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Tutorial: Timeseries of Satellite Data using Python Binder

Tutorial to learn how to access and analyze time series of Satellite and Satellite-based Data using Python and JupyterLab in the Cloud

Objective

This tutorial aims to provide scientists (or anybbody) who want to use satellite data with the necessary tools for obtaining, temporally analyzing, and visualizing these data using the Cloud. Note: This in not a tutorial on Python per se - there are a myriad of resources for that. The purpose of this tutorial is to learn, through examples, only the necessary Python code and tools required to extract and do simple temporal analysis of satellite data.

We want you to get your toes wet, get to see and use the power of Python, and then maybe you will want to learn more. For that, we encourage you to visit the links on the Resources section at the end of each chapter.

This project, supported by the Better Scientific Software foundation, and the tutorial in oceanography that we use as basis here was originally funded by NASA.

How it works

This tutorial is developed to run and access satellite data on the Cloud.

To launch the tutorial:

  • Click on the binder icon below. It will redirect you to an online environment with the tutorial.
  • It might take some time to load the first time, but eventually you'll be promted with a Jupyter environment, listing the Chapters of this tutorial on your web browser (See Chapter 2 for a brief guide on Jupyter Notebook).
  • Double click on the Chapter you want to work on. It will open in a new tab.
  • At the end of the session, quit the session (top right of the page).
  • You can access the tutorial (repeating this same procedure) as many times as you want.

Binder

This tutorial is divided into Chapters that provide the necessary tools as building blocks. These chapters are stand-alone, so can be skipped if you are familiar with the particular tool presented.

Chapters:

  1. Introduction to Python for Earth Science: Basic concepts about Python

  2. Introduction to Jupyter Lab: How to use the web interface JupyterLab

  3. Python Basics: Basic of Python

4a. Python Tools: xarray, the library that makes satellite data analysis easy

4b. Plotting Tools: Python plotting libraries

  1. Satellite Cloud Data: Background information on Cloud access and data

  2. Ocean Data Example: First cloud data acquisition and analysis on ocean surface temperature data

  3. Atmospheric Data Example: Acquisition and analysis of satellite-based data wind data from the cloud

  4. Land Data Example: Acquisition and analysis of vegetation data from online data

If you want to run it on your computer

The tutorials can also be cloned from this repository and run locally on your computer (you would need good access ttot the internet and make sure your libraries are update and compatible). To get instructions of how to install Python, Jupyter Notebooks, clone the tutorials from Github, and to access the data on the cloud, see here.

Developed by: Marisol García-Reyes (marisolgr@faralloninstitute.org)

Funded by: Better Scientific Software (BSSw) Fellowship Program

Modified from 'Python for Oceanographers' by: Chelle Gentemann and Marisol García-Reyes. Access here, and 'Pangeo Tutorial for AGU Oceans Sciences 2020' here.

