├── .gitignore ├── LICENSE.md ├── README.md ├── airpollution ├── Measuring Air Pollution from Space.ipynb ├── images │ ├── 2018-08-06, Sentinel-5P AER_AI, Sentinel 5P L2 - AER AI 340 and 380.jpg │ └── 2018-08-06, Sentinel-5P NO2, Sentinel 5P L2 - Nitrogen Dioxide.jpg └── video │ └── video.mp4 ├── postBuild ├── requirements.txt ├── volcanoes ├── README.md ├── Volcano from Space.ipynb ├── data │ ├── 060bb4ff0f8820a33633796a532d0732 │ │ ├── request.json │ │ └── response.tiff │ ├── 0bdef434f99e0a51cdd9b8507ef2b81d │ │ ├── request.json │ │ └── response.tiff │ ├── 139f861c15461760d27103a888fd12a0 │ │ ├── request.json │ │ └── response.png │ ├── 18cba188bc4d9fec183b8cbcc660d298 │ │ ├── request.json │ │ └── response.png │ ├── 22a9eba839d553bea1b997526259c246 │ │ ├── request.json │ │ └── response.tiff │ ├── 2ee7b56f73843f415b61cdd7d3a5b4f8 │ │ ├── request.json │ │ └── response.png │ ├── 38104a6d9e8c5b248ee42362a8b54c4f │ │ ├── request.json │ │ └── response.tiff │ ├── 38f0988b08400478bf3af61305ed9bfe │ │ ├── request.json │ │ └── response.png │ ├── 3cd0a224558a959d0f5dd27f41c20338 │ │ ├── request.json │ │ └── response.tiff │ ├── 412557d5614b45c9a30e2063557e25aa │ │ ├── request.json │ │ └── response.tiff │ ├── 42078f29acb54c3f919dbed0910afb54 │ │ ├── request.json │ │ └── response.tiff │ ├── 42f5da9e1044e28c590c4ac1eed70e36 │ │ ├── request.json │ │ └── response.png │ ├── 4b3e1d8536878b4916043e53d432db04 │ │ ├── request.json │ │ └── response.tiff │ ├── 57e7b308be62d4b9f56bf42738e62244 │ │ ├── request.json │ │ └── response.tiff │ ├── 6337d477c8fb97200e43a1ef348bee58 │ │ ├── request.json │ │ └── response.png │ ├── 67b2d33515115a14442b006921c8d829 │ │ ├── request.json │ │ └── response.png │ ├── 78a5fe80bf5c149dfb149172aa88cda3 │ │ ├── request.json │ │ └── response.png │ ├── 842bce1076318ec8d7883e1390f45db3 │ │ ├── request.json │ │ └── response.png │ ├── 897e053336718cd94d174edb29197ecb │ │ ├── request.json │ │ └── response.png │ ├── 8b28b11af96dd080feadcd1084e9a774 │ │ ├── request.json │ │ └── response.tiff │ ├── 8e0f59022df49d47a9e3289ad7f0c71a │ │ ├── request.json │ │ └── response.tiff │ ├── 8f44855e4384387d7eaa0317e7ee4369 │ │ ├── request.json │ │ └── response.tiff │ ├── 965af58e6de0f59d5a9e578d46249d0d │ │ ├── request.json │ │ └── response.png │ ├── 9bc1d2db87cf5911f092b24a3f6b25c6 │ │ ├── request.json │ │ └── response.tiff │ ├── 9d70e353bb386ee51329f96fb314b49c │ │ ├── request.json │ │ └── response.tiff │ ├── a4c44b4b7947c55d6203a90db8d98a4f │ │ ├── request.json │ │ └── response.tiff │ ├── aa8841b07801349b219bee532762345d │ │ ├── request.json │ │ └── response.png │ ├── ae89ef6b6bc4ffb69ce6a47868874207 │ │ ├── request.json │ │ └── response.png │ ├── afd6db510236bb91f21277686807a3f8 │ │ ├── request.json │ │ └── response.png │ ├── d9af42899a9adf10583f84a12a6c675d │ │ ├── request.json │ │ └── response.png │ ├── df56c0b0f9e241b380a652451d9c7d69 │ │ ├── request.json │ │ └── response.png │ ├── e833eaed5d6897389e624daface5b314 │ │ ├── request.json │ │ └── response.png │ └── f2d24209087c39dfa73a03a1f43ea7b6 │ │ ├── request.json │ │ └── response.tiff └── images │ ├── 1984-05-19, Landsat 5 ESA, Custom script.jpg │ ├── 2006-11-24, Landsat 5 ESA, Custom script.jpg │ ├── 2015-12-03, Landsat 8 USGS, Custom script.jpg │ ├── 2018-12-26, Sentinel-2A L1C, Custom script.jpg │ └── hourglass.png └── wildfires ├── Wildfires from Satellite Images.ipynb ├── images ├── 2016-08-07, Sentinel-2A L1C, Custom script.png ├── 2016-08-07_Sentinel-2A L1C_Custom_script_BRN.png ├── 2016-08-17, Sentinel-2A L1C, Custom script_BRN.png ├── 2018-07-21, Sentinel-2A L1C, Custom script_Russia_close.jpg ├── 2018-07-21, Sentinel-2A L1C, Custom script_Russia_far.jpg ├── Digitized_Burned_Areas.png ├── NDVI_Graph_Burned.png ├── NDVI_Graph_UnBurned.png ├── S5p_21_7_2018.png ├── S5p_6_7_2018.png ├── Sentinel-2 