https://opensource.org/licenses/MIT RESEO - REmote SEnsing Ontology Aldana-Martín, J. F., García-Nieto, J., Roldan-Garcia, M. D. M., & Aldana-Montes, J. F. (2021). Semantic modelling of Earth Observation remote sensing [Computer software]. doi: https://doi.org/10.1016/j.eswa.2021.115838 2021-09-24T23:59:59 Khaos Research Group, University of Málaga Earth Observation (EO) based on Remote Sensing (RS) is gaining importance nowadays, since it offers a well-grounded technological framework for the development of advanced applications in multiple domains, such as climate change, precision agriculture, smart urbanism, safety, and many others. This promotes the continuous generation of data-driven software facilities oriented to advanced processing, analysis and visualization, which often offer enhanced computing capabilities. Nevertheless, the development of knowledge-driven approaches is still an open challenge in remote sensing, besides they provide human experts with domain knowledge representation, support for data standardization and semantic integration of sources, which indeed enhance the construction of advanced on-top applications. To this end, the use of ontologies and web semantic technologies have shown high success in knowledge representation in many fields, in which the Earth Observation is not an exception. However, as argued by the research community, there is large room for improvement in the specific case of remote sensing, where ontologies that consider the special nature and structure of different satellital and airborne data products are demanded. This article addresses, in first instance, part of this need by proposing a semantic model for the consolidation, integration, reasoning and linking of data (and meta-data), in the context of satellital remote sensing products for EO. With this objective, an OWL ontology has been developed and an RDF repository has been generated to allow advanced SPARQL querying. Although the proposal has been designed to consider remote sensing data products in general, the current study is mainly focused on the Sentinel 2 satellite mission from the Copernicus Programme of the European Space Agency (ESA). Four different use cases are showcased to check potentials of the proposed semantic model in terms of ontology integration, federated querying, data analysis and reasoning. 2022-01-01T23:59:59 published Expert Systems with Applications (c) Khaos Research Group, University of Málaga Remote Sensing Ontology https://doi.org/10.1016/j.eswa.2021.115838 reseo https://w3id.org/reseo RESEO - REmote SEnsin Ontology http://khaos.uma.es/green-senti/reseo/0.1 0.1 http://khaos.uma.es/green-senti/reseo/0.1 1.0 Algo (normalmente un producto) tiene una fuente de datos Something (usually a product) has data source has data source tiene fuente de datos Algo (normalmente un producto) tiene una fecha Something (usually a product) has date has date tiene fecha has nearest station tiene estación más cercana Algo (normalmente una snapshot) tiene un producto Something (usually a snapshot) has product has product tiene producto Algo (normalmente un producto) tiene una escena Something (usually a product) has scene has scene tiene escena has sensor has value Es fuente de datos de algo (normalmente un producto) Is data source of something (usually a product) es fuente de datos de is data source of Es fecha de algo (normalmente un producto) Is date of something (usually a product) es fecha de is date of Es producto de algo (normalmente una snaphsot) Is product of something (usually a snapshot) es producto de is product of Es escena de algo (normalmente un producto) Is scene of something (usually a product) es escena de is scene of part of product part of raster image processed by Propiedades de snapshot Snapshot properties Bare soil percentage Example: 35.747452 Porcentaje de suelo desnudo Ejemplo: 35.747452 Bare soil