About

Tutorials to access and process time series of satellite data in the cloud

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Tutorial: Timeseries of Satellite Data using Python Binder
Tutorial to learn how to access and analyze time series of Satellite and Satellite-based Data using Python and JupyterLab in the Cloud
Objective
This tutorial aims to provide scientists (or anybbody) who want to use satellite data with the necessary tools for obtaining, temporally analyzing, and visualizing these data using the Cloud. Note: This in not a tutorial on Python per se - there are a myriad of resources for that. The purpose of this tutorial is to learn, through examples, only the necessary Python code and tools required to extract and do simple temporal analysis of satellite data.
We want you to get your toes wet, get to see and use the power of Python, and then maybe you will want to learn more. For that, we encourage you to visit the links on the Resources section at the end of each chapter.
This project, supported by the Better Scientific Software foundation, and the tutorial in oceanography that we use as basis here was originally funded by NASA.
How it works
This tutorial is developed to run and access satellite data on the Cloud.
To launch the tutorial:
  • Click on the binder icon below. It will redirect you to an online environment with the tutorial.
  • It might take some time to load the first time, but eventually you'll be promted with a Jupyter environment, listing the Chapters of this tutorial on your web browser (See Chapter 2 for a brief guide on Jupyter Notebook).
  • Double click on the Chapter you want to work on. It will open in a new tab.
  • At the end of the session, quit the session (top right of the page).
  • You can access the tutorial (repeating this same procedure) as many times as you want.
Binder
This tutorial is divided into Chapters that provide the necessary tools as building blocks. These chapters are stand-alone, so can be skipped if you are familiar with the particular tool presented.
Chapters:
  1. Introduction to Python for Earth Science: Basic concepts about Python
  2. Introduction to Jupyter Lab: How to use the web interface JupyterLab
  3. Python Basics: Basic of Python
4a. Python Tools: xarray, the library that makes satellite data analysis easy
4b. Plotting Tools: Python plotting libraries
  1. Satellite Cloud Data: Background information on Cloud access and data
  2. Ocean Data Example: First cloud data acquisition and analysis on ocean surface temperature data
  3. Atmospheric Data Example: Acquisition and analysis of satellite-based data wind data from the cloud
  4. Land Data Example: Acquisition and analysis of vegetation data from online data
If you want to run it on your computer
The tutorials can also be cloned from this repository and run locally on your computer (you would need good access ttot the internet and make sure your libraries are update and compatible). To get instructions of how to install Python, Jupyter Notebooks, clone the tutorials from Github, and to access the data on the cloud, see here.
Developed by: Marisol García-Reyes (marisolgr@faralloninstitute.org)
Funded by: Better Scientific Software (BSSw) Fellowship Program
Modified from 'Python for Oceanographers' by: Chelle Gentemann and Marisol García-Reyes. Access here, and 'Pangeo Tutorial for AGU Oceans Sciences 2020' here.
About
Tutorials to access and process time series of satellite data in the cloud
Resources
Readme
GPL-3.0 license
Activity
Stars
32 stars
Watchers
3 watching
Forks
12 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Tutorial: Timeseries of Satellite Data using Python Binder
Tutorial to learn how to access and analyze time series of Satellite and Satellite-based Data using Python and JupyterLab in the Cloud
Objective
This tutorial aims to provide scientists (or anybbody) who want to use satellite data with the necessary tools for obtaining, temporally analyzing, and visualizing these data using the Cloud. Note: This in not a tutorial on Python per se - there are a myriad of resources for that. The purpose of this tutorial is to learn, through examples, only the necessary Python code and tools required to extract and do simple temporal analysis of satellite data.
We want you to get your toes wet, get to see and use the power of Python, and then maybe you will want to learn more. For that, we encourage you to visit the links on the Resources section at the end of each chapter.
This project, supported by the Better Scientific Software foundation, and the tutorial in oceanography that we use as basis here was originally funded by NASA.
How it works
This tutorial is developed to run and access satellite data on the Cloud.
To launch the tutorial:
  • Click on the binder icon below. It will redirect you to an online environment with the tutorial.
