Web Time Series Service (WTSS) is a lightweight web service for handling time series data from remote sensing imagery. Given a location and a time interval you can retrieve the according time series as a JSON array of numbers.
In WTSS a coverage is a three dimensional array associate to spatial and temporal reference systems.
WTSS is based on three operations:
list_coverages: returns the list of all available coverages in the service.describe_coverage: returns the metadata of a given coverage.time_series: query the database for the list of values for a given location and time interval.
There are free and open source implementations based on this specification:
- wtss-server: is a WTSS web server implemented in Python.
- wtss.py: is a client API for Python.
- wtss: is a client API for R.
- api: WTSS Specification using OpenAPI 3.0.
- jsonschemas: JSON Schema for WTSS responses.
The list_coverages operation can be used as follow:
http://myserver/
It will return a JSON document such as:
{
"wtss_version": "2.0.0",
"links": [
{
"rel": "service-docs",
"title": "Documentation for WTSS",
"href": "http://myserver/docs"
},
{
"rel": "data",
"title": "S2-SEN2COR_10_16D_STK-1",
"href": "http://myserver/S2-SEN2COR_10_16D_STK-1"
},
{
"rel": "data",
"title": "CB4_64_16D_STK-1",
"href": "http://myserver/CB4_64_16D_STK-1"
}
]
}If you need the metadata of a given coverage you can use the describe_coverage operation as follow:
http://myserver/<CollectionId>
The result of describe_coverage operation is a JSON document such as:
{
"name": "S2-SEN2COR_10_16D_STK",
"version": 1,
"fullname": "S2-SEN2COR_10_16D_STK-1",
"description": "This datacube was generated with all available surface reflectance images processed using Sen2cor (ilumination corrections on). The data is provided with 10 meters of spatial resolution, reprojected and cropped to BDC_SM grid, considering a temporal compositing function of 16 days using the best pixel approach (Stack).",
"title": "Sentinel-2 - MSI - Sen2cor - Cube Stack 16 days - v001",
"timeline": [
"2020-12-18",
"2020-12-02",
"2020-11-16"
],
"bands": [
{
"name": "B01",
"common_name": "coastal",
"scale": 0.0001,
"nodata": -9999.0,
"data_type": "int16",
"resolution_x": 10.0,
"resolution_y": 10.0,
"min_value": 0.0,
"max_value": 10000.0
},
{
"name": "B02",
"common_name": "blue",
"scale": 0.0001,
"nodata": -9999.0,
"data_type": "int16",
"resolution_x": 10.0,
"resolution_y": 10.0,
"min_value": 0.0,
"max_value": 10000.0
},
{
"name": "B03",
"common_name": "green",
"scale": 0.0001,
"nodata": -9999.0,
"data_type": "int16",
"resolution_x": 10.0,
"resolution_y": 10.0,
"min_value": 0.0,
"max_value": 10000.0
}
],
"extent": {"type": "Polygon", "coordinates": []},
"bdc:crs": "+proj=aea +lat_0=-12 +lon_0=-54 +lat_1=-2 +lat_2=-22 +x_0=5000000 +y_0=10000000 +ellps=GRS80 +units=m +no_defs ",
"grs_name": "BDC_SM",
"raster_size": {
"x": 16806,
"y": 10986
}
}You can retrieve the time series for a given location through the time_series:
http://myserver/S2-SEN2COR_10_16D_STK-1/timeseries
With body:
{
"attributes": [
"NDVI"
],
"start_datetime": "2017-01-01T00:00:00Z",
"end_datetime": "2017-01-16T23:00:00Z",
"geom": {
"type": "Polygon",
"coordinates": [
[
[
-54.0,
-12.0
],
[
-54.0,
-11.99
],
[
-53.99,
-11.99
],
[
-53.99,
-11.99
],
[
-54.0,
-12.0
]
]
]
}
}The result of time_series is a JSON document such as:
{
"result": {
[
{
"pixel_center": {
"type": "Point",
"coordinates": [
-53.99998107263968,
-11.989973727535231
]
},
"time_series": {
"timeline": [
"2017-01-01",
"2017-01-17"
],
"values": {
"NDVI": [
7919,
8457
]
}
}
}
]
},
"query": {
"collectionId": "S2-SEN2COR_10_16D_STK-1",
"attributes": [ "red", "nir" ],
"geom": {
"type": "Polygon",
"coordinates": [
[
[
-54.0,
-12.0
],
[
-54.0,
-11.99
],
[
-53.99,
-11.99
],
[
-53.99,
-11.99
],
[
-54.0,
-12.0
]
]
]
},
"start_datetime": "2017-01-01T00:00:00Z",
"end_datetime": "2017-01-16T23:00:00Z",
}
}The build system for the REST API documentation relies on the Node.js run-time environment:
If you have Node.js installed, please, execute the following command to install the ReDoc dependency:
$ npm installAfter that, generate the documentation:
$ npm run buildThe above command will create a folder named dist with the bundled file index.html. You may open it in your web browser or may serve it with an HTTP Server.
For Python developers, you can serve the HTMl with:
python3 -m http.server 8080 --directory distCopyright (C) 2022 INPE.
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.