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Save alexgleith/dc49156aab4b9270b0a0f145bd7fa0ce to your computer and use it in GitHub Desktop.
Hey @krishnaglodha
At the top, add import odc.geo
and then you can use the odc
extension for xarrays, so you can do:
data.red.isel(time=0).odc.write_cog("out.tif")
Nice notebook. You can filter cloud cover like this:
search = client.search(collections = [collection],
bbox = tas_bbox,
datetime = '2023-01-01/2023-12-31',
query = {"eo:cloud_cover":{"lt":0.2}},
max_items=10,
)
You can filter cloud cover like this:
Hey @zxdawn, you can filter out scenes like that, but often you want to keep all the valid pixels you can, in which case, doing a per-pixel cloud mask is better.
That's not covered in this simple example, but I did include it in this different example .
@alexgleith Thanks for the tip! Actually, I'm still confused about the eo:cloud_cover
option. Is that the cloud fraction over the bbox or the whole scene?
Is that the cloud fraction over the bbox or the whole scene?
It's for each of the individual scenes (STAC Items).
Thank you for notebook.
Can you tell me what is tas_bbox and how can I use latitude and longitude instead?
Hi @shahriarEbram I've expanded the comments to describe what each step is doing. Hope that helps.
Hi, can you please tell me how can I loop through multiple tiles and download the raw data in my local machine ?