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Landsat 8 BQA band has smaller extent than other bands (Earth Engine)

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I am using Landsat 8 TOA imagery and noticed that image.geometry() returns an area larger than the BQA band, resulting in gaps along the edges where there are no values. This is a problem for me because when I export a binary image masked with the BQA band, these gaps are automatically filled with zeroes and easily confused with real data zeroes.

Is this a known issue with Landsat imagery, or is it a GEE thing? And are there any ideas for how I could get around this? I am trying to clip to the BQA band using image.select('BQA').geometry() but it gives the same extent as the image.

Here is some code showing the problem:
https://code.earthengine.google.com/f64e94126d3835241f8202adad4f5539

In the code above, I am stacking a bunch of data into an image for export into Python for predicting floods. I'm retrieving a Landsat 8 TOA image, detecting flood waters, masking out clouds with the BQA band, and stacking the flood image and other auxiliary data into one output image.

I then export this image to do classifier training in Python. I noticed the exported image had a lot of padded zeros along the side, and realized that this is due to the BQA band in the Landsat image having a smaller extent than the other bands.

Thanks for any possible insight.

python google-earth-engine

share

asked 8 mins ago

la_lechela_leche

576

add a comment | 

0

I am using Landsat 8 TOA imagery and noticed that image.geometry() returns an area larger than the BQA band, resulting in gaps along the edges where there are no values. This is a problem for me because when I export a binary image masked with the BQA band, these gaps are automatically filled with zeroes and easily confused with real data zeroes.

Is this a known issue with Landsat imagery, or is it a GEE thing? And are there any ideas for how I could get around this? I am trying to clip to the BQA band using image.select('BQA').geometry() but it gives the same extent as the image.

Here is some code showing the problem:
https://code.earthengine.google.com/f64e94126d3835241f8202adad4f5539

In the code above, I am stacking a bunch of data into an image for export into Python for predicting floods. I'm retrieving a Landsat 8 TOA image, detecting flood waters, masking out clouds with the BQA band, and stacking the flood image and other auxiliary data into one output image.

I then export this image to do classifier training in Python. I noticed the exported image had a lot of padded zeros along the side, and realized that this is due to the BQA band in the Landsat image having a smaller extent than the other bands.

Thanks for any possible insight.

python google-earth-engine

share

asked 8 mins ago

la_lechela_leche

576

add a comment | 

I am using Landsat 8 TOA imagery and noticed that image.geometry() returns an area larger than the BQA band, resulting in gaps along the edges where there are no values. This is a problem for me because when I export a binary image masked with the BQA band, these gaps are automatically filled with zeroes and easily confused with real data zeroes.

Is this a known issue with Landsat imagery, or is it a GEE thing? And are there any ideas for how I could get around this? I am trying to clip to the BQA band using image.select('BQA').geometry() but it gives the same extent as the image.

Here is some code showing the problem:
https://code.earthengine.google.com/f64e94126d3835241f8202adad4f5539

In the code above, I am stacking a bunch of data into an image for export into Python for predicting floods. I'm retrieving a Landsat 8 TOA image, detecting flood waters, masking out clouds with the BQA band, and stacking the flood image and other auxiliary data into one output image.

I then export this image to do classifier training in Python. I noticed the exported image had a lot of padded zeros along the side, and realized that this is due to the BQA band in the Landsat image having a smaller extent than the other bands.

Thanks for any possible insight.

python google-earth-engine

share

asked 8 mins ago

la_lechela_leche

576

share

asked 8 mins ago

la_lechela_leche

576

share

asked 8 mins ago

la_lechela_leche

576

asked 8 mins ago

la_lechela_leche

576

asked 8 mins ago

la_lechela_leche

576