 2014 imagery. NDVI=(NearIR-Red)/(NearIR+Red). The color ramp (produced by ESRI) goes from brown (less healthy vegetation) to red to green (healthier vegetation or more "greenness").This service is offered by the California Department of Fish and Wildlife (CDFW). For more information about CDFW map services, please visit: https://wildlife.ca.gov/Data/GIS/Map-Services

Normalized difference vegetation index (NDVI) produced from the 2005 Landsat Thematic Mapper (TM) image. NDVI is a means of monitoring density and vigour of green vegetation growth using the spectral reflectivity of solar radiation. It is computed as follows: (NIR-RED) / (NIR+RED), where NIR (Near Infra-Red) is the TM band 4 (0.76-0.9 micrometers) and RED is band 3 (0.78-0.82 micrometers).

Normalized Difference Vegetation Index (NDVI) from MODIS data for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023). Source data: - MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid (MOD13A2 v061): https://lpdaac.usgs.gov/products/mod13a2v061/ The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MOD13A2) Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle and the highest NDVI/EVI value. For the time period January 2019 - December 2023, the NDVI layer of the original data has been processed. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layers and appear as "no data". These 16-Day data are then aggregated to monthly temporal resolution using the maximum and reprojected to Latitude-Longitude/WGS84. File naming: ndvi_filt_YYYY_MM_01T00_00_00.tif e.g.: ndvi_filt_2023_12_01T00_00_00.tif The date within the filename is year and month of aggregated timestamp. Pixel values: NDVI * 10000 Scaled to Integer, example: value 6473 = 0.6473 Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: north: 28N south: 14N west: 18W east: 4W Temporal extent: January 2019 - December 2023 Spatial resolution: 30 arc seconds (approx. 1000 m) Temporal resolution: monthly Software used: GRASS GIS 8.3.2 Format: GeoTIFF Original dataset license: All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For more information see: https://lpdaac.usgs.gov/products/mod13a2v061/ Processed by: mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/) Contact: mundialis GmbH & Co. KG, info@mundialis.de Acknowledgements: This study was partially funded by EU grant 874850 MOOD. The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the European Commission.

Normalized difference vegetation index (NDVI) produced from the 2005 Landsat Thematic Mapper (TM) image. NDVI is a means of monitoring density and vigour of green vegetation growth using the spectral reflectivity of solar radiation. It is computed as follows: (NIR-RED) / (NIR+RED), where NIR (Near Infra-Red) is the TM band 4 (0.76-0.9 micrometers) and RED is band 3 (0.78-0.82 micrometers).

Normalized difference vegetation index (NDVI) produced from the 2000 Enhanced Landsat Thematic Mapper(ETM) image. NDVI is a means of monitoring density and vigour of green vegetation growth using the spectral reflectivity of solar radiation. It is computed as follows: (NIR-RED) / (NIR+RED), where NIR (Near Infra-Red) is the ETM band 4 (0.76-0.9 micrometers) and RED is band 3 (0.78-0.82 micrometers).

Crop growth and yield monitoring over agricultural fields is an essential procedure for food security and agricultural economic return prediction. The advances in remote sensing have enhanced the process of monitoring the development of agricultural crops and estimating their yields. Therefore, remote sensing and GIS techniques were employed, in this study, to predict potato tuber crop yield on three 30 ha center pivot irrigated fields in an agricultural scheme located in the Eastern Region of Saudi Arabia. Landsat-8 and Sentinel-2 satellite images were acquired during the potato growth stages and two vegetation indices (the normalized difference vegetation index (NDVI) and the soil adjusted vegetation index (SAVI)) were generated from the images. Vegetation index maps were developed and classified into zones based on vegetation health statements, where the stratified random sampling points were accordingly initiated. Potato yield samples were collected 2–3 days prior to the harvest time and were correlated to the adjacent NDVI and SAVI, where yield prediction algorithms were developed and used to generate prediction yield maps. Results of the study revealed that the difference between predicted yield values and actual ones (prediction error) ranged between 7.9 and 13.5% for Landsat-8 images and between 3.8 and 10.2% for Sentinel-2 images. The relationship between actual and predicted yield values produced R2 values ranging between 0.39 and 0.65 for Landsat-8 images and between 0.47 and 0.65 for Sentinel-2 images. Results of this study revealed a considerable variation in field productivity across the three fields, where high-yield areas produced an average yield of above 40 t ha-1; while, the low-yield areas produced, on the average, less than 21 t ha-1. Identifying such great variation in field productivity will assist farmers and decision makers in managing their practices.

