VANE, the most powerful and simple query language for creating Basemaps from different satellite images with online processing and computing, with global coverage.

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Operations with satellite images


Tiles or polygons

Request an area of your interest by two ways that are tiles (XYZ) and polygons that are consist from a number of geographical coordinates (lat/lon)

Bands and layers mix up

Choose bands to construct your map from grayscale, RGB, NDVI, false colors; any combination of available bands is possible

Operations with images

Apply different operations to your images such as Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), RGB or change detection

Customization of color scheme

Configure a color palette of any image to get proper colors, e.g. for vegetation indexes

Color correction

Normalize brightness of an individual image for better color compatibility with other images to generate naturally looking maps

Time

Choose images on the specific date, for the particular period, pick up the best images for the last month or week, and many other options.

Query by tile server or polygon


By tile server (ZXY)

The endpoint is //sat.owm.io/sql/{z}/{x}/{y}? where Z is a zoom level, and X and Y identify the tile.

Query template: //sat.owm.io/sql/{z}/{x}/{y}?select={select}&from={from}&where={where}&order={order}&op={op}&APPID={APIKEY}


By polygon (GeoJSON)

The endpoint is //sat.owm.io/sql? , setup polygon= as a GeoJSON array.

Query template: //sat.owm.io/sql?select={select}&from={from}&where={where}&order={order}&op={op}&APPID={APIKEY}&polygon={polygon}


Parameters of query

Parameter Details
APIKEY

API key of the user

{z}/{x}/{y}

Query by a tile server. Z is the zoom level, and X and Y identify the tile

select

Bands that you pick up and combine for preferable processing

E.g. for RGB (Landsat 8) select=b4,b3,b2

for NDVI (Landsat 8) select=b5,b4

In the forthcoming version of the language, you can configure formulas like (b4-b3)/(b4+b3), NDVI(b4,b3) and many others.

from

Name of the dataset. Landsat 8, Sentinel-2, MODIS (Terra, Aqua), and Rapideye satellite images are available.

from=l8 (Landsat)

from=s2 (Sentinel-2)

from=terra (Terra)

from=aqua (Aqua)

from=modis (Aqua+Terra)

from=re (Rapideye)

where

Set up a filter by specific date or period.

day:2016-08-01

between(2016-06-23:2016-07-09)

day<2016-08-25

day>2016-08-01

now-100


Set up cloudiness filter to choose images with clouds less than some level, in %.

clouds<80

order

The result can include lots of images. To optimize it you can set up an order of choosing and combining of images.

You can choose amongst the following parameters like ‘first,' ‘last’ or ‘best.'

order=first

color

You can apply a variety of color processing to your images.

Set up levels of brightness for RGB:

color=brightness(5000:16000)


Set up a color scheme for NDVI:

color=0:ffffe5ff;0.1:f7fcb9ff;0.2:d9f0a3ff;0.3:addd8eff;0.4:78c679ff;0.5:41ab5dff;0.6:238443ff;0.7:006837ff;1:004529ff

op

Operation with layers/bands and their combination

Presets with fixed set of layers:

op=truecolor

op=falsecolor

op=ndvi

Operations with any layers:

op=rgb

op=ndi

op=evi

polygon

Set up your GeoJSON array with coordinates describing your polygon, encode it into string using any encoder you prefer, then embed a resulting string into your tile request polygon=%7B%22type%22%3A%22Feature%22%2C%22geometry%22%3A%7B%22type%22%3A%22Polygon%22%2C%22
coordinates%22%3A%5B%5B%5B-121.32%2C37.65%5D%2C%5B-121.32%2C37.72%5D%2C%5B-121.11%2C37.72%5D%2C%5B-121.11%2C37.65%5D%2C%5B-121.32%2C37.65%5D%5D%5D%7D%7D+

You can draw a polygon using the tool for visual query constructing owm.io/cases/create-polygon

Examples of queries

Basic map generated from the best images ever
Parameters
select=b4,b3,b2
order=best