L1C from 2016-08-17.png ├── Sentinel-2 image on 2017-07-03 _ dBRN.jpg └── Sentinel-2 image on 2017-07-03.jpg └── themes.json /.gitignore: -------------------------------------------------------------------------------- 1 | /airpollution/.ipynb_checkpoints 2 | /volcanoes/.ipynb_checkpoints 3 | -------------------------------------------------------------------------------- /LICENSE.md: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Sentinel Hub 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # education 2 | Worked out examples for common eo topics, meant to facilitate learning and encourage curiosity. 3 | -------------------------------------------------------------------------------- /airpollution/Measuring Air Pollution from Space.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Measuring Air Pollution from Space\n", 8 | "This use case was prepared with support from the European Space Agency and using Copernicus data.\n", 9 | "\n", 10 | "## Introduction\n", 11 | "\n", 12 | "Air pollution is a serious problem worldwide - especially in large urban areas. According to the World Health Organization (WHO) some 4.2 million premature deaths each year are linked to air pollution. And according to data the European Environment Agency published, around 90% of city dwellers in Europe are subjected to air pollutants at levels deemed harmful.\n", 13 | "There are a number of pollutants which are linked to human activity - in particular the burning of fossil fuels. The most harmful one in terms of human health impact is fine particulate matter or PM2.5. Other harmful pollutants include carbon monoxide (CO) and nitrogen dioxide (NO2), both of which can originate from vehicle traffic but also from other, natural sources. Ozone (O3) near the ground (tropospheric ozone) is another harmful pollutant which forms as a result of the reaction of products created by the burning of fossil fuels with the sun’s UV radiation and can cause serious respiratory illness. However, higher up our atmosphere - in the stratosphere at a height of around 10 to 50 Km - Ozone occurs naturally and plays an important role in filtering out the sun's harmful UV radiation.\n", 14 | "\n", 15 | "While a significant part of harmful pollutants are the direct or indirect result of human activity, there are also a large number of natural emission sources such as volcanic eruptions which release sulphur dioxide (SO2), carbon monoxide released by wildfires or windblown dust which contributes to high PM concentrations. Furthermore, pollutants can be carried over large distances by global wind patterns such that there is not always a clear connection between air quality and local anthropogenic or natural emissions. Observing and understanding the global pollution patterns is therefore vital in understanding causes and sources of harmful pollutants and ultimately developing strategies to tackle air pollution. \n", 16 | "\n", 17 | "Ground-based measurements such as weather stations are a very important part of the picture. Ground-based sensors are generally in direct contact with the medium being measured (ambient air) and allow for very precise, continuous measurements. The drawback of such in-situ measurements is that they are localized and cannot cover the entire breadth of our atmosphere. Therefore, to get the complete picture of atmospheric pollution and the distribution of atmospheric gases, an additional type of measurements is needed.