  • It might take some time to load the first time, but eventually you'll be promted with a Jupyter environment, listing the Chapters of this tutorial on your web browser (See Chapter 2 for a brief guide on Jupyter Notebook).
  • Double click on the Chapter you want to work on. It will open in a new tab.
  • At the end of the session, quit the session (top right of the page).
  • You can access the tutorial (repeating this same procedure) as many times as you want.
Binder
This tutorial is divided into Chapters that provide the necessary tools as building blocks. These chapters are stand-alone, so can be skipped if you are familiar with the particular tool presented.
Chapters:
  1. Introduction to Python for Earth Science: Basic concepts about Python
  2. Introduction to Jupyter Lab: How to use the web interface JupyterLab
  3. Python Basics: Basic of Python
4a. Python Tools: xarray, the library that makes satellite data analysis easy
4b. Plotting Tools: Python plotting libraries
  1. Satellite Cloud Data: Background information on Cloud access and data
  2. Ocean Data Example: First cloud data acquisition and analysis on ocean surface temperature data
  3. Atmospheric Data Example: Acquisition and analysis of satellite-based data wind data from the cloud
  4. Land Data Example: Acquisition and analysis of vegetation data from online data
If you want to run it on your computer
The tutorials can also be cloned from this repository and run locally on your computer (you would need good access ttot the internet and make sure your libraries are update and compatible). To get instructions of how to install Python, Jupyter Notebooks, clone the tutorials from Github, and to access the data on the cloud, see here.
Developed by: Marisol García-Reyes (marisolgr@faralloninstitute.org)
Funded by: Better Scientific Software (BSSw) Fellowship Program
Modified from 'Python for Oceanographers' by: Chelle Gentemann and Marisol García-Reyes. Access here, and 'Pangeo Tutorial for AGU Oceans Sciences 2020' here.
About
Tutorials to access and process time series of satellite data in the cloud
Resources
Readme
GPL-3.0 license
Activity
Stars
32 stars
Watchers
3 watching
Forks
12 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Tutorial: Timeseries of Satellite Data using Python Binder
Tutorial to learn how to access and analyze time series of Satellite and Satellite-based Data using Python and JupyterLab in the Cloud
Objective
This tutorial aims to provide scientists (or anybbody) who want to use satellite data with the necessary tools for obtaining, temporally analyzing, and visualizing these data using the Cloud. Note: This in not a tutorial on Python per se - there are a myriad of resources for that. The purpose of this tutorial is to learn, through examples, only the necessary Python code and tools required to extract and do simple temporal analysis of satellite data.
We want you to get your toes wet, get to see and use the power of Python, and then maybe you will want to learn more. For that, we encourage you to visit the links on the Resources section at the end of each chapter.
This project, supported by the Better Scientific Software foundation, and the tutorial in oceanography that we use as basis here was originally funded by NASA.
How it works
This tutorial is developed to run and access satellite data on the Cloud.
To launch the tutorial:
  • Click on the binder icon below. It will redirect you to an online environment with the tutorial.
  • It might take some time to load the first time, but eventually you'll be promted with a Jupyter environment, listing the Chapters of this tutorial on your web browser (See Chapter 2 for a brief guide on Jupyter Notebook).
  • Double click on the Chapter you want to work on. It will open in a new tab.
  • At the end of the session, quit the session (top right of the page).
  • You can access the tutorial (repeating this same procedure) as many times as you want.
Binder
This tutorial is divided into Chapters that provide the necessary tools as building blocks. These chapters are stand-alone, so can be skipped if you are familiar with the particular tool presented.
Chapters:
  1. Introduction to Python for Earth Science: Basic concepts about Python
  2. Introduction to Jupyter Lab: How to use the web interface JupyterLab
  3. Python Basics: Basic of Python
4a. Python Tools: xarray, the library that makes satellite data analysis easy
4b. Plotting Tools: Python plotting libraries
  1. Satellite Cloud Data: Background information on Cloud access and data
  2. Ocean Data Example: First cloud data acquisition and analysis on ocean surface temperature data
  3. Atmospheric Data Example: Acquisition and analysis of satellite-based data wind data from the cloud
  4. Land Data Example: Acquisition and analysis of vegetation data from online data
If you want to run it on your computer
