- What is CREODIAS?
- Computing & Cloud
- Data & Processing
- Pricing Plans
- Fight with COVID-19
- Examples of usage
- Processing Sentinel-5P data using HARP and Python
- EO Data Access (R)evolution
- Land cover classification using remote sensing and AI/ML technology
- AI-based satellite image enhancer and mosaicking tools
- Monitoring air pollution using Sentinel-5P data
- Species classification of forests
- Enabling AI / ML workflows with CREODIAS vGPUs
- Satellite remote sensing analyses of the forest
- Satellite-based Urban Heat Island Mapping on CREODIAS
- Old but gold - historical EO data immediately available and widely used on CREODIAS
- CREODIAS for emergency fire management
- AgroTech project as an example of how CREODIAS can be used for food and environmental research
- Monitoring Air Quality of Germany in Pre vs During COVID Lockdown Period
- EO4UA
- Common Agricultural Policy monitoring with Earth Observation
- Applications of CREODIAS data
- Meteorological data usage on the CREODIAS platform
- Building added value under Horizon Europe with CREODIAS
- CREODIAS: Introduction to SAR Sentinel-1 data
- Land subsidence and landslides monitoring based on satellite data
- Satellite imagery in support of the Common Agriculture Policy (CAP) and crop statistics
- Useful tools for data processing, available on CREODIAS platform
- CREODIAS for hydrological drought modelling
- CREODIAS for managing Urban Heat Islands
- CREODIAS for Digitising Green Spaces
- CREODIAS for Air Quality
- Advanced data processors on CREODIAS
- CREODIAS for your application
- Solutions for agriculture with CREODIAS
- Earth Observation data for Emergency response
- Security Applications with Satellite Data
- Climate Monitoring with Satellite Data
- Water Analysis on CREODIAS
- CREODIAS for land and agriculture monitoring
- Solutions for atmospheric analysis
- Example of tool usage
- Processing EO Data and Serving www services
- Processing and Storing EO
- Embedding OGC WMS Services into Your website
- GPU Use Case
- Using the EO Browser
- EO Data Finder API Manual
- Use of SNAP and QGIS on a CREODIAS Virtual Machine
- Use of WMS Configurator
- DNS as a Service - user documentation
- Use of Sinergise Sentinel Hub on the CREODIAS EO Data Hub
- Load Balancer as a Service
- Jupyter Hub
- Use of CREODIAS Finder for ordering data
- ESRI ArcGIS on CREODIAS
- Use of CEMS data through CREODIAS
- Searching, processing and analysis of Sentinel-5P data on CREODIAS
- ASAR data available on CREODIAS
- Satellite remote sensing analyses of the forest
- EO Data Catalogue API Manual
- Public Reporting Dashboards
- Sentinel Hub Documentation
- Legal Matters
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Sentinel Hub Documentation
Sentinel Hub is a multi-spectral and multi-temporal big data satellite imagery service, capable of fully automated archiving, real-time processing and distribution of remote sensing data and related EO products, managed by Sinergise Ltd. For more information on Sinergise go to Third Party Services.
Statistical info API documentation
The Statistical info (or feature info service, abbreviated FIS), performs elementary statistical computations---such as mean, standard deviation, and histogram approximating the distribution of reflectance values---on remotely sensed data for a region specified in a given spatial reference system across different bands and time ranges.
A quintessential usage example would be querying the service for basic statistics and the distribution of NDVI values for a polygon representing an agricultural unit over a time range.
Basic information
The service retrieves all data from the given time range that fit the prescribed criteria, computes statistics on these data, and returns the result in the form of a JSON object.
The base URL for the FIS service: http://services.sentinel-hub.com/ogc/fis/{INSTANCE_ID}.
Replace {INSTANCE_ID}
with the one you find in the WMS Configurator (as in examples of query URLs at the bottom of the page).
By deafult, the service uses the settings from the instance specified by the instance ID, but one can override these by specifying them in the URL. For example, setting MAXCC=10
overrides whatever is set in the instance settings in the WMS configurator. The instances are managed (i.e. edited, created, and deleted) in the Configuration Utility.
FIS only works on instances that don't enforce image types other than 32 bit RAW. Ensure that the image type is not enforced in the instance configuration (i.e. either have it set to None
or to image/raw;depth=32f
).
As of this writing, only GET endpoint is implemented.
More details
More about custom URL parameters, output image formats, atmospheric correction, preview modes, custom evaluation script, and which operations can be used with WMS, WMTS, WFS or WCS services is available here.
FIS URL parameters
Below is a list of FIS parameters. Besides these, one can also pass custom parameters.
