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renaming of planet scope and sky sat bands
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‎planet_scope/cloud_classification/README.md

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## Evaluate and visualize
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As PlanetScope is commercial data, brought into Sentinel Hub as Bring Your Own Data, direct EO Browser and Sentinel Playgorund links are not possible due to the personalized data credentials.
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As PlanetScope is commercial data, brought into Sentinel Hub as Bring Your Own Data, direct EO Browser links are not possible due to the personalized data credentials.
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## General description
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PlanetScope has several usable data mask (UDM2) bands. The `UDM2_Clear` band gives information on whether the pixel is clear, meaning that the surface is clearly visible. If it's not clear, that means it's classified as either shadow, snow, light haze, heavy haze or cloud. Each of these is a separate UDM2 mask band:
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PlanetScope has several usable data mask (UDM2) bands. The `clear` band gives information on whether the pixel is clear, meaning that the surface is clearly visible. If it's not clear, that means it's classified as either shadow, snow, light haze, heavy haze or cloud. Each of these is a separate UDM2 mask band:
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- UDM2_Snow
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- UDM2_Shadow
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- UDM2_LightHaze
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- UDM2_HeavyHaze
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- UDM2_Cloud
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- snow
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- shadow
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- haze_light
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- haze_heavy
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- cloud
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When the mask band == 1, the pixel belongs to the mask (for example, when UDM2_Snow == 1, the pixel was recognized as snow). The bands are mutually exclusive - if a pixel belongs to one mask class, it cannot belong to any other.
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When the mask band == 1, the pixel belongs to the mask (for example, when `snow` == 1, the pixel was recognized as snow). The bands are mutually exclusive - if a pixel belongs to one mask class, it cannot belong to any other.
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In the script, each of the UDM2 mask bands is classified with a unique color, and the pixels that do not belong to any class, are returned transparent.
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‎planet_scope/cloud_classification/script.js

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return {
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input: [{
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bands: [
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"UDM2_Clear",
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"UDM2_Snow",
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"UDM2_Shadow",
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"UDM2_LightHaze",
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"UDM2_HeavyHaze",
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"UDM2_Cloud"
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"clear",
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"snow",
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"shadow",
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"haze_light",
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"haze_heavy",
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"cloud"
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]
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}],
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output: {
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}
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function evaluatePixel(samples) {
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if (samples.UDM2_Snow == 1) {
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if (samples.snow == 1) {
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return [0, 0.5, 1, 1] // blue
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}
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if (samples.UDM2_Shadow == 1) {
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if (samples.shadow == 1) {
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return [0.4, 0.4, 0.4, 1] //grey
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}
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if (samples.UDM2_LightHaze == 1) {
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if (samples.haze_light == 1) {
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return [0, 0.9, 1, 1] //sky blue
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}
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if (samples.UDM2_HeavyHaze == 1) {
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if (samples.haze_heavy == 1) {
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return [1, 0.7, 1, 1] //light pink
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}
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if (samples.UDM2_Cloud == 1) {
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if (samples.cloud == 1) {
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return [0.7, 0.7, 0.7, 1] //white
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}
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else {

‎planet_scope/false_color/README.md

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## Evaluate and visualize
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As PlanetScope is commercial data, brought into Sentinel Hub as Bring Your Own Data, direct EO Browser and Sentinel Playgorund links are not possible due to the personalized data credentials.
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As PlanetScope is commercial data, brought into Sentinel Hub as Bring Your Own Data, direct EO Browser links are not possible due to the personalized data credentials.
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## General description
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The [False color](https://en.wikipedia.org/wiki/False_color) infrared composite maps near-infrared spectral band B4 with red and green bands, B3 and B2, to [sRGB](https://en.wikipedia.org/wiki/SRGB) components directly. It is most commonly used to assess plant density and healht, as plants reflect near infrared and green light, while absorbing red. Since they reflect more near infrared than green, plant-covered land appears deep red. Denser plant growth is darker red. Cities and exposed ground are gray or tan, and water appears blue or black.
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The [False color](https://en.wikipedia.org/wiki/False_color) infrared composite maps near-infrared spectral band `nir` with `red` and `green` bands to [sRGB](https://en.wikipedia.org/wiki/SRGB) components directly. It is most commonly used to assess plant density and healht, as plants reflect near infrared and green light, while absorbing red. Since they reflect more near infrared than green, plant-covered land appears deep red. Denser plant growth is darker red. Cities and exposed ground are gray or tan, and water appears blue or black.
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## Description of representative images
2020

