No Code, No Problems: Exploring Satellite Views with Panoply
Remote sensing or geographic data in general are sometimes really hard to read and visualize using programming languages, despite all the advances. It gets even more complicated when you need to slice these data, since you face projections, indexing and tons of other details. So if you want to have a quick look at your data or make sure that you code is correct, I suggest you to use Panoply by NASA.
Supported Data Formats:
- Read: netCDF4, GRIB, hdf-EOS, hdf4/hdf5, GeoTIFF, and more.
- Generate: CSV, JPEG, PNG, TIFF, PDF.
Compatibility:
- OS: Windows, Linux, MacOS.
Panoply’s interface is an absolute breeze. Simply upload your dataset via “File” → “Open,” and your data neatly lands in the Datasets tab at the lower screen section.
For the purposes of this tutorial I’m going to use MOD09GA product from MODIS Terra, and you can download the file I use from here. After uploading it to Panoply, you can an abundance of different datasets and even subdatasets.
If you click on one of them, you’ll be able to see all the needy-greedy details about the dataset such data types, scale factors, resolution etc.
But let’s skip the geeky stuff! Click on “Data_Fields.”
Right-click on surf_refl_b01_c, then left-click on create plot and hit create button.
Voila! Your first map and a satellite’s snapshot is visualized. But anyone would agree that it looks terrible. So let’s make it human-friendly.
Firstly, we need to slice the region of interest. Click CTRL+1 and you’ll get the plot controls window. Here is everything we need for now.
First of all, let’s choose Map Projection and set it to Equirectangular Regional. After that we need to center our map and specify width and height of our window.
Much better, but the grid feels too sparse. Adjust it using the Grid option. Opt for dashed grid lines, 2.5° spacing, and label every other grid line.
Now let’s work with overlays. Panoply has lots of built-in counturs and layers, but we can pick only three at a time. One layer is already beaing used, as we have continents and oceance. Now lat’s add a layer with national borders and another with rivers.
A couple more tweaks. Modify the title and subtitle!
Lastly, ditch the default color map for a more pleasing one. As band 1 is Red band, I selected red color map, which is much more stylish!
So the final results I exported from Panoply looks like this:
There are many other parameters you can play around with, so make your custom visualizations, without any code lines. Panoply also can create line plots, display arrays of data in different formats and many more, so with proper usage it’s a really powerful tool!
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P.s. I’m extremely passionate about (Geo)Data Science, ML/AI and Climate Change. So if you want to work together on some project pls contact me here or in LinkedIn.
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