Facing errors in Land degradation notebook run with different geometry. about farmvibes-ai HOT 2 CLOSED

Hello, @click2cloud-SanchitG. Thank you for raising this issue and for the patience in my reply.
I was able to run the notebook with the geometry and time range that you provided and found the same issue.

The problem is not with the workflow or notebook, but with the quality of the output Landsat rasters for this geometry and time range. Below are a few random dates and their landsat and NDVI rasters (I've increased the geometry a bit, to improve the visualization):

Landsat:

NDVI:

The black/white pixels in Landsat/NDVI rasters show pixels with missing data. This seems to happen in the majority of images. Because of this, the workflow is unable to train the pixel-wise linear trend model. The error that you see happens during plotting the linear trend raster, due to an overflow in the axis definition (because the values in the rasters are very large due to the bad prediction).

To deal with cases like this, a possible workaround is filtering out rasters that have a high percentage of missing data, before feeding it to the NDVI + linear trend ops. You would need to break the landsat_ndvi_trend workflow in two parts: one that downloads Landsat, and another that receives the filtered list of Landsat rasters and perform the remaining tasks of the workflow (NDVI computation + linear trend). In between them, you would filter the bad rasters.

Let me know if you have any doubts or if I can help you with anything else.

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Closing this issue for now. Let us know if you have any doubts.

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