The tutorials can also be cloned from this repository and run locally on your computer (you would need good access ttot the internet and make sure your libraries are update and compatible). To get instructions of how to install Python, Jupyter Notebooks, clone the tutorials from Github, and to access the data on the cloud, see here.
Developed by: Marisol García-Reyes (marisolgr@faralloninstitute.org)
Funded by: Better Scientific Software (BSSw) Fellowship Program
Modified from 'Python for Oceanographers' by: Chelle Gentemann and Marisol García-Reyes. Access here, and 'Pangeo Tutorial for AGU Oceans Sciences 2020' here.
About
Tutorials to access and process time series of satellite data in the cloud
Resources
Readme
GPL-3.0 license
Activity
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32 stars
Watchers
3 watching
Forks
12 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Tutorial: Timeseries of Satellite Data using Python Binder
Tutorial to learn how to access and analyze time series of Satellite and Satellite-based Data using Python and JupyterLab in the Cloud
Objective
This tutorial aims to provide scientists (or anybbody) who want to use satellite data with the necessary tools for obtaining, temporally analyzing, and visualizing these data using the Cloud. Note: This in not a tutorial on Python per se - there are a myriad of resources for that. The purpose of this tutorial is to learn, through examples, only the necessary Python code and tools required to extract and do simple temporal analysis of satellite data.
We want you to get your toes wet, get to see and use the power of Python, and then maybe you will want to learn more. For that, we encourage you to visit the links on the Resources section at the end of each chapter.
This project, supported by the Better Scientific Software foundation, and the tutorial in oceanography that we use as basis here was originally funded by NASA.
How it works
This tutorial is developed to run and access satellite data on the Cloud.
To launch the tutorial:
  • Click on the binder icon below. It will redirect you to an online environment with the tutorial.
  • It might take some time to load the first time, but eventually you'll be promted with a Jupyter environment, listing the Chapters of this tutorial on your web browser (See Chapter 2 for a brief guide on Jupyter Notebook).
  • Double click on the Chapter you want to work on. It will open in a new tab.
  • At the end of the session, quit the session (top right of the page).
  • You can access the tutorial (repeating this same procedure) as many times as you want.
Binder
This tutorial is divided into Chapters that provide the necessary tools as building blocks. These chapters are stand-alone, so can be skipped if you are familiar with the particular tool presented.
Chapters:
  1. Introduction to Python for Earth Science: Basic concepts about Python
  2. Introduction to Jupyter Lab: How to use the web interface JupyterLab
  3. Python Basics: Basic of Python
4a. Python Tools: xarray, the library that makes satellite data analysis easy
4b. Plotting Tools: Python plotting libraries
  1. Satellite Cloud Data: Background information on Cloud access and data
  2. Ocean Data Example: First cloud data acquisition and analysis on ocean surface temperature data
  3. Atmospheric Data Example: Acquisition and analysis of satellite-based data wind data from the cloud
  4. Land Data Example: Acquisition and analysis of vegetation data from online data
If you want to run it on your computer
The tutorials can also be cloned from this repository and run locally on your computer (you would need good access ttot the internet and make sure your libraries are update and compatible). To get instructions of how to install Python, Jupyter Notebooks, clone the tutorials from Github, and to access the data on the cloud, see here.
Developed by: Marisol García-Reyes (marisolgr@faralloninstitute.org)
Funded by: Better Scientific Software (BSSw) Fellowship Program
Modified from 'Python for Oceanographers' by: Chelle Gentemann and Marisol García-Reyes. Access here, and 'Pangeo Tutorial for AGU Oceans Sciences 2020' here.
About
Tutorials to access and process time series of satellite data in the cloud
Resources
Readme
GPL-3.0 license
Activity
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Watchers
3 watching
Forks
12 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Tutorial: Timeseries of Satellite Data using Python Binder
Tutorial to learn how to access and analyze time series of Satellite and Satellite-based Data using Python and JupyterLab in the Cloud
Objective
This tutorial aims to provide scientists (or anybbody) who want to use satellite data with the necessary tools for obtaining, temporally analyzing, and visualizing these data using the Cloud. Note: This in not a tutorial on Python per se - there are a myriad of resources for that. The purpose of this tutorial is to learn, through examples, only the necessary Python code and tools required to extract and do simple temporal analysis of satellite data.
