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For MODIS satellite images how to automate the download, unzip, import and process to point and then to Excel for each climate satellite MODIS layer.

There are 3650 days of imagery to process with 8 layers per day across modis aqua and terra, giving LST AM and PM and the corresponding timestamps.

So this is 29,200 layers to be run over a set of point files to give the corresponding satellite data at each point for each layer type needed.

To me it seems like a massive undertaking but my knowledge of coding and automation is limited

I realise the broadness of the questions to further wittle down my queries from the already useful answers,

  • How do I create such a cloud library of modis data ?

  • How is such data then further automated using Python, specifically are there any examples of such automation or any places I need to go to learn this specific parts of Python?

If this is still too basic I apologise, but I am coming at this from a complete Python beginners perspective.

closed as too broad by Vince, whyzar, tinlyx, nmtoken, Midavalo May 27 '17 at 20:05

Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. Avoid asking multiple distinct questions at once. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.

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This task calls for an automated, scripted processing. You need to write a script which will download the data, unzip it, and then process it. Based on my experience with a similar task, I'll suggest the following pointers:

  • Python is a perfect fit for this task. If you are not comfortable with it, you better spend some time and effort learning it, and getting good at it.
  • While you might have experience in ArcGIS, and people would suggest that you use arcpy, it's worth exploring other APIs like GDAL, numpy & R. In my experience, the performance boost from using other libraries more than makes up for the time spent learning them.
  • The Amount of data which has to be downloaded and processed makes it good fit for the processing to happen in the cloud. The resources & cost required for downloading and storing large amounts of data on to a local workstation can get prohibitively expensive quite quickly. The cost savings of doing this in the cloud can be substantial.

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