A Normalized Difference Vegetation Index (NDVI) was applied to the source NAIP 2010 imagery. NDVI=(NearIR-Red)/(NearIR+Red). The color ramp (produced by ESRI) goes from brown (less healthy vegetation) to red to green (healthier vegetation or more "greenness").

MODIS NDVI data was extracted from MODIS product type MOD13Q1. MODIS/Terra Vegetation Indices 16-Day L3 Global 250m covers Central Kenya. Data was caputred on 2007-10-16.

Climate change projections show that temperature and precipitation increases can alter the exchange of greenhouse gases between the atmosphere and high latitude landscapes, including their freshwaters. Dissolved organic carbon (DOC) plays an important role in greenhouse gas emissions, but the impact of catchment productivity on DOC release to subarctic waters remains poorly known, especially at regional scales. We test the hypothesis that increased terrestrial productivity, as indicated by the normalized difference vegetation index (NDVI), generates higher stream DOC concentrations in the Stordalen catchment in subarctic Sweden. Furthermore, we aimed to determine the degree to which other generic catchment properties (elevation, slope) explain DOC concentration, and whether or not land cover variables representing the local vegetation type (e.g., mire, forest) need to be included to obtain adequate predictive models for DOC delivered into rivers. We show that the land cover type, especially the proportion of mire, played a dominant role in the catchment’s release of DOC, while NDVI, slope, and elevation were supporting predictor variables. The NDVI as a single predictor showed weak and inconsistent relationships to DOC concentrations in recipient waters, yet NDVI was a significant positive regulator of DOC in multiple regression models that included land cover variables. Our study illustrates that vegetation type exerts primary control in DOC regulation in Stordalen, while productivity (NDVI) is of secondary importance. Thus, predictive multiple linear regression models for DOC can be utilized combining these different types of explanatory variables.

Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from MODIS data for Europe at 1 km resolution.

Source data: - MODIS/Terra Vegetation Indices 16-Day L3 Global 500 m SIN Grid (MOD13A1 v006): https://lpdaac.usgs.gov/products/mod13a1v006/ - MODIS/Aqua Vegetation Indices 16-Day L3 Global 500 m SIN Grid (MYD13A1 v006): https://lpdaac.usgs.gov/products/myd13a1v006/

The MOD/MYD13A1 Version 6 product provide Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value.

For the time periods October 2016 - March 2017 and August 2020 - April 2021, the original data has been reprojected to ETRS89-extended / LAEA Europe and aggregated to a 1 km grid. The temporal resolution is 16 days. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layers and appear as "no data".

File naming: productCode.acquisitionDate[A (YYYYDDD)]_mosaic_spatialResolution_frequency_VI.tif example: MOD13A1.A2020305_mosaic_1000m_16_days_NDVI.tif

The date is Year and Day of Year.

Values are NDVI/EVI * 10000. Example: Value 6473 = 0.6473

Projection + EPSG code: ETRS89 / LAEA Europe (EPSG:3035) (EPSG: 3035)

Spatial extent: north: 72N south: 30S west: -52W east: 49E

Spatial resolution: 1 km

Temporal resolution: 16 days

Pixel values: NDVI/EVI * 10000 (scaled to Integer; example: value 6473 = 0.6473)

Software used: GRASS GIS 8.0

Original dataset license: All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For more information see: https://lpdaac.usgs.gov/products/myd13a1v006/

Processed by: mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/)

The Crop Condition Assessment Program (CCAP) is developed and maintained by the Remote Sensing and Geospatial Analysis Section (RSGA) within the Agriculture Division. The CCAP combines remote sensing, GIS, and the Internet to provide reliable, objective, and timely information on crop and pasture/rangeland conditions using a mapping application for the whole Canadian agricultural area and the northern portion of the United States.

Since the 2010 growing season, the CCAP has been enhanced with the integration of MODerate-resolution Imaging Spectoradiometer (MODIS) data (250-meter resolution). This detector captures two spectral bands (red and infrared) that have proven to be extremely useful for vegetation monitoring to produce the Normalized Difference Vegetation Index (NDVI). CCAP allowed the visualization of MODIS data since 2017 for most of the growing weeks in Canada, between Julian weeks 15 and 37.

This dataset give access to 18 years of MODIS images in GeoTIF format and cover all the crops area during the crops growing season.

GIS files pctcanloss from deltaNDVI

This dataset is a normalised difference vegetation index (NDVI) representing vegetation 'greenness' across the City of Port Adelaide Enfield Council. It is based on 4-band multispectral Beijing-3 (BJ3A1) satellite imagery captured between January and February 2022 and has a spatial resolution of 0.5m.This data can be used to describe the 'greenness' across the Council for the purposes of urban planning, conservation and decision making.

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.