Hydrology & water — JRC Global Surface Water (v1.4)¶
JRC's Global Surface Water occurrence band (1984-2021) over a small AOI near the Nile delta — a static ee.Image with no temporal cadence.
Setup¶
The imports for the whole notebook in one place: pyramids provides Dataset (reading + plotting) and earthlens provides the unified EarthLens entry point plus the bundled GEE Catalog.
import os
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
from pyramids.dataset import Dataset as PyramidsDataset
from earthlens import EarthLens
from earthlens.gee import Catalog
Credentials and output directory¶
The notebook reads its GEE service-account credentials from the GEE_SERVICE_ACCOUNT / GEE_SERVICE_KEY environment variables — both must be set before running this cell. Each download is written under a per-notebook out/ directory.
OUT_DIR = Path('out') / 'hydrology-water'
OUT_DIR.mkdir(parents=True, exist_ok=True)
SERVICE_ACCOUNT = os.environ['GEE_SERVICE_ACCOUNT']
SERVICE_KEY = os.environ['GEE_SERVICE_KEY']
print(f'output directory: {OUT_DIR.resolve()}')
Inspect the catalog entry¶
Before downloading anything, look at what the bundled catalog knows about the asset — bands, cadence, license, provider.
cat = Catalog()
ds = cat.get_dataset('JRC/GSW1_4/GlobalSurfaceWater')
Print the headline metadata for the asset so the request below is easy to reason about.
print(f'title: {ds.title}')
print(f'ee_type: {ds.ee_type}')
print(f'spatial_resolution: {ds.spatial_resolution} m')
print(f'extent.start_date: {ds.extent.start_date}')
print(f'extent.end_date: {ds.extent.end_date}')
print(f'default_reducer: {ds.default_reducer}')
print(f'license: {ds.license}')
print(f'provider: {ds.provider}')
print(f'#bands: {len(ds.bands)}')
print(f'band ids (first 5): {list(ds.bands)[:5]}')
Download¶
Tiny AOI ([29.9, 30.1] lat, [31.1, 31.3] lon) at 90.0 m, raw cadence — keeps the synchronous download under EE's 32768-px per-axis cap. The construct → authenticate → download steps are kept on separate lines.
gee = EarthLens(
data_source="gee",
start='1984-03-16',
end='1984-03-17',
dataset='JRC/GSW1_4/GlobalSurfaceWater',
variables=['occurrence'],
aoi=[31.1, 29.9, 31.3, 30.1],
cadence='raw',
path=str(OUT_DIR),
scale=90.0,
reducer='mean',
)
gee.authenticate(service_account=SERVICE_ACCOUNT, service_key=SERVICE_KEY)
download() writes the GeoTIFF(s) to disk and returns the written paths.
paths = gee.download(progress_bar=False)
print(f'wrote {len(paths)} GeoTIFF(s):')
for p in paths:
print(f' {p} ({p.stat().st_size / 1024:.1f} KB)')
download_ok = True
Quick preview¶
Load the first written GeoTIFF through pyramids and read its single band into an array. (pyramids.dataset.Dataset is the project's GeoTIFF/NetCDF wrapper.)
pds = PyramidsDataset.read_file(str(paths[0]))
arr = pds.read_array()
if arr.ndim == 3:
arr = arr[0]
Mask the dataset's nodata value so the colormap isn't pinned to it.
nodata = pds.no_data_value
if nodata is not None:
try:
arr = np.ma.masked_equal(
arr, float(nodata[0] if isinstance(nodata, (list, tuple)) else nodata)
)
except (TypeError, ValueError):
pass
Render the occurrence band and report its value range.
fig, ax = plt.subplots(figsize=(6, 5))
im = ax.imshow(arr, cmap='viridis')
ax.set_title(f'{"JRC/GSW1_4/GlobalSurfaceWater"} / {"occurrence"}')
ax.set_xlabel('x (px)')
ax.set_ylabel('y (px)')
fig.colorbar(im, ax=ax, shrink=0.8)
plt.tight_layout()
plt.show()
print(f'value range: [{float(np.nanmin(arr)):.4g}, {float(np.nanmax(arr)):.4g}]')
Tracking submitted jobs (asynchronous export)¶
The download above uses export_via="url" — a synchronous getDownloadURL round-trip. Nothing was queued, so there's no Earth Engine job to track.
To track an export instead, switch to an asynchronous sink (drive / gcs / asset) and pass wait_for_export=False so .download() returns a TaskInfo at submission time rather than blocking until completion. The cells below submit the same (asset_id, band, AOI, scale) request as an export_via="asset" task into the service account's own asset folder, then walk the four jobs-API calls (list_recent_tasks → wait_for_task_id → ee.data.getAsset → ee.data.deleteAsset) to make the job finish and tidy up. See track-batch-exports.ipynb for a deeper worked example.
Prepare the demo asset folder¶
The export writes into a Folder asset that we own; GEE._export_via_batch writes the image at <asset_id>/<prefix>, so asset_id here is the parent folder. Clear any leftover children from a previous run, then create the folder fresh.
import ee
from earthlens.gee import cancel_task, list_recent_tasks, wait_for_task_id
_proj = ee.data._get_projects_path().removeprefix('projects/')
DEMO_FOLDER = f'projects/{_proj}/assets/earthlens-demo-hydrology-water'
print(f'demo folder: {DEMO_FOLDER}')
Best-effort cleanup of any leftover children and the folder itself, so the folder is empty before we create it below.
try:
for child in ee.data.listAssets({'parent': DEMO_FOLDER}).get('assets', []):
ee.data.deleteAsset(child['name'])
print(f'cleared leftover child: {child["name"]}')