2.1 Load and Clip Bathymetry (GEBCO) (+4)
2.1 Load and Clip Bathymetry (GEBCO)
import ee
ee.Initialize(project="your-project")
gebco = ee.Image("projects/sat-io/open-datasets/gebco/GEBCO_2023")
bathy = gebco.select("elevation").clip(aoi)
# Get depth statistics over AOI
stats = bathy.reduceRegion(
reducer=ee.Reducer.minMax().combine(
ee.Reducer.mean(), sharedInputs=True
),
geometry=aoi,
scale=500, # metres
maxPixels=1e9
)
print(stats.getInfo())
2.2 Sentinel-2 Composite (Cloud-Free)
s2 = (
ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED")
.filterBounds(aoi)
.filterDate("2024-06-01", "2024-09-30")
.filter(ee.Filter.lt("CLOUDY_PIXEL_PERCENTAGE", 10))
.select(["B2", "B3", "B4", "B8"]) # Blue, Green, Red, NIR
.median() # cloud-free composite
.clip(aoi)
)
2.3 Time-Series: ERA5 Wind Speed
era5 = (
ee.ImageCollection("ECMWF/ERA5/DAILY")
.filterBounds(aoi)
.filterDate("2023-01-01", "2024-01-01")
.select(["mean_2m_air_temperature",
"u_component_of_wind_10m",
"v_component_of_wind_10m"])
)
# Compute wind speed band
def add_wind_speed(img):
ws = img.expression(
"sqrt(u*u + v*v)",
{"u": img.select("u_component_of_wind_10m"),
"v": img.select("v_component_of_wind_10m")}
).rename("wind_speed")
return img.addBands(ws)
era5_ws = era5.map(add_wind_speed)
2.4 Export to GeoTIFF (Google Drive)
task = ee.batch.Export.image.toDrive(
image=bathy,
description="gebco_north_sea",
folder="gee_exports",
fileNamePrefix="gebco_north_sea_500m",
region=aoi,
scale=500,
crs="EPSG:32631", # UTM Zone 31N
maxPixels=1e10,
fileFormat="GeoTIFF"
)
task.start()
# Poll status
import time
while task.active():
status = task.status()
print(f"State: {status['state']}")
time.sleep(30)
print("Export complete:", task.status()["state"])
2.5 geemap Visualisation
import geemap
m = geemap.Map(center=[57.0, -1.0], zoom=6)
vis_bathy = {"min": -200, "max": 0, "palette": ["blue", "white"]}
m.addLayer(bathy, vis_bathy, "GEBCO Bathymetry")
m.add_colorbar(vis_bathy, label="Depth (m)")
m.save("north_sea_bathymetry.html")
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