percentage Porcentaje de suelo desnudo Acquisition start date time, i.e. time of the oldest data within the product. Format standard: YYYY-MM-DD hh:mm Example: 2018-06-28 10:56 Fecha y hora del comienzo de la adquisición. La fecha y hora de los datos más antiguos del producto. Formato estándar: YYYY-MM-DD hh:mm Ejemplo:2018-06-28 10:56 Begin position Posición inicial Cloud coverage (percent) of a WRS scene. Values: 0.00 - 100.00 -1 = Cloud cover not calculated or assessed Cloud cover Cobertura de nubes All sensors = Cloud coverage over land (percent) assigned to a WRS scene. Values: -1 = Cloud cover land not calculated or assessed. Cloud cover land Tierra cuberta por nubes Cloud cover percentage. Example: 3.8478 Porcentaje de cobertura de nubes. Ejemplo: 3.8478 Cloud cover percentage Porcentaje cubierto de nubes Landsat Collection 1 is organized by a tiered inventory structure to indicate the quality and level of processing of the data. Values: T1 = Tier 1 is the highest available quality and processing level. This category is suitable for time-series analysis across the different Landsat sensors. T2 = Tier 2 scenes contain significant cloud cover and have insufficient ground control to generate precision and terrain corrected products. RT = Real-Time indicates initial processing with additional processing required to achieve Tier 1 or Tier 2. Categoría de colección Collection category Collection number Landsat Collections provides a quality controlled selection of radiometrically calibrated and consistently geolocated data with sufficiently geographically aligned pixels to support time series analysis through the full Landsat record. Número de colección column Datatake start date time, i.e time of the oldest data within the datatake. Format standard: YYYY-MM-DDThh:mm:ss.mmmZ Example: 2018-06-28T10:56:19.024Z Inicio de la detección de toma de datos. La fecha y hora de los datos más antiguos dentro de la toma de datos. Formato estándar: YYYY-MM-DDThh:mm:ss.mmmZ Data take sensing start inicio de la detección de toma de datos Data type identifier string used to create the L-1 product. Values: L1G = L1G Data L1GT = L1Gt Data L1T = L1T Data Data Type Tipo de datos Year, month and day that this scene was imaged. Format: YYYY/MM/DD Date acquired Fecha de adquisición Datum used in creating the image. Values: WGS84 = World Geodetic System 1984 Datum Sistema de referencia Acquisition end date time, i.e. time of the most recent data within the product. Format standard: YYYY-MM-DDThh:mm:ss.mmmZ Example: 2018-06-28T10:56:19.024Z Fecha y hora del final de la adquisición. La fecha y hora de los datos más recientes del producto. Formato estándar: YYYY-MM-DDThh:mm:ss.mmmZ Ejemplo:2018-06-28T10:56:19.024Z End position Posición final EVI (Enhaced vegetation index) Calculation for Sentinel 2 product: 2.5 * (B08 - B04) / ((B08 + 6 * B04 - 7.5 * B02) + 1) This value is the mean of the calculated image Example: 0.27742198 EVI (Índice de vegetación mejorado) Calculo para productos Sentinel 2: 2.5 * (B08 - B04) / ((B08 + 6 * B04 - 7.5 * B02) + 1) Este valor es la media de la imagen calculada Ejemplo: 0.27742198 EVI EVI Image with the value of EVI for each pixel Imagen con el valor de EVI para cada pixel Image EVI Imagen EVI Fecha del archivo File date Nombre del paquete de archivos, con extensión ‘.SAFE’. Ejemplo: S2B_MSIL2A_20180628T105619_N0208_R094_T30SUF_20180628T151421.SAFE Package file name, with extension ‘.SAFE’. Example: S2B_MSIL2A_20180628T105619_N0208_R094_T30SUF_20180628T151421.SAFE Filename Nombre de archivo Boundaries of the frame in a simplified form.The coordinates shall be declared in a counter-clockwise order. Example: POLYGON ((-5.248379903953922 37.02527578563511,-4.014302538465313 37.042247242200354,-4.001451962964202 36.05254100905861,-5.219917191699283 36.03616769808406,-5.248379903953922 37.02527578563511)) Contorno del marco en formato