Tile server template
//{s}.sat.owm.io/sql/{z}/{x}/{y}?select=b4,b3,b2&order=best

Example
//sat.owm.io/sql/9/143/218?select=b4,b3,b2&order=best&appid=..

builder

RGB color from Sat, 09 Jul 2016
Parameters
select=b4,b3,b2
where=day:2016-07-09

Tile server template
//{s}.sat.owm.io/sql/{z}/{x}/{y}?select=b4,b3,b2&where=day:2016-07-09

Example
//sat.owm.io/sql/12/3560/2179?select=b4,b3,b2&where=day:2016-07-09&appid=..

builder

False color from Sat, 09 Jul 2016
Parameters
select=b7,b5,b4
op=rgb
where=day:2016-07-09

Tile server template
//{s}.sat.owm.io/sql/{z}/{x}/{y}?select=b7,b5,b4&where=day:2016-07-09

Example
//sat.owm.io/sql/12/3560/2179?select=b7,b5,b4&where=day:2016-07-09&appid=..

builder

Custom false color
Parameters
select=b10,b7,b5
op=rgb
where=day:2016-10-01
Tile server template
//{s}.sat.owm.io/sql/{z}/{x}/{y}?select=b10,b7,b3&where=day:2016-10-01&from=l8

Example
//b.sat.owm.io/sql/9/82/198?select=b10,b7,b3&where=day:2016-10-01&from=l8&appid=..

builder

NDVI
Parameters
op=ndvi
where=day<2016-07-19
order=last

Tile server template
//{s}.sat.owm.io/sql/{z}/{x}/{y}?op=ndvi&where=day<2016-07-19&order=last

Example
//sat.owm.io/sql/12/883/1630?where=day<2016-07-19&from=l8&order=last&op=ndvi&appid=..

builder

Map generated from images available within a particular period
Between
2016-06-01 and 2016-06-31
Parameters
where=between(2016-06-01:2016-06-31)
order=best

Tile server template
//{s}.sat.owm.io/sql/{z}/{x}/{y}?select=b4,b3,b2&where=between(2016-06-01:2016-06-31)&order=best

Example
//sat.owm.io/sql/10/496/402?where=between(2016-06-01:2016-06-31)&appid=..

builder

Map generated from the best images for the last 200 days
Parameters
where=now-200

Tile server template
//{s}.sat.owm.io/sql/{z}/{x}/{y}?select=b4,b3,b2&where=now-200

Example
//sat.owm.io/sql/10/496/402?select=b4,b3,b2&where=now-200&appid=..

builder

RGB color by polygon from 14 Aug 2016
Parameters
where=day:2016-08-14
polygon={GeoJSON Polygon}

Tile server template
//{s}.sat.owm.io/sql?where=day:2016-08-14&polygon=...

Example
//sat.owm.io/sql?where=day:2016-08-14&polygon=...


NDVI by polygon from 14 Aug 2016
Parameters
where=day:2016-08-14
op=ndvi
polygon={GeoJSON Polygon}

Tile server template
//{s}.sat.owm.io/sql?where=day:2016-08-14&op=ndvi&polygon=...

Example
//sat.owm.io/sql?where=day:2016-08-14&op=ndvi&polygon=...




Operations with metadata

Query for image list

Query
Query for image list by coordinates Parameters
lat
lon

Example
sat.owm.io/api/3.0/search?lat=55&lon=37

Query for image list by a polygon Parameters
poly

Example
sat.owm.io/api/3.0/search?poly={"type":"Feature","geometry":{"type":"Polygon","coordinates":[[[-121.32,37.65],[-121.32,37.72],[-121.11,37.72],[-121.11,37.65],[-121.32,37.65]]]}}

Query by a tile server Parameters
Z is the zoom level
X, Y — identify the tile

Example
sat.owm.io/api/3.0/search/12/3560/2179

Meta information

Query
Query by tile server Parameters
where
from
order

Example
sat.owm.io/meta/8/124/100?where=day<2016-10-01&from=l8&order=best

Scene information

Query
Query by a scene ID Parameters
ID
Example
api.sat.owm.io/api/2.5/scene?id=LC80120282015304LGN00




Data sources

VANE processes images from any Earth observation satellites. The publicly available coverage (see Global Satellite Map) consists of open data from MODIS, Landsat and Sentinel-2 sources, the commercial data includes Planet's imagery (Rapideye, Planetscope) and more data to come.