\n" 18 | ] 19 | }, 20 | { 21 | "cell_type": "markdown", 22 | "metadata": {}, 23 | "source": [ 24 | "## Satellite-based Atmospheric Remote Sensing\n", 25 | "\n", 26 | "This is where satellites come in. Satellite-based sensors allow us to retrieve information over large areas and even globally. These measurements (which have a high spatial coverage but are also rather coarse) can then be integrated with local measurements from ground stations to get a full picture of air pollution patterns. Unlike in-situ sensors, satellite-based sensors make use of the principle of remote sensing where measurements are obtained *without direct contact* with the atmosphere. The idea behind such observations is that each gas and particle - due to its unique shape and structure on a molecular level - interacts differently with the electromagnetic radiation emitted by the sun. By observing the electromagnetic energy reflected by the atmosphere on different wavelengths using a so-called spectrometer, satellites are able to detect the unique electromagnetic ‘fingerprints’ of a great number of atmospheric gases and particles.\n", 27 | "\n", 28 | "There have been a range of satellite missions over the years dedicated to observing atmospheric gases. One of the earlier missions was NASA's *Total Ozone Mapping Spectrometer (TOMS)* which was launched aboard the Nimbus-7 meteorological satellite in 1978. TOMS is also a good showcase on how satellite- and ground-based measurements complement each other: TOMS recorded extremely low ozone (O3) concentrations in the atmosphere above Antarctica. So low in fact, that they were discarded as an instrument error until ground-based measurements confirmed these measurements in the 1980's and lead to the discovery of what became known as the ozone hole. The European Space Agency's (ESA) *Global Ozone Monitoring Experiment (GOME)*, launched in 1995 aboard the ERS-2 satellite, was able to detect not only ozone but also nitrogen dioxide (NO2) and different cloud properties using its four spectral channels. This was followed up by ESA's SCIAMACHY (*SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY*) instrument in 2002 and NASA's *Ozone monitoring instrument (OMI)* in 2004.\n", 29 | "\n", 30 | "In October 2017 ESA launched Sentinel-5P, an earth-observation satellite charged with detecting different types of trace gases and pollutants which extends the capabilities of both SCIAMACHY and OMI. The satellite is mapping atmospheric gases for the whole world on a daily basis with a resolution of 7 Km x 3.5 Km per pixel using its state-of-the-art Tropomi instrument. While this may sound like a low resolution, it is actually much higher than any of Sentinel-5P’s predecessors. This allows us for example to monitor air pollution over individual cities, which was previously not possible using satellite-based measurements alone. By observing the visible, near-infrared and shortwave infrared range of the electromagnetic spectrum, Tropomi can detect a wide range of atmospheric gases such as nitrogen dioxide, ozone, formaldehyde, sulphur dioxide, methane and carbon monoxide as well as aerosols. The combination of a daily repeat time, a high spatial resolution and the large number of atmospheric gases and particles measured means, Sentinel-5P is providing new insights into global pollution patterns. What is more, since Sentinel-5P is also monitoring sulphur dioxide and carbon monoxide it can also be used to monitor volcanic eruptions and wildfires. \n", 31 | "\n", 32 | "For more information on Sentinel-5P, you can visit ESA’s website dedicated to the topic and this website dedicated to the Tropomi instrument.\n" 33 | ] 34 | }, 35 | { 36 | "cell_type": "markdown", 37 | "metadata": {}, 38 | "source": [ 39 | "## Measuring NO2 and Aerosols with Sentinel-5P\n", 40 | "\n", 41 | "Here we see NO2 concentrations over part of Europe as measured by Sentinel-5P:\n", 42 | "\n", 43 | "\n", 44 | "\n", 45 | "\n", 50 | "\n", 51 | "\n", 52 | "\n", 53 | "\n", 54 | "
\n", 46 | "\n", 47 | " \"Drawing\"\n", 48 | "\n", 49 | "
NO2 concentration in Europe as acquired by Sentinel 5P on 6th of August 2018 (EO Browser link).
