Comments (5)
scheip
commented on July 28, 2024
I can confirm I do not see this behavior with another EE dataset. For example, the only change in the below is switching over to an ERA5 dataset:
# specify study area
ur = [35.89, -81.87] # y, x
ll = [35.83, -81.945] # y, x # full region
coords = [ll[1], ll[0], ll[1], ur[0], ur[1], ur[0], ur[1], ll[0]]
geom = ee.Geometry.Polygon(coords)
# Specify image collections
ic = ee.ImageCollection("ECMWF/ERA5_LAND/MONTHLY_AGGR").filterDate('2016-06-01', '2024-01-01').filterBounds(geom).select('temperature_2m')
ds = xr.open_dataset(
ic,
engine='ee',
crs='EPSG:32617', # utm zone 17n
scale=500,
geometry=geom
)
# plot lai, average across x and y
ds['temperature_2m'].mean(dim=['X', 'Y']).plot()
And this yields the same result every time, as expected.
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noahgolmant
commented on July 28, 2024
Is there a mismatch between the bounds CRS and the requested CRS of the dataset? I am not exactly sure why the issue is non-deterministic as a result. I ran this on my own test area, Pará, Brazil, and observed similar zero values until I adjusted this. When I assume EPSG:4326 for your bounds, I think the boundary is in Antarctica, so the LAI is 0 as expected.
With the original code:
import ee
import geopandas as gpd
from shapely.geometry import mapping
import xarray as xr
import xee
from ingest.consts import SERVICE_PATH, EMAIL
credentials = ee.ServiceAccountCredentials(EMAIL, SERVICE_PATH)
ee.Initialize(credentials)
bounds = gpd.read_file("/home/noah/ingest/configs/data/acre_tiny_test_site.geojson").iloc[0].geometry
geom = ee.Geometry(mapping(bounds))
# Specify image collections
ic = ee.ImageCollection("MODIS/061/MCD15A3H").filterDate('2016-06-01', '2024-01-01').filterBounds(geom).select('Lai')
ds = xr.open_dataset(
ic,
engine='ee',
crs='EPSG:32617',
scale=500,
geometry=geom
)
# plot lai, average across x and y
ds['Lai'].mean(dim=['X', 'Y']).plot()
With the following variant, setting CRS to 4326, we see no zeros and expected seasonal variation:
# Specify image collections
ic = ee.ImageCollection("MODIS/061/MCD15A3H").filterDate('2016-06-01', '2024-01-01').filterBounds(geom).select('Lai')
ds = xr.open_dataset(
ic,
engine='ee',
crs='EPSG:4326',
geometry=geom
)
# plot lai, average across x and y
ds['Lai'].mean(dim=['lon', 'lat']).plot()
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scheip
commented on July 28, 2024
Thank you @noahgolmant for trying this out! I don't know what happened, perhaps an update on GEE's side, but the code I listed in my first message now produces expected results, with no zeros and no code changes. Very strange!
I'd be curious if you re-ran your first code block if you also now get expected results?
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scheip
commented on July 28, 2024
Strange - running the same troubleshooting code pasted above again is resulting in spurious/missing values. Any further thoughts on this?
Copying the troubleshooting code here:
# specify study area
ur = [35.89, -81.87] # y, x
ll = [35.83, -81.945] # y, x # full region
coords = [ll[1], ll[0], ll[1], ur[0], ur[1], ur[0], ur[1], ll[0]]
geom = ee.Geometry.Polygon(coords)
# Specify image collections
ic = ee.ImageCollection("MODIS/061/MCD15A3H").filterDate('2016-06-01', '2024-01-01').filterBounds(geom).select('Lai')
ds = xr.open_dataset(
ic,
engine='ee',
crs='EPSG:32617',
scale=500,
geometry=geom
)
# plot lai, average across x and y
ds['Lai'].mean(dim=['X', 'Y']).plot()
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naschmitz
commented on July 28, 2024
Sounds like this is related to #119. We're looking into it.
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Related Issues (20)
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