Landsat 8

Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) images consist of nine spectral bands with a spatial resolution of 30 meters for Bands 1 to 7 and 9. New band 1 (ultra-blue) is useful for coastal and aerosol studies. New band 9 is useful for cirrus cloud detection. The resolution for Band 8 (panchromatic) is 15 meters. Thermal bands 10 and 11 are useful in providing more accurate surface temperatures and are collected at 100 meters. Approximate scene size is 170 km north-south by 183 km east-west (106 mi by 114 mi).

Landsat 8
Operational
Land Imager
(OLI)
and
Thermal
Infrared
Sensor
(TIRS)

Launched
February 11, 2013
Bands Wavelength
(micrometers)
Resolution
(meters)
Band 1 - Coastal aerosol 0.43 - 0.45 30
Band 2 - Blue 0.45 - 0.51 30
Band 3 - Green 0.53 - 0.59 30
Band 4 - Red 0.64 - 0.67 30
Band 5 - Near Infrared (NIR) 0.85 - 0.88 30
Band 6 - SWIR 1 1.57 - 1.65 30
Band 7 - SWIR 2 2.11 - 2.29 30
Band 8 - Panchromatic 0.50 - 0.68 15
Band 9 - Cirrus 1.36 - 1.38 30
Band 10 - Thermal Infrared (TIRS) 1 10.60 - 11.19 100 * (30)
Band 11 - Thermal Infrared (TIRS) 2 11.50 - 12.51 100 * (30)

* TIRS bands are acquired at 100 meter resolution, but are resampled to 30 meter in delivered data product.

More about Landsat 8 landsat.usgs.gov/landsat8.php

More about band names landsat.usgs.gov/band_designations_landsat_satellites.php

Sentinel-2

The Sentinel-2 satellites will each carry a single multi-spectral instrument (MSI) with 13 spectral channels in the visible/near infrared (VNIR) and short wave infrared spectral range (SWIR).

Bands Central Wavelength (micrometers) Resolution (meters)
Band 1 – Coastal aerosol 0.443 60
Band 2 – Blue 0.490 10
Band 3 – Green 0.560 10
Band 4 – Red 0.665 10
Band 5 – Vegetation Red Edge 0.705 20
Band 6 – Vegetation Red Edge 0.740 20
Band 7 – Vegetation Red Edge 0.783 20
Band 8 – NIR 0.842 10
Band 8A – Narrow NIR 0.865 20
Band 9 – Water vapour 0.945 60
Band 10 – SWIR – Cirrus 1.375 60
Band 11 – SWIR 1.610 20
Band 12 – SWIR 2.190 20

More about Sentinel-2 en.wikipedia.org/wiki/Sentinel-2

Rapideye

The RapidEye satellite constellation consists of five satellites collectively able to collect over 6 million square kilometers of data per day at 6.5 meter GSD (at nadir). Each satellite measures less than one cubic meter and weighs 150 kg (bus + payload). All five satellites are equipped with identical sensors and are located in the same orbital plane.
Today RapidEye is owned and operated by Planet.

Bands Central Wavelength (micrometers) Resolution (meters)
Band 1 – Blue 0.44 - 0.51 6.5
Band 2 – Green 0.52 - 0.59 6.5
Band 3 – Red 0.63 - 0.68 6.5
Band 4 – Red Edge 0.69 - 0.73 6.5
Band 5 – NIR 0.76 - 0.85 6.5

More about Rapideye en.wikipedia.org/wiki/RapidEye