Hi everybody，I have 2016-2019 Sentinel 1a GRD IW mode image. Due to the large amount of data, I need to program batch processing data. What are the specific processing steps and how to program them？
have you seen these?
Thank you for your reply. I’ve read part of what you said, but I haven’t finished it completely.
As for the use of RUS, I have sent an email to inquire about it before, and the reply is that the service is only available to EU residents or citizens
you don’t need the RUS virtual machine to replicate the contents of the python-based SAR-processing shown in the video.
OK, I’ll have a try. Thank you！
There is a saying: “Proper preparation prevents pxxx poor performance” that is very appropriate to large batch processing projects. Without knowing your background, it may be helpful to mention the preparation appropriate for batch processing with ESA SNAP and related tools. There are many step-by-step workflow examples, but large-scale processing raises issues such as resource management (memory, CPU’s, mass storage space) that aren’t considered in the examples.
Batch processing generally requires some familiarity with command-line tools on the platform you are using. If you don’t have access to RUS or a similar facility at your institution you may have to do your own installation and configuration, which often requires some command-line expertise.
Although there are GUI systems that can be helpful when running a workflow many times on different data, developing the workflow often requires some experimentation and problem-solving that often require command-line processing. Most batch processing systems use linux, but Windows and macOS are sometimes used. You will find it worthwhile taking the time to become comfortable working with the command-line. For linux LinuxCommand.org is a good reference and has a number of translations. For Windows, the official Microsoft documentation for PowerShell is a good starting point. There are also good on-line courses such as EdX Introduction to Linux.
Python is often useful and works well on all three platforms. See Python.org’s Getting Started.