Mandatory | Parameter | Description | Example |
---|---|---|---|
Yes | LAYER |
The preconfigured layer (image) based on which the statistics are computed. Exactly one layer has to be specified, additional overlays may be added. If the product has multiple bands, such as, for example, LAYER=TRUE_COLOR , statistics are computed for each band separately. See the list of supported EO products. |
LAYER=NDVI |
Yes | CRS |
Coordinate reference system. | CRS=EPSG:3857 |
Yes | TIME |
Time range from which to retrieve the images. The service computes statistics for each image from the time range that fits the prescribed criteria. | TIME=2016-01-01/2017-10-30 |
Yes | RESOLUTION |
Specifies the spatial resolution, in meters per pixel, of the image from which the statistics are to be estimated. When using CRS=EPSG:4326 one has to add the "m" suffix to enforce resolution in meters per pixel (e.g. RESOLUTION=10m ). |
RESOLUTION=10 |
Yes* | GEOMETRY |
A WKT representation of a geometry describing the region of interest. Note that WCS 1.1.1 standard is used here, so for EPSG:4326 coordinates should be in latitude/longitude order. |
|
Yes* | BBOX |
A bounding box, represented with the bottom-left and upper-right vertices, describing the region of interest. |
|
No | STYLE |
Specified style (overrides the one specified in the layer configuration). For indices (one-component products such as NDVI , NDWI , etc.), setting STYLE=INDEX enforces raw data (other popular choices for one-component products include GRAYSCALE and COLORMAP . For multi-component products (such as TRUE_COLOR , FALSE_COLOR , etc.), setting STYLE=SENSOR enforces the raw sensor data to be used, while STYLE=REFLECTANCE enforces raw sensor data scaled to the range [0,1] . See which styles are available for various EO products. |
STYLE=INDEX |
No | BINS |
The number of bins (a positive integer) in the histogram. When this parameter is absent, no histogram is computed. | BINS=32 |
*Note. One should use either BBOX
or GEOMETRY
, never both, to specify the region of interest.
For more information about layers and styles, see the WMS documentation.
Output format
The service works on single-band (e.g., NDVI
, NDWI
, etc.) as well as multi-band (e.g., TRUE_COLOR
, FALSE_COLOR
, etc.) products. In case of a multi-band product, the statistics are computed for each band (see the description of the output structure below).
Results are always returned as JSON objects of the following form:
{
"C0": [
{
"date": "date1",
"basicStats": {
"min": minValue1,
"max": maxValue1,
"mean": meanValue1,
"stDev": standardDeviation1,
},
"histogram": [
{
"value": value,
"count": count
},
...,
{
"value": value,
"count": count
},
]
},
...,
{
...
}
],
...,
"CN": [
...
]
}
In words, the bands comprising the specified layer correspond to keys in the response JSON object. The value associated with each band is an array of objects containing statistics for that band in the image, one per available date.
The "histogram"
is absent when the histogram isn't request (i.e., when the BINS
parameter is not set).
Examples
The example below illustrates how data from the statistical service may be visualized.
Visualizing the results
Below is a plot of NDVI value distributions across time ranges. The violin plots were computed from the following FIS request: http://services.sentinel-hub.com/ogc/fis/{INSTANCE_ID}?LAYER=5_VEGETATION_INDEX&STYLE=INDEX&CRS=EPSG%3A3857&
TIME=2015-01-01%2F2015-10-01&BBOX=1550369.86,5586056.25,1547498.69,5584861.92&RESOLUTION=10&MAXCC=5&BINS=100
Requesting stats without a histogram
Query URL: http://services.sentinel-hub.com/ogc/fis/{INSTANCE_ID}?LAYER=5_VEGETATION_INDEX&STYLE=INDEX&CRS=EPSG%3A3857&TIME=2015-01-01%2
F2015-10-01&BBOX=1550369.86,5586056.25,1547498.69,5584861.92&RESOLUTION=10&MAXCC=5
Response:
{
"C0": [
{
"date": "2015-08-30",
"basicStats": {
"min": -0.5478424429893494,
"max": 0.7815912365913391,
"mean": 0.147320137875888,
"stDev": 0.35443419609590726
}
},
{
"date": "2015-07-11",
"basicStats": {
"min": -0.5127978920936584,
"max": 0.8115044236183167,
"mean": 0.20168528533031557,
"stDev": 0.31436594348376923
}
}
]
}
Requesting stats with a histogram
Query URL: http://services.sentinel-hub.com/ogc/fis/{INSTANCE_ID}?LAYER=5_VEGETATION_INDEX&STYLE=INDEX&CRS=EPSG%3A3857&TIME=2015-01-01%2
F2015-10-01&BBOX=1550369.86,5586056.25,1547498.69,5584861.92&RESOLUTION=10&MAXCC=5&BINS=5
Response:
{
"C0": [
{
"date": "2015-08-30",
"basicStats": {
"min": -0.5478424429893494,
"max": 0.7815912365913391,
"mean": 0.147320137875888,
"stDev": 0.35443419609590726
},
"histogram": {
"bins": [
{
"value": -0.23164855383139124,
"count": 14377.0
},
{
"value": 0.08134964140017291,
"count": 1844.0
},
{
"value": 0.23792375711536012,
"count": 4692.0
},
{
"value": 0.37365802377462387,
"count": 4.0
},
{
"value": 0.5359620948992795,
"count": 13236.0
}
]
}
},
{
"date": "2015-07-11",
"basicStats": {
"min": -0.5127978920936584,
"max": 0.8115044236183167,
"mean": 0.20168528533031557,
"stDev": 0.31436594348376923
},
"histogram": {
"bins": [
{
"value": -0.3067768962218847,
"count": 145.0
},
{
"value": -0.11833351501876165,
"count": 14484.0
},
{
"value": 0.13131743694345155,
"count": 30.0
},
{
"value": 0.24965114950902842,
"count": 7984.0
},
{
"value": 0.5777060477812307,
"count": 11510.0
}
]
}
}
]
}