simplificado. Las coordenadas deben ser declaradas en orden inverso a la agujas del reloj. Ejemplo: POLYGON ((-5.248379903953922 37.02527578563511,-4.014302538465313 37.042247242200354,-4.001451962964202 36.05254100905861,-5.219917191699283 36.03616769808406,-5.248379903953922 37.02527578563511)) Contorno Footprint formula Boundaries of the frame. In addition to the constraints imposed by the GML type for linear rings, the coordinates shall be declared in a counter-clockwise order. Example: <gml:Polygon srsName="http://www.opengis.net/gml/srs/epsg.xml#4326" xmlns:gml="http://www.opengis.net/gml"> <gml:outerBoundaryIs> <gml:LinearRing> <gml:coordinates>37.02527578563511,-5.248379903953922 37.042247242200354,-4.014302538465313 36.05254100905861,-4.001451962964202 36.03616769808406,-5.219917191699283 37.02527578563511,-5.248379903953922</gml:coordinates> </gml:LinearRing> </gml:outerBoundaryIs> </gml:Polygon> Contorno del marco. Además de las restrincciones impuestas por el tipo GML para anillos lineales, las coordenadas deben ser declaradas en orden inverso a la agujas del reloj. Ejemplo: <gml:Polygon srsName="http://www.opengis.net/gml/srs/epsg.xml#4326" xmlns:gml="http://www.opengis.net/gml"> <gml:outerBoundaryIs> <gml:LinearRing> <gml:coordinates>37.02527578563511,-5.248379903953922 37.042247242200354,-4.014302538465313 36.05254100905861,-4.001451962964202 36.03616769808406,-5.219917191699283 37.02527578563511,-5.248379903953922</gml:coordinates> </gml:LinearRing> </gml:outerBoundaryIs> </gml:Polygon> Contorno GML GML footprint High proba clouds percentage Example: 0.232073 Porcentaje de nubes altas Ejemplo: 0.232073 High proba clouds percentage Porcentaje de nubes altas Identificador de cuadrante para ordenarlos horizontalemente. Ejemplo: SF30U Tile identifier to order tiles horizontally. Example: SF30U Identificador ordenado de cuadrante Ordered tile identifier Identificador Ejemplo: S2A_MSIL1C_20180603T110041_N0206_R094_T30SUF_20180603T130559 Identifier Example: S2A_MSIL1C_20180603T110041_N0206_R094_T30SUF_20180603T130559 Identificador Identifier Composite image quality for the bands. For Landsat 8 or 9, if scene is OLI or OLI_TIRS combined, value will reflect OLI quality score. If TIRS, the TIRS quality score will be used Values: 9 = Best 0 = Worst -1 = Image quality not calculated or assessed Calidad de imagen OLI Image quality OLI Composite image quality for the bands. For Landsat 8 or 9, if scene is OLI or OLI_TIRS combined, value will reflect OLI quality score. If TIRS, the TIRS quality score will be used Values: 9 = Best 0 = Worst -1 = Image quality not calculated or assessed Calidad de imagen TIRS Image quality TIRS Representa la fecha y hora en la que el producto fue indexado por el Servicio de base de datos DHuS. La hora está en UTC. Formato estándar: YYYY-MM-DD hh:mm Example: 2018-06-28 16:38 Represents the time when the product is indexed in the DHuS Service databases. Time is in UTC. Format standard: YYYY-MM-DD hh:mm Example: 2018-06-28 16:38 Fecha de ingestión Ingestion date Nombre del instrumento usado para la recogida de datos Ejemplo: Multi-Spectral Instrument The instrument name used for acquiring the data. Example: Multi-Spectral Instrument Instrument name Nombre del instrumento Abreviación del nombre del instrumento usado para la recogida de datos Example: MSI The short name of the instrument used for acquiring the data. Example: MSI Abrevación de nombre del instrumento Instrument short name Landsat 8 product properties Propiedades de producto Landsat 8 The naming convention of each L7 ETM+ Collection 1 Level-1 image is based on acquisition and processing parameters. Format: LXSS_LLLL_PPPRRR_YYYYMMDD_yyyymmdd_CC_TX L = Landsat X = Sensor (E = Enhanced Thematic Mapper Plus) SS = Satellite (07 = Landsat 7) LLLL = Processing Correction Level (L1TP = precision and terrain, L1GT = systematic terrain, L1GS = systematic) PPP = WRS Path RRR = WRS Row YYYYMMDD = Acquisition Date expressed in Year, Month, Day yyyymmdd = Processing Date expressed in Year, Month, Day CC = Collection Number (01) TX = Collection Category (RT = Real Time, T1 = Tier 1, T2 = Tier 2) Example: LE07_L1TP_029030_20140715_20140805_02_T1 Identificador de producto Landsat Landsat product ID Landsat scene identifier. Format: LXSPPPRRRYYYYDDDGSIVV L = Landsat X = Sensor (T = TIRS, O = OLI, C = OLI_TIRS) S = Satellite PPP = WRS Path RRR = WRS Row YYYY = Year of Acquisition DDD = Day of Acquisition Year GSI = Ground Station Identifier VV = Version Example: LC82010342019039LGN00 Identificador de escenas Landsat Landsat scene ID Download link for the product from https://scihub.copernicus.eu Example: https://scihub.copernicus.eu/dhus/odata/v1/Products('95bec4b4-a2d4-4b6f-a7cd-1de5cb3e85b7')/$value Enlace de descarga para el producto en https://scihub.copernicus.eu Ejemplo: https://scihub.copernicus.eu/dhus/odata/v1/Products('95bec4b4-a2d4-4b6f-a7cd-1de5cb3e85b7')/$value Enlace de descarga del producto Product download link Enlace al producto en https://scihub.copernicus.eu Ejemplo: https://scihub.copernicus.eu/dhus/odata/v1/Products('95bec4b4-a2d4-4b6f-a7cd-1de5cb3e85b7')/ Link to the product from https://scihub.copernicus.eu Example: https://scihub.copernicus.eu/dhus/odata/v1/Products('95bec4b4-a2d4-4b6f-a7cd-1de5cb3e85b7')/ Enlace al producto Link to product Enlace al hdfs Ejemplo: hdfs://192.168.213.52:9000/smartcampus/products/S2A_MSIL2A_20170728T105621_N0205_R094_T30SUF_20170728T110406 Link to hdfs Example: hdfs://192.168.213.52:9000/smartcampus/products/S2A_MSIL2A_20170728T105621_N0205_R094_T30SUF_20170728T110406 Enlace hdfs Link hdfs Enlace a una imagen de baja resolución del producto en https://scihub.copernicus.eu Example: https://scihub.copernicus.eu/dhus/odata/v1/Products('95bec4b4-a2d4-4b6f-a7cd-1de5cb3e85b7')/Products('Quicklook')/$value Link to a low resolution image of the product on https://scihub.copernicus.eu Example: https://scihub.copernicus.eu/dhus/odata/v1/Products('95bec4b4-a2d4-4b6f-a7cd-1de5cb3e85b7')/Products('Quicklook')/$value Enlace al icono del producto Link to product icon Low proba clouds percentage Example: 1.868199 Porcentaje de nubes bajas Ejemplo: 1.868199 Low proba clouds percentage Porcentaje de nubes bajas L0Ra map projection selectively applied to HDTs based on geographic location. Used for processed archive data. Values: HOM = Hotine Oblique Mercator UTM = Universal Transverse Mercator Map projection Proyección sobre mapa Medium proba clouds percentage Example: 0.577722 Porcentaje de nubers medias Ejemplo: 0.577722 Medium proba clouds percentage Porcentaje de nubers medias GVMI (Global Vegetation Moisture Index) Calculation for Sentinel 2 product: (B8A - B11) / (B8A + B11) This value is the mean of the calculated image Example: -0.02705707 GVMI (Índice Global de humedad de Vegetación) Calculo par productos Sentinel2: (B8A - B11) / (B8A + B11) Este valor es la media de la imagen calculada Example: -0.02705707 MOISTURE MOISTURE Image with the value of MOISTURE for each pixel Imagen con el valor de MOISTURE para cada pixel Image MOISTURE Imagen MOISTURE NDSI (Normalized Difference Snow Index) Calculation for Sentinel 2 product: (B03 - B11) / (B03 + B11) This value is the mean of the calculated image Example: 0.0 NDSI (Índice de diferencia de nieve normalizado) Calculo para productos Sentinel 2: (B03 - B11) / (B03 + B11) Este valor es la media de la imagen calculada Ejemplo: 0.0 NDSI NDSI Image with the value of NDSI for each pixel Imagen con el valor de NDSI para cada pixel Image NDSI Imagen NDSI NDVI (Normalized Difference Vegetation Index) Calculation for Sentinel 2 product: (B08 - B04) / (B08 + B04) This value is the mean of the calculated image Example: 0.14262335 NDVI (Índice de diferencia de vegetación normalizado) Calculo para productos Sentinel 