‎planet_scope/false_color/script.js

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function setup() {
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return {
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input: ["NIR", "Green", "Blue"],
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input: ["nir", "green", "blue"],
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output: { bands: 3 }
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};
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}
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function evaluatePixel(sample) {
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return [sample.NIR / 3000, sample.Green / 3000, sample.Blue / 3000];
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return [sample.nir / 3000, sample.green / 3000, sample.blue / 3000];
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}

‎planet_scope/green_city/README.md

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## Evaluate and visualize
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As PlanetScope is commercial data, brought into Sentinel Hub as Bring Your Own Data, direct EO Browser and Sentinel Playgorund links are not possible due to the personalized data credentials.
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As PlanetScope is commercial data, brought into Sentinel Hub as Bring Your Own Data, direct EO Browser links are not possible due to the personalized data credentials.
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## General description of the script
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‎planet_scope/green_city/script.js

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*/
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// Normalized Difference Vegetation Index
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var ndvi = (NIR - Red) / (NIR + Red);
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var ndvi = (nir - red) / (nir + red);
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// Threshold for vegetation
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var veg_th = 0.4;
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// Simple RGB
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var R = Red / 3000;
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var G = Green / 3000;
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var B = Blue / 3000;
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var R = red / 3000;
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var G = green / 3000;
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var B = blue / 3000;
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// Transform to Black and White
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var Y = 0.2 * R + 0.7 * G + 0.1 * B;

‎planet_scope/ndvi/README.md

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## Evaluate and visualize
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As PlanetScope is commercial data, brought into Sentinel Hub as Bring Your Own Data, direct EO Browser and Sentinel Playgorund links are not possible due to the personalized data credentials.
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As PlanetScope is commercial data, brought into Sentinel Hub as Bring Your Own Data, direct EO Browser links are not possible due to the personalized data credentials.
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## General description
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The normalized difference vegetation index, abbreviated NDVI, is defined as
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$$NDVI := \mathtt{Index}(NIR,RED) = \frac{NIR-RED}{NIR+RED}.$$
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$$NDVI := \mathtt{Index}(nir,red) = \frac{nir-red}{nir+red}.$$
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This is an example script which can be used with EO Browser and is configured to return statistics in a format which can be used with the statistical info chart. For more information, see <a href = "https://www.sentinel-hub.com/faq/how-configure-your-layers-statistical-info-eo-browser/"> How Can I Configure My Layers For Statistical Information In EO Browser?</a>
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‎planet_scope/ndvi/eob.js

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return {
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input: [{
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bands: [
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"Red",
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"NIR",
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"red",
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"nir",
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"dataMask",
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"UDM2_Clear"
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"clear"
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]
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}],
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output: [
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}
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function evaluatePixel(samples) {
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let ndvi = (samples.NIR - samples.Red) / (samples.NIR + samples.Red);
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let ndvi = (samples.nir - samples.red) / (samples.nir + samples.red);
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const indexVal = samples.dataMask === 1 ? ndvi : NaN;
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const clear = samples.dataMask * samples.UDM2_Clear;
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const clear = samples.dataMask * samples.clear;
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let id_default = colorBlend(ndvi, [0.0, 0.5, 1.0],
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[
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return {
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default: id_default,
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index: [indexVal],
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eobrowserStats: [indexVal, samples.UDM2_Clear],
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eobrowserStats: [indexVal, samples.clear],
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dataMask: [samples.dataMask],
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};
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}