We want you to get your toes wet, get to see and use the power of Python, and then maybe you will want to learn more. For that, we encourage you to visit the links on the Resources section at the end of each chapter.
This project, supported by the Better Scientific Software foundation, and the tutorial in oceanography that we use as basis here was originally funded by NASA.
How it works
This tutorial is developed to run and access satellite data on the Cloud.
To launch the tutorial:
  • Click on the binder icon below. It will redirect you to an online environment with the tutorial.
  • It might take some time to load the first time, but eventually you'll be promted with a Jupyter environment, listing the Chapters of this tutorial on your web browser (See Chapter 2 for a brief guide on Jupyter Notebook).
  • Double click on the Chapter you want to work on. It will open in a new tab.
  • At the end of the session, quit the session (top right of the page).
  • You can access the tutorial (repeating this same procedure) as many times as you want.
Binder
This tutorial is divided into Chapters that provide the necessary tools as building blocks. These chapters are stand-alone, so can be skipped if you are familiar with the particular tool presented.
Chapters:
  1. Introduction to Python for Earth Science: Basic concepts about Python
  2. Introduction to Jupyter Lab: How to use the web interface JupyterLab
  3. Python Basics: Basic of Python
4a. Python Tools: xarray, the library that makes satellite data analysis easy
4b. Plotting Tools: Python plotting libraries
  1. Satellite Cloud Data: Background information on Cloud access and data
  2. Ocean Data Example: First cloud data acquisition and analysis on ocean surface temperature data
  3. Atmospheric Data Example: Acquisition and analysis of satellite-based data wind data from the cloud
  4. Land Data Example: Acquisition and analysis of vegetation data from online data
If you want to run it on your computer
The tutorials can also be cloned from this repository and run locally on your computer (you would need good access ttot the internet and make sure your libraries are update and compatible). To get instructions of how to install Python, Jupyter Notebooks, clone the tutorials from Github, and to access the data on the cloud, see here.
Developed by: Marisol García-Reyes (marisolgr@faralloninstitute.org)
Funded by: Better Scientific Software (BSSw) Fellowship Program
Modified from 'Python for Oceanographers' by: Chelle Gentemann and Marisol García-Reyes. Access here, and 'Pangeo Tutorial for AGU Oceans Sciences 2020' here.
About
Tutorials to access and process time series of satellite data in the cloud
Resources
Readme
GPL-3.0 license
Activity
Stars
32 stars
Watchers
3 watching
Forks
12 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Tutorial: Timeseries of Satellite Data using Python Binder
Tutorial to learn how to access and analyze time series of Satellite and Satellite-based Data using Python and JupyterLab in the Cloud
Objective
This tutorial aims to provide scientists (or anybbody) who want to use satellite data with the necessary tools for obtaining, temporally analyzing, and visualizing these data using the Cloud. Note: This in not a tutorial on Python per se - there are a myriad of resources for that. The purpose of this tutorial is to learn, through examples, only the necessary Python code and tools required to extract and do simple temporal analysis of satellite data.
We want you to get your toes wet, get to see and use the power of Python, and then maybe you will want to learn more. For that, we encourage you to visit the links on the Resources section at the end of each chapter.
This project, supported by the Better Scientific Software foundation, and the tutorial in oceanography that we use as basis here was originally funded by NASA.
How it works
This tutorial is developed to run and access satellite data on the Cloud.
To launch the tutorial:
  • Click on the binder icon below. It will redirect you to an online environment with the tutorial.
  • It might take some time to load the first time, but eventually you'll be promted with a Jupyter environment, listing the Chapters of this tutorial on your web browser (See Chapter 2 for a brief guide on Jupyter Notebook).
  • Double click on the Chapter you want to work on. It will open in a new tab.
  • At the end of the session, quit the session (top right of the page).
  • You can access the tutorial (repeating this same procedure) as many times as you want.
Binder
This tutorial is divided into Chapters that provide the necessary tools as building blocks. These chapters are stand-alone, so can be skipped if you are familiar with the particular tool presented.
Chapters:
  1. Introduction to Python for Earth Science: Basic concepts about Python