\n", 55 | "\n", 56 | "Blue represents a low NO2 concentration, while red tones are indicating high levels of NO2. We can see a high concentration over large urban agglomerations such as the Randstad area in the Netherlands, the Ruhrgebiet in Germany, Northern Belgium, London and Paris. It is interesting that in particular large industrial cities seem to be affected. This indicates that a large part of the NO2 is coming from the manufacturing industry and vehicle traffic. It is also striking that many image tiles are empty. This is due to these tiles being excluded for low quality (e.g. if no values could be retrieved due to clouds).\n", 57 | "\n", 58 | "Following is a visualization of the UV aerosol index, another of Sentinel-5P's products (for an explanation of the product see here):\n", 59 | "\n", 60 | "\n", 61 | "\n", 62 | "\n", 67 | "\n", 68 | "\n", 69 | "\n", 70 | "\n", 71 | "
\n", 63 | "\n", 64 | " \"Drawing\"\n", 65 | "\n", 66 | "
UV aerosol index over Northern Africa concentration in Europe as acquired by Sentinel 5P on 6th of August 2018 (EO Browser link).
\n", 72 | "\n", 73 | "Aerosols are microscopic particles suspended in air which can be solid or liquid and form an important part of our atmosphere. The sources of these aerosols are extremely varied: dust, volcanic ashes, smoke from fires, fossil fuel combustion and other anthropogenic activities are some examples of aerosols. In the image above, we can clearly see a large concentration of aerosols in the western part of North Africa. This corresponds to Saharan dust particles which are being transported westward by the prevalent wind patterns. The Saharan dust in particular is transported over large distances up until South America where it helps fertilize soils in the Amazon Basin.\n", 74 | "\n", 75 | "Note the diagonal strips in the image. They are the consequence of combining 4 images from neighbouring swaths. Each of the 4 images covers the width of 2600 km (i.e. swath width). The stripes appear since the 4 images are acquired 101 minutes apart (i.e. period) and the UV aerosol index changes in between (source). \n", 76 | "\n", 77 | "In terms of the climate system, aerosols are one of the largest unknowns. They are extremely varied in origin, shape, size and chemical composition and can have both a cooling and a heating effect on our climate. That is because, depending on their specific chemical and physical properties, they either reflect or absorb sunlight. The actual particles are also known as *particulate matter* (the term aerosol generally refers to the suspension of particles in the air) and are generally divided by size into PM2.5 and PM10 with PM2.5 being particles with a diameter < 2.5 \\\\(\\mu\\\\)m and PM10 being particles with a diameter between 2.5 and 10 \\\\(\\mu\\\\)m. In particular PM2.5 is linked to negative human health impacts.\n", 78 | "\n", 79 | "Below is another example of Sentinel-5P aerosol measurements between the 7th and 11th November 2018 off the coast of California. Simply play the video below. For better experience, view the video in full screen mode.\n", 80 | "\n", 81 | "Alternatively, open EO Browser here and then use calendar to visualize images for dates between 7th and 11th November.\n" 82 | ] 83 | }, 84 | { 85 | "cell_type": "markdown", 86 | "metadata": {}, 87 | "source": [ 88 | "