Single point of access
The Earth on Demand imagery portal allows access to a variety of EO data sources, so you only have one place to go to build your query
Because our data refresh is limited only by the underlying satellite, we can offer near-real-time (< 5 day old) data across the globe
With multiple output formats, we can help everyone from business analysts to data scientists to unlock insights from EO data
Get to Know the Product
What can I do with satellite image data?
Earth Observing satellite image data is useful for a variety of purposes, from viewing the most recent satellite picture of your hometown, to observing how a glacier changes over time, to making economic measurements by estimating oil inventories and counting cars, to combating climate change through scientific analysis.
Experienced remote sensing scientists understand that the spectral richness and massive scale of this data make it a treasure trove for uncovering insights. However, this imagery is accessible and applicable to anyone, and each individual can explore the Earth's surface and how it changes over time according to their own inspiration.
With Earth OnDemand, we set out to serve everyone who wants to better understand our planet. We offer support for experienced scientists via our Jupyter Notebook export, and we provide natural color images for any user inspired to observe our changing planet.
How do I search for imagery?
- Navigate to a location and zoom in.
- Specify the date range you are interested in.
- Filter out imagery results that might cloud your analysis.
- Use the Selection Tool to draw a box around your exact area of interest (the small blue rectangle in the screenshot).
- Search results are previewed on the map, using the footprints of each satellite platform.
- The wide blue swaths are MODIS, green rectangles are Landsat, and yellow rectangles are Sentinel. The numbers shown indicate the number of multi-spectral images available for the date range specified.
How do I know which platform/satellite operator to choose?
Earth OnDemand provides access to the latest imagery and historical archives of multiple satellite platforms and operators. For our current beta, we offer access to the below public (free) providers:
|Satellite||Operated By||Launch Year||Resolution||Revisit Rate||Bands|
|MODIS||NASA||2002||As fine as 500m||1-2 days||7|
|Landsat||NASA, USGS||2013||As fine as 15m||10-20 days||11|
|Sentinel||ESA||2015||As fine as 10m||2-10 days||12|
Each platform offers unique advantages. For some analyses, well-calibrated spectral data (e.g. measurements including visible and non-visible light) can be the most useful; in this case MODIS would likely be the best candidate. In other analyses, Sentinel’s granular 10m resolution could be best. Thus, the choice of provider will ultimately depend on the type of analysis or exploration being performed. In these cases, cost is not a factor, as this data (both current imagery and historical archives) are publicly available.
How do I download a single natural color image?
After performing a search, click on the footprint (and satellite platform) of the scene in which you are interested. From the list of scenes that appear:
- Get more details of an image by clicking the More Info button.
- Click the Download button to save a JPEG image.
- View a larger version in a new browser window by clicking on the thumbnail image.
How can I export my full query results into a CSV file or Jupyter notebook?
- Click on the CSV download button. This will return an indexed directory of links to the band-specific images for the dates, times, and Area of Interest for your search.
- Click on the Jupyter Notebook download button. This will download a notebook pre-populated with code to request your queried imagery from the selected providers.
How can I analyze this data using Python?
- Click on the Jupyter Notebook download button (see image above). This will download a notebook pre-populated with code that will:
- Submit your original query to the Earth OnDemand REST API.
- Download the results to a CSV file.
- Create a Pandas DataFrame of your query results.
- Step through example tasks in our "Getting Started Notebook".
- Learn how to build your own EO workflows in Python with our "RasterFrames Documentation".