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Merge pull request #340 from azlinszkysinergise/andras-experimenting
Andras experimenting
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‎Gemfile

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source 'https://rubygems.org'
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gem "github-pages", "232", group: :jekyll_plugins
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gem "just-the-docs", "0.9.0"
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gem "webrick"

‎_layouts/script.html

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S1_AWS_EW_HHHV: S1_CDAS_EW_HHHV
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S1_MOSAIC: 3c662330-108b-4378-8899-525fd5a225cb
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DEM_MAPZEN: DEM_COPERNICUS_30_CDAS
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---
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<h1 class="d-inline" id={{page.slug}}> {{page.title}} </h1>

‎dem/dem-central-europe/index.md

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---
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title: DEM colormap for Central Europe
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parent: dem
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layout: script
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nav_exclude: true
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examples:
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- zoom: '7'
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lat: '47.2233'
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lng: '21.57715'
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datasetId: DEM
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fromTime: '2020-07-12T00:00:00.000Z'
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toTime: '2020-07-12T23:59:59.999Z'
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platform:
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- CDSE
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- EOB
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evalscripturl: https://custom-scripts.sentinel-hub.com/custom-scripts/dem/dem-central-europe/script.js
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---
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This color ramp is defined to make interpreting topography on the Copernicus 30 DEM in Central Europe easy. Therefore, it runs between -200 and 3500 m above sea level. It uses classical colours typical for elevation maps: brown and white for high mountains and greens for lowland areas. Note that since the Copernicus 30 DEM does not include a seafloor model, the elevations below 0 m ASL are not used.
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## Example location
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The example is a site near Kosice, Slovakia, showing an elevation gradient between the Zemplín mountains and the Ondava river floodplain
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```yaml
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examples:
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- zoom: '11'
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lat: '48.81432'
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lng: '21.52702'
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datasetId: DEM
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fromTime: '2020-07-12T00:00:00.000Z'
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toTime: '2020-07-12T23:59:59.999Z'
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platform:
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- CDSE
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- EOB
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evalscripturl: https://custom-scripts.sentinel-hub.com/custom-scripts/dem/dem-central-europe/script.js
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```
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## General description of the script
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## Color Table
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<table>
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<tr>
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<th>Value</th>
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<th>Color Code</th>
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<th>Color Patch</th>
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</tr>
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<tr>
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<td>3500</td>
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<td>#f7eafd</td>
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<td style="background-color: #f7eafd;"></td>
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</tr>
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<tr>
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<td>3000</td>
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<td>#ebebeb</td>
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<td style="background-color: #ebebeb;"></td>
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</tr>
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<tr>
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<td>1000</td>
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<td>#a76b18</td>
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<td style="background-color: #a76b18;"></td>
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</tr>
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<tr>
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<td>500</td>
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<td>#dca620</td>
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<td style="background-color: #dca620;"></td>
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</tr>
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<tr>
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<td>200</td>
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<td>#A2AE32</td>
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<td style="background-color: #A2AE32;"></td>
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</tr>
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<tr>
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<td>100</td>
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<td>#18a722</td>
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<td style="background-color: #18a722;"></td>
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</tr>
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<tr>
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<td>50</td>
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<td>#018643</td>
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<td style="background-color: #018643;"></td>
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</tr>
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<tr>
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<td>30</td>
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<td>#015965</td>
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<td style="background-color: #015965;"></td>
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</tr>
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<tr>
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<td>10</td>
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<td>#01471c</td>
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<td style="background-color: #01471c;"></td>
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</tr>
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<tr>
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<td>0</td>
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<td>#bbbcbc</td>
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<td style="background-color: #bbbcbc;"></td>
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</tr>
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<tr>
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<td>-10</td>
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<td>#2754de</td>
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<td style="background-color: #2754de;"></td>
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</tr>
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<tr>
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<td>-20</td>
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<td>#0d027e</td>
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<td style="background-color: #0d027e;"></td>
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</tr>
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<tr>
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<td>-50</td>
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<td>#08014c</td>
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<td style="background-color: #08014c;"></td>
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</tr>
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<tr>
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<td>-200</td>
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<td>#4a4a4a</td>
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<td style="background-color: #4a4a4a;"></td>
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</tr>
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</table>
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## Author of the script
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András Zlinszky, Sinergise

‎dem/dem-central-europe/script.js

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//VERSION=3
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// To set custom max and min values, set
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// choose your max and min values.
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// The color map will then be scaled
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// to those max and min values
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const max = 3500;
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const min = -200;
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function setup() {
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return {
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input: ["DEM", "dataMask"],
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output: [
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{ id: "default", bands: 4, sampleType: "AUTO" },
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{ id: "index", bands: 1, sampleType: "FLOAT32" },
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{ id: "dataMask", bands: 1 },
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],
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};
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}
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const map = [
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[3500, 0xf7eafd],
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[3000, 0xebebeb],
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[1000, 0xa76b18],
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[500, 0xdca620],
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[200, 0xA2AE32],
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[100, 0x18a722],
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[50, 0x018643],
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[30, 0x15965],
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[10, 0x01471c],
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[0, 0xbbbcbc],
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[-10, 0x2754de],
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[-20, 0x0d027e],
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[-50, 0x08014c],
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[-200, 0x4a4a4a],
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];
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const visualizer = new ColorRampVisualizer(map, min, max);
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function evaluatePixel(samples) {
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const imgVals = visualizer.process(samples.DEM)
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// Return the 4 inputs and define content for each one
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return {
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default: [...imgVals, samples.dataMask],
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index: [samples.DEM],
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dataMask: [samples.dataMask],
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}
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}