  2. Introduction to Jupyter Lab: How to use the web interface JupyterLab
  3. Python Basics: Basic of Python
4a. Python Tools: xarray, the library that makes satellite data analysis easy
4b. Plotting Tools: Python plotting libraries
  1. Satellite Cloud Data: Background information on Cloud access and data
  2. Ocean Data Example: First cloud data acquisition and analysis on ocean surface temperature data
  3. Atmospheric Data Example: Acquisition and analysis of satellite-based data wind data from the cloud
  4. Land Data Example: Acquisition and analysis of vegetation data from online data
If you want to run it on your computer
The tutorials can also be cloned from this repository and run locally on your computer (you would need good access ttot the internet and make sure your libraries are update and compatible). To get instructions of how to install Python, Jupyter Notebooks, clone the tutorials from Github, and to access the data on the cloud, see here.
Developed by: Marisol García-Reyes (marisolgr@faralloninstitute.org)
Funded by: Better Scientific Software (BSSw) Fellowship Program
Modified from 'Python for Oceanographers' by: Chelle Gentemann and Marisol García-Reyes. Access here, and 'Pangeo Tutorial for AGU Oceans Sciences 2020' here.
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Tutorials to access and process time series of satellite data in the cloud
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Tutorial: Timeseries of Satellite Data using Python Binder
Tutorial to learn how to access and analyze time series of Satellite and Satellite-based Data using Python and JupyterLab in the Cloud
Objective
This tutorial aims to provide scientists (or anybbody) who want to use satellite data with the necessary tools for obtaining, temporally analyzing, and visualizing these data using the Cloud. Note: This in not a tutorial on Python per se - there are a myriad of resources for that. The purpose of this tutorial is to learn, through examples, only the necessary Python code and tools required to extract and do simple temporal analysis of satellite data.
We want you to get your toes wet, get to see and use the power of Python, and then maybe you will want to learn more. For that, we encourage you to visit the links on the Resources section at the end of each chapter.
This project, supported by the Better Scientific Software foundation, and the tutorial in oceanography that we use as basis here was originally funded by NASA.
How it works
This tutorial is developed to run and access satellite data on the Cloud.
To launch the tutorial:
  • Click on the binder icon below. It will redirect you to an online environment with the tutorial.
  • It might take some time to load the first time, but eventually you'll be promted with a Jupyter environment, listing the Chapters of this tutorial on your web browser (See Chapter 2 for a brief guide on Jupyter Notebook).
  • Double click on the Chapter you want to work on. It will open in a new tab.
  • At the end of the session, quit the session (top right of the page).
  • You can access the tutorial (repeating this same procedure) as many times as you want.
Binder
This tutorial is divided into Chapters that provide the necessary tools as building blocks. These chapters are stand-alone, so can be skipped if you are familiar with the particular tool presented.
Chapters:
  1. Introduction to Python for Earth Science: Basic concepts about Python
  2. Introduction to Jupyter Lab: How to use the web interface JupyterLab
  3. Python Basics: Basic of Python
4a. Python Tools: xarray, the library that makes satellite data analysis easy
4b. Plotting Tools: Python plotting libraries
  1. Satellite Cloud Data: Background information on Cloud access and data
  2. Ocean Data Example: First cloud data acquisition and analysis on ocean surface temperature data
  3. Atmospheric Data Example: Acquisition and analysis of satellite-based data wind data from the cloud
  4. Land Data Example: Acquisition and analysis of vegetation data from online data
If you want to run it on your computer
The tutorials can also be cloned from this repository and run locally on your computer (you would need good access ttot the internet and make sure your libraries are update and compatible). To get instructions of how to install Python, Jupyter Notebooks, clone the tutorials from Github, and to access the data on the cloud, see here.
Developed by: Marisol García-Reyes (marisolgr@faralloninstitute.org)
Funded by: Better Scientific Software (BSSw) Fellowship Program
Modified from 'Python for Oceanographers' by: Chelle Gentemann and Marisol García-Reyes. Access here, and 'Pangeo Tutorial for AGU Oceans Sciences 2020' here.
About
Tutorials to access and process time series of satellite data in the cloud
Resources
Readme
GPL-3.0 license
Activity
Stars
32 stars
Watchers
3 watching
Forks
12 forks
Report repository
Releases
Packages
Contributors
Languages