2: (B08 - B04) / (B08 + B04) Este valor es la media de la imagen calculada Ejemplo: 0.14262335 NDVI NDVI Image with the value of NDVI for each pixel Imagen con el valor de NDVI para cada pixel Image NDVI Imagen NDVI Percentage of area non vegetated Example: 33.812419 Porcentaje de área sin vegetación Ejemplo: 33.812419 Not vegetated percentage Porcentaje sin vegetación Dirección de la órbita durante la captura del producto. Puede ser 'ASCENDING' o 'DESCENDING' Ejemplo: 'ASCENDING' Orbit direccion while taking he product Can be either 'ASCENDING' or 'DESCENDING' Example: 'ASCENDING' Dirección de órbita Orbit direction Absolute orbit number to which the oldest data unit of the product refers Example: 5525 Número de órbita absoluto al que se refieren los datos más antiguos del producto Ejemplo: 5525 Número de órbita Orbit number Orientation used in creating the image. Values: NOMINAL = Nominal Path NORTH_UP = North Up TRUE_NORTH = True North USER = User Orientación Orientation Origin of the image Values: Example: "Image courtesy of the U.S. Geological Survey" Origen Origin Output format of the image. Values: GEOTIFF = Geostationary Earth Orbit Tagged Image File Format Formato de salida Output format Identifica unívocamente la plataforma. La sintaxis exacta y el espacio léxico de este identificador están definidos por el Centro Mundial de Datos para Información Satelital (WDC-SI), que depende del Centro Nacional de Datos de Ciencia Espacial de la NASA. Para cada plataforma, el identificador exacto está disponible en el nombre de la misión en la dirección: http://nssdc.gsfc.nasa.gov/nmc/sc-query.html. Ejemplo: 2015-028A Univocally identifies the platform. The exact syntax and lexical space of this identifier is defined by the World Data Center for Satellite Information (WDC-SI), which depends on the NASA’s National Space Science Data Center. For each platform, the exact identifier is available from the mission name at the address: http://nssdc.gsfc.nasa.gov/nmc/sc-query.html. Example: 2015-028A Identificador NSSDC de la plataforma Platform NSSDC identifier El nombre de la missión completo de la plataforma. Este nombre no es un identificador perfecto. Ejemplo: Sentinel-2 The mission full name of the platform. This name is not a perfect identifier. Example:Sentinel-2 Nombre de la plataforma Platform name El identificador serial de la plataforma es el número de sequencia de la plataforma junto con la missión Ejemplo: Sentinel-2A The platform serial identifier is the sequence number of the platform among the mission Example: Sentinel-2A Identificador serial de la plataforma Platform serial identifier La línea base del procesado Ejemplo: 2.08 The processing baseline Example: 2.08 Línea base de procesado Processing baseline El último nivel de procesamiento Ejemplo: Level-2A The level of the last processing Example: Level-2A Nivel de procesado Processing level Format of the product Example: SAFE Formato del producto Ejemplo: SAFE Format Formato Product type Example: S2MSI2A Tipo del producto Ejemplo: S2MSI2A Product type Tipo del producto Número de órbita absoluto al que se refieren los datos más antiguos del producto Ejemplo: 94 Relative orbit number to which the oldest data unit of the product refers Example: 94 Número de órbita relativo Relative orbit number row El nombre del archivo recibido por ESA para la acquisición continua de una imagen de un satélite Sentinel-2 en un modo MSI dado o una "toma de datos" Formato: GMMM_YYYYMMDDTHHMMSS_Affffff_Nxx.yy G = identificador de toma de datos MMM = ID de misión: S2A o S2B YYYYMMDDTHHMMSS = Fecha y hora de inicio de la acquisición Affffff = Número de órbita absoluto Nxx.yy = Versión del software del vendedor Ejemplo: GS2A_20180603T110041_015392_N02.06 The file name provided by ESA for the continuous acquisition of an image from a SENTINEL-2 satellite in a given MSI imaging mode or a "datatake". Format: GMMM_YYYYMMDDTHHMMSS_Affffff_Nxx.yy G = Datatake Identifier MMM = Mission ID: S2A or S2B YYYYMMDDTHHMMSS = Acquisition Start Date and Time Affffff = Absolute Orbit Number Nxx.yy = Vendor Software Version Example: GS2A_20180603T110041_015392_N02.06 Datatake identifier Identificador de toma de datos Modo del sensor Ejemplo: INS-NOBS Sensor mode Example: INS-NOBS Modo de operación del sensor Sensor operational mode Propiedades de producto Sentinel 2 Sentinel 2 product properties Size of the product Example: 634.50 MB Tamaño del producto Ejemplo: 634.50 MB Size Tamaño Percentage of snow ice Example: 0.00018899999999999999 Porcentaje de nieve hielo Ejemplo: 0.00018899999999999999 Porcentaje de nieve hielo Snow ice percentage Identificador de estación Station ID Original ground receiving station to which this data was downlinked. All data received from ground stations are archived at the USGS EROS unless otherwise noted. Maps showing these stations can be found at https://landsat.usgs.gov/about_ground_stations.php. Values: AAA = North America receiving site unknown AA0 = North America receiving site unknown ABG = Awutu Bawjiase, Ghana ABN = Abuja, Nigeria AGS = Poker Flats, Alaska, USA ASA = Alice Springs, Australia ASK = Astana, Kazakhstan ASN = Alice Springs, Australia ATG = Atlanta, Georgia, USA (test site) AWE = Aswan, Egypt BIK = Bishkek, Kyrgyzstan BJC = Beijing, China BKT = Bangkok, Thailand BOC = Bogatá, Colombia CBA = Canberra, Australia CHM = Chetumal, Mexico CLT = Chung-Li, Taiwan CNB = Cuiabá, Brazil COA = Còrdoba, Argentina CPE = Cotopaxi, Ecuador CUB = Cuiabá, Brazil DJK = Daejeon, Korea DKI = Parepare, Indonesia DUB = Dubai, United Arab Emirates DVU = Dunaivtsi, Ukraine DWA = Darwin, Australia EDC = Sioux Falls, South Dakota, USA (aka LGS) EV1 = Eagle Vision 1 EV3 = Eagle Vision 3 EV4 = Eagle Vision 4 FAK = Fairbanks, Alaska, USA FFF = Foreign (non-North American) Receiving Site FUI = Fucino, Italy GDS = Goldstone, California, USA GLC = Gilmore Creek, Alaska, USA GMD = Greenbelt, Maryland, USA GNC = Gatineau, Canada HAJ = Hatoyama, Japan HCA = Datron testing in California, USA HIJ = Hiroshima, Japan HCV = Ho Chi Minh City, Vietnam HLV = Hoa Lac, Vietnam HNV = Hanoi, Vietnam HOA = Hobart, Australia IKR = Irkutsk, Russia ISP = Islamabad, Pakistan IVC = Inuvik, Canada JSA = Hartebeesthoek, South Africa (aka Johannesburg) KHC = KaShi, China KIS = Kiruna, Sweden KLM = Kuala Lumpur, Malaysia KMC = Kunming, China KUJ = Kumamoto, Japan LBG = Libreville, Gabon LGN = Landsat Ground Network (Landsat 8) LGS = Sioux Falls, South Dakota, USA (aka EDC) LUZ = Lusaka, Zambia MCM = Mexico City, Mexico MGR = Magadan, Russia MGS = McMurdo, Antarctica MLK = Malindi, Kenya MOR = Moscow, Russia MPF = Montpelier, France MPS = Maspalomas, Spain MTI = Matera, Italy NOK = Norman, Oklahoma, USA NPA = North Pole, Alaska, USA NP1 = North Pole, Alaska, USA NP2 = North Pole, Alaska, USA NP3 = North Pole, Alaska, USA NP4 = North Pole, Alaska, USA NP5 = North Pole, Alaska, USA NRK = Nairobi, Kenya NSG = Neustrelitz, Germany NSN = Neustrelitz, Germany NWN = Noordwijk, Netherlands OHA = O’Higgins, Antarctica PAC = Prince Albert, Canada PFS = Poker Flats, Alaska, USA PF1 = Poker Flats, Alaska, USA PF2 = Poker Flats, Alaska, USA PUC = Punto Arenas, Chile RPI = Rumpin, Indonesia RSA = Riyadh, Saudi Arabia SAC = Santiago, Chile SEK = Seoul, South, Korea SGI = Shadnagar, India SGP = Singapore SGS = Svalbard, Norway SG1 = Svalbard, Norway SNC = SanYa, China SRT = Si Racha, Thailand TFT = Taipei, Taiwan (fix Location) TGS = Transportable Ground Station TMT = Taipei, Taiwan (mobile location) TOF = Toulouse, France UHI = Honolulu, Hawaii, USA ULK = Ulsan, South Korea ULM = Ulan Bator, Mongolia UPR = Mayagüez, Puerto Rico WPS = Wallops Island, Virginia, USA XX0 = Receiving station unknown XXX = Receiving station unknown Identificador de estación Station identifier Resumen