‎planet_scope/ndvi/script.js

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//NDVI
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var val = (NIR - Red) / (NIR + Red);
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var val = (nir - red) / (nir + red);
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return colorBlend(val,
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[0.0, 0.5, 1.0],

‎planet_scope/ndwi/README.md

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## Evaluate and visualize
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As PlanetScope is commercial data, brought into Sentinel Hub as Bring Your Own Data, direct EO Browser and Sentinel Playgorund links are not possible due to the personalized data credentials.
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As PlanetScope is commercial data, brought into Sentinel Hub as Bring Your Own Data, direct EO Browser links are not possible due to the personalized data credentials.
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## General description of the script
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The NDWI is useful for water body mapping, as water bodies strongly absorb light in visible to infrared electromagnetic spectrum. NDWI uses green and near infrared bands to highlight water bodies. It is sensitive to built-up land and can result in over-estimation of water bodies.
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$$NDWI = \frac{Green-NIR}{Green+NIR}.$$
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$$NDWI = \frac{green-nir}{green+nir}.$$
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Values description: Index values greater than 0.5 usually correspond to water bodies. Vegetation usually corresponds to much smaller values and built-up areas to values between zero and 0.2.
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‎planet_scope/ndwi/script.js

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//NDWI
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var val = (Green / 3000 - NIR / 3000) / (Green / 3000 + NIR / 3000);
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var val = (green / 3000 - nir / 3000) / (green / 3000 + nir / 3000);
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return colorBlend(val,
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[-1, -0.5, -0.2, 0, 0.2, 0.5, 1.0],

‎planet_scope/planet_scope.md

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The spectral bands of PlanetScope data are the following if you order a 4-band <a href = "https://docs.sentinel-hub.com/api/latest/data/planet/planet-scope/#productbundle-parameter">product bundle</a>:
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- *B1 - Blue, resolution 3m*
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- *blue - Blue, resolution 3m*
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- *B2 - Green, resolution 3m*
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- *green - Green, resolution 3m*
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- *B3 - Red, resolution 3m*
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- *red - Red, resolution 3m*
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- *B4 - Near Infrared, resolution 3m*
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- *nir - Near Infrared, resolution 3m*
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The spectral bands of PlanetScope data are the following if you order a 8-band product bundle:
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- *B1 - Coastal Blue, resolution 3m*
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- *coastal_blue - Coastal Blue, resolution 3m*
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- *B2 - Blue, resolution 3m*
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- *blue - Blue, resolution 3m*
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- *B3 - Green I, resolution 3m*
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- *green_i - Green I, resolution 3m*
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- *B4 - Green, resolution 3m*
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- *green - Green, resolution 3m*
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- *B5 - Yellow, resolution 3m*
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- *yellow - Yellow, resolution 3m*
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- *B6 - Red, resolution 3m*
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- *red - Red, resolution 3m*
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- *B7 - Red Edge, resolution 3m*
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- *rededge - Red Edge, resolution 3m*
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- *B8 - Near-infrared, resolution 3m*
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- *nir - Near-infrared, resolution 3m*
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</details>
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‎planet_scope/true_color/README.md

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## Evaluate and visualize
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As PlanetScope is commercial data, brought into Sentinel Hub as Bring Your Own Data, direct EO Browser and Sentinel Playgorund links are not possible due to the personalized data credentials.
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As PlanetScope is commercial data, brought into Sentinel Hub as Bring Your Own Data, direct EO Browser links are not possible due to the personalized data credentials.
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## General description
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The true color product maps PlanetScope band values B3, B2, and B1 which roughly correspond to red, green, and blue part of the spectrum, respectively, to R, G, and B components.
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The true color product maps PlanetScope band values `red`, `green`, and `blue` which roughly correspond to red, green, and blue part of the spectrum, respectively, to R, G, and B components.
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## Description of representative images
2020

