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Introduction to ClimateSERV

This course will teach you how to use ClimateSERV, a free web-based tool that allows users to download, analyze and interpret historical and forecasted geospatial data.

7 hours to complete
Online course
Progress at your own speed
Start any time
Start when suits you best

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Course image by kjpargete on Freepik

ClimateSERV offers users access to historical and forecasted data for meteorological variables such as precipitation, temperature, as well as biophysical variables like soil moisture, evaporative stress index, and the normalized difference vegetation index.

Users can download, analyze, and visualize these datasets – which are available on a global scale – in two different ways:

In this course we will learn how to use both the ClimateSERV GUI and the ClimateSERVpy API to write Python code to request data from ClimateSERV.

Course content

This course is asynchronous, but we estimate that it will take about 7 hours of study to complete. The course is broken down into four modules as shown below.

Weekly / module breakdown

Learning outcomes

Is this course for you?

  • Target Audience

    The target audience of this course is extremely broad. The ClimateSERV GUI is designed to be user-friendly such that practitioners with limited experience in Earth Observation science can visualize and/or request data. Simultaneously, the API allows more experienced users, especially those with programming experience, to develop more complex workflows. The meteorological data available in ClimateSERV is attractive to students studying atmospheric science, whereas the biophysical data is useful for students working in agriculture and food security. However, because precipitation and soil moisture data can be used as inputs to a wide variety of models, ClimateSERV may be a useful for resource for all disciplines within Earth Observation.

  • Knowledge prerequisites

    Before taking this course, it is recommended that students be familiar with the following subjects:

    Introduction to Optical Remote Sensing 

    • Students should have knowledge of terms like spatial resolution, temporal resolution, raster, visual channels (i.e. true color vs false color)
      If you do not have substantial knowledge regarding the above topics, it is recommended to take this module before proceeding: Introduction to Python 
    • Students should have a working knowledge of numpy and matplotlib libraries
  • Any other prerequisites

    Students will need a computer,  a Google Drive account, and will need to download Google Colaboratory to their Google Drive account.


SERVIR is a joint initiative of NASA, the US Agency for International Development (USAID), and leading geospatial organizations around the world working to apply Earth Observations to address environmental challenges.

We are situated under NASA’s Applied Science and Capacity Building Programs, seeking to bring science down to Earth and affect change. In doing so, we co-develop services that often employ geospatial tools. Over the years, the user base for our tools has grown beyond the intended end user. As such, SERVIR seeks to build global capacity to use our mature tools via online learning.

To learn more about SERVIR, visit our website

License information

Course Materials of this course are Copyright  NASA-USAID SERVIR under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license unless specified otherwise.


For questions, please email: info@geoversity.io

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