del producto. incluye fecha, instrumento, modo del sensor, satélite y tamaño Ejemplo: Date: 2017-09-26T10:58:11.026Z, Instrument: MSI, Mode: , Satellite: Sentinel-2, Size: 699.32 MB Summary of the product. Includes date, instrument, sensor mode, satellite and size Example: Date: 2017-09-26T10:58:11.026Z, Instrument: MSI, Mode: , Satellite: Sentinel-2, Size: 699.32 MB Resumen Summary Extract tile identifier (MGRS encoded). If more than one tile is present in the user product, first one is selected. Example: 30SUF Extraer identificador de cuadrante (codificado MGRS). Si hay más de un cuadrante en el producto del usuario, se selecciona el primero Ejemplo: 30SUF Identificador de cuadrante Tile identifier Title Example: S2A_MSIL2A_20170628T105651_N0205_R094_T30SUF_20170628T110445 Título Ejemplo: S2A_MSIL2A_20170628T105651_N0205_R094_T30SUF_20170628T110445 Title Título TC (Color verdadero) Calculado para Sentinel 2 usando B04 para el valor rojo, B03 para el valor verde y B02 para el valor azul de cada pixel Este valor es la media de la imagen calculada Ejemplo: 56.023668417104275 TC (True Color) Calculated using B04 as red value, B03 as green value and B02 as blue value for each pixel This value is the mean of the calculated image Example: 56.023668417104275 TC TC Image with the value of TC for each pixel Imagen con el valor de TC para cada pixel Image TC Imagen TC Porcentaje sin clasificar Ejemplo: 0.377401 Unclassified percentage Example: 0.377401 Porcentaje sin clasificar Unclassified percentage UTM (Universal Transverse Mercator) zone number in a map projection. A negative zone indicates that the false northing needs to be applied to the northing coordinate and a positive zone indicates that the false northing has been applied. Values: Negative Zone = Indicates that the false northing needs to be applied. Positive Zone = Indicates that the false northing has been applied. UTM zone Zona UTM Identificador único universal Ejemplo: 20f35b69-4eb1-49f1-be5f-b55cec37ea66 Universally unique identifier Example: 20f35b69-4eb1-49f1-be5f-b55cec37ea66 UUID UUID value Percentage of vegetation Example: 10.589334000000001 Porcentaje de vegetación Ejemplo: 10.589334000000001 Porcentaje de vegetación Vegetation percentage Porcentaje de agua Ejemplo: 15.87375 Water of percentage Example: 15.87375 Porcentaje de agua Water percentage Band 1 Band 2 Band 3 Band 4 Band 5 Band 6 Band 7 Band 8 Band 9 Band 10 Band 11 Band 12 Band 8A Bare Soil Building Data source Fuente de datos Landsat 8 Landsat 8 Landsat 8 product Producto de Landsat 8 MSI NDVI NDVI Processing Pixel Pixel Value Product Producto Raster Image Satellite Satélite Escena Scene Sentinel 2 Sentinel 2 Producto de Sentinel 2 Sentinel 2 product Snapshot Snapshot Vegetation Water true Classify pixels as vegetation using bands NDVI-B11-B04-B02 isVegetation NDVI-B11-B04-B02 0.056 0.469 0.120 0.094 true Classify pixels as building using bands NDVI-B11-B04-B02 isBuilding NDVI-B11-B04-B02 0.232 0.469 0.094 0.120 true Classify pixels as bare soil using bands NDVI-B11-B04-B02. 0.232 isBareSoil NDVI-B11-B04-B02 -2 0.469 0.094 0.232 0.120 true Classify pixels as vegetation using bands NDVI-B11-B01 isVegetation NDVI-B11-B01 0.469 0.120 0.246 0.040 true Classify pixels as bare soil using bands NDVI-B11-B04-B02 isBareSoil NDVI-B11-B04-B02 0.469 0.056 0.120 0.094 true Classify pixels as bare soil using bands NDVI-B11-B04 isBareSoil NDVI-B11-B04 0.114 0.246 0.120 true Classify pixels as water using bands NDVI-B11-B04 isWater NDVI-B11-B04 0.246 0.120 0.114 true Classify pixels as bare soil using bands NDVI-B12 isBareSoil NDVI-B12 0.520 0.144 0.469 true Classify pixels as vegetation using bands NDVI-B12 isVegetation NDVI-B12 0.520 0.144 0.469 true Classify pixels as vegetation using bands NDVI isVegetation NDVI 0.520 true Classify pixels as bare soil using bands NDVI-B11-B01 isBareSoil NDVI-B11-B01 0.040 0.469 0.120 0.246