‎planet_scope/true_color/script.js

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function setup() {
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return {
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input: ["Blue", "Green", "Red"],
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input: ["blue", "green", "red"],
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output: { bands: 3 }
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};
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}
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function evaluatePixel(sample) {
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return [sample.Red / 3000, sample.Green / 3000, sample.Blue / 3000];
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return [sample.red / 3000, sample.green / 3000, sample.blue / 3000];
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}

‎skysat/ndvi/README.md

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## Evaluate and visualize
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As SkySat is commercial data, brought into Sentinel Hub as Bring Your Own Data, direct EO Browser and Sentinel Playgorund links are not possible due to the personalized data credentials.
13+
As SkySat is commercial data, brought into Sentinel Hub as Bring Your Own Data, direct EO Browser links are not possible due to the personalized data credentials.
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## General description
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The normalized difference vegetation index, abbreviated NDVI, is defined as
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$$NDVI := \mathtt{Index}(NIR,Red) = \frac{NIR-RED}{NIR+Red}.$$
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$$NDVI := \mathtt{Index}(nir,red) = \frac{nir-red}{nir+red}.$$
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2525
The result is visualized using the `valueInterpolate` function. It maps the values of the
2626
NDVI in certain intervals to RGBA (RGB+Alpha) values for visualization.

‎skysat/ndvi/script.js

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//VERSION=3
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function setup() {
33
return {
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input: ["Red", "NIR", "dataMask"],
4+
input: ["red", "nir", "dataMask"],
55
output: { bands: 4 }
66
}
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}
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var f = 10000
99
function evaluatePixel(sample) {
10-
var NDVI = index(sample.NIR, sample.Red)
10+
var NDVI = index(sample.nir, sample.red)
1111
return valueInterpolate(NDVI,
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[0, 0.2, 0.3, 0.4, 0.5, 1.0],
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[

‎skysat/panchromatic/script.js

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function setup() {
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return {
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input: ["PAN", "dataMask"],
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input: ["pan", "dataMask"],
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output: { bands: 4 }
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}
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}
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var f = 10000
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function evaluatePixel(sample) {
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return [sample.PAN / f, sample.PAN / f, sample.PAN / f, sample.dataMask]
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return [sample.pan / f, sample.pan / f, sample.pan / f, sample.dataMask]
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}

‎skysat/skysat.md

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The spectral bands of SkySat data are the following:
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*Blue - 450-515 nm, resolution 0.5m*
15+
*blue - 450-515 nm, resolution 0.5m*
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17-
*Green - 515-595 nm, resolution 0.5m*
17+
*green - 515-595 nm, resolution 0.5m*
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*Red - 605-695 nm, resolution 0.5m*
19+
*red - 605-695 nm, resolution 0.5m*
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21-
*NIR - 740-900 nm Near Infrared, resolution 0.5m*
21+
*nir - 740-900 nm Near Infrared, resolution 0.5m*
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*Pan - 450-900nm Panchromatic, resolution 0.5*
23+
*pan - 450-900nm Panchromatic, resolution 0.5*
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2525
- [True Color](/skysat/true_color)
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- [Panchromatic](/skysat/panchromatic)

‎skysat/true_color/README.md

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## General description
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The true color product combines Skysat band values Red, Blue, and Green to create a true color image.
17+
The true color product combines Skysat band values `red`, `blue`, and `green` to create a true color image.
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## Description of representative images
2020

‎skysat/true_color/script.js

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function setup() {
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return {
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input: ["Blue", "Red", "Green", "dataMask"],
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input: ["blue", "red", "green", "dataMask"],
77
output: { bands: 4 }
88
}
99
}
1010
var f = 2.5 / 10000
1111
function evaluatePixel(sample) {
12-
return [sample.Red * f, sample.Green * f, sample.Blue * f, sample.dataMask]
12+
return [sample.red * f, sample.green * f, sample.blue * f, sample.dataMask]
1313
}

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