‎sentinel-5p/no2_monthly_mean/README.md

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---
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title: NO2 Monthly mean
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parent: Sentinel-5p
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grandparent: Sentinel
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layout: script
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permalink: /sentinel-5p/no2_monthly_mean/
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nav_exclude: true
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examples:
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- zoom: '8'
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lat: '46.60228'
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lng: '15.17212'
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datasetId: S5_NO2
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fromTime: '2024-12-28T00:00:00.000Z'
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toTime: '2025-01-28T23:59:59.999Z'
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platform:
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- CDSE
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- EOB
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evalscripturl: https://custom-scripts.sentinel-hub.com/custom-scripts/sentinel/sentinel-5p/no2_monthly_mean/script.js
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---
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## General description of the script
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This script calculates the mean values of NO<sub>2</sub> over the time period you select. It is useful for visualizing large-scale patterns of air pollution.
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In order to comply with the SI unit definitions, the TROPOMI NO2 data product gives trace gas concentrations in mol/m<sup>2</sup>.
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Cloud-affected pixels are masked from Sentinel-5P air pollution imagery, you will see them as empty pixels on the map. Due to the relatively low resolution of Sentinel-5P TROPOMI, these datasets work best at regional to continental scale, however, at such scales it is extremely rare to encounter an image that is not influenced by clouds. Therefore, similar to [Sentinel-2 quarterly mosaics](https://dataspace.copernicus.eu/news/2024-2-27-exploring-new-frontier-sentinel-cloudless-mosaics-copernicus-data-space-ecosystem), creating longer-term mean mosaics of Sentinel-5P data enables exploring pollution patterns across regions.
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The script is written to work for NO2, but can be used for other Sentinel-5P data layers as well by manually changing the variable "NO2" in the setup function and in the sum(array) function.
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## Representative image
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This image shows the monthly mean NO2 concentration for January 2024 over Central Europe. You can see the major urban centers of Northern Germany, Belgieum and the Netherlands, the Rhine valley, the Czech basin, and the high pollutant concentration in the Po valley.
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View in Copernicus Browser [here](https://tinyurl.com/europesen5p)
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!['Sentinel-5 N02 monthly mean over Central Europe, January 2024, ](.\img\sentinel_5p_monthly_NO2_europe_2024_jan.jpg)
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## References
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- [Sentinel-5P TROPOMI NO2 data products Algorithm Theoretical Basis Document](https://sentiwiki.copernicus.eu/__attachments/1673595/S5P-KNMI-L2-0005-RP%20-%20Sentinel-5P%20TROPOMI%20ATBD%20NO2%20data%20products%202022%20-%202.4.0.pdf?inst-v=9aab56a9-2b0f-4066-9dbe-4985b055a039)

‎sentinel-5p/no2_monthly_mean/img/sentinel_5p_monthly_NO2_europe_2024_jan.jpg

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‎sentinel-5p/no2_monthly_mean/script.js

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//VERSION=3
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// By Jonas Viehweger, Sinergise
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var minVal = 0.0;
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var maxVal = 0.0001;
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const map = [
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[0, 0x00007f],
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[1, 0x0000ff],
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[2, 0x00ffff],
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[3, 0xffff00],
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[4, 0xff0000],
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[5, 0x7f0000]
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];
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const visualizer = new ColorRampVisualizer(map, minVal, maxVal)
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function setup() {
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return {
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input: ["NO2","dataMask"],
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output: [
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{ id: "default", bands: 4 },
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{ id: "index", bands: 1, sampleType: "FLOAT32" },
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{ id: "eobrowserStats", bands: 1, sampleType: "FLOAT32"},
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{ id: "dataMask", bands: 1 },
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],
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mosaicking: "ORBIT"
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};
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}
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function isClear(sample) {
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return sample.dataMask == 1;
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}
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function sum(array) {
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let sum = 0;
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for (let i = 0; i < array.length; i++) {
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sum += array[i].NO2;
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}
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return sum;
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}
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function evaluatePixel(samples) {
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const clearTs = samples.filter(isClear)
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const mean = sum(clearTs) / clearTs.length
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const [r, g, b] = visualizer.process(mean);
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const dataMask = clearTs.length > 0;
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return {
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default: [r, g, b, dataMask],
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index: [mean],
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eobrowserStats: [mean],
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dataMask: [dataMask],
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};
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}

‎sentinel-5p/sentinel-5p.md

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- [Cloud Top Pressure](/sentinel-5p/cloud-top-pressure)
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- [Cloud Base Pressure](/sentinel-5p/cloud-base-pressure)
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- [Cloud Optical Thickness](/sentinel-5p/cloud-optical-thickness)
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- [Cloud Effective Radiometric Fraction](/sentinel-5p/cloud-radiometric-fraction)
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- [Cloud Effective Radiometric Fraction](/sentinel-5p/cloud-radiometric-fraction)
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### Additional Products
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- [Nitrogen Dioxide monthly mean](/sentinel-5p/no2_monthly_mean)

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