“Well, by the angle of the sun he had to be in an n-mile radius of his car. In that area, there are only 4 cliffs with sufficient height to be in that photo with wildfire burn areas in between. And the plants had to be of the genus whateverae due to the shape of their spikes, and those _obviosuly_ only grow on the north face of sedimentary rocks... so it had to be X”
I understand why people get lost.
https://www.star.nesdis.noaa.gov/GOES/sector.php?sat=G17&sec...
Though it’s a trade off! GOES has a really fast return (10-15 minutes, IIRC) but doesn’t do global and has comparatively poor spatial resolution (red at 500m per pixel, many near IR at 1km, LWIR at 2km). So you can get some data every 15 minutes, but for the city of San Francisco it’s only 10x10 pixels.
This is part of why folks like to join high-resolution data with high-temporal frequency data like GOES. Including me! (Well, I hope to get back to my side project using the Descartes Labs API to “learn” high-spatial GOES from the combo of sentinel2 and GOES).
If you do R&D you can also get API access for free or heavily discounted.
This a project backed by the EU and the European Space Agency; no scams there!
Sometimes you have the right person in the right place at the right time. I'm also really interested in learning more about how this person pinpointed the hiker.
If you search for "NMPZ" on YouTube, you'll see that some people have become very very good at this (!).
Even if it's Yandex (Russian) Street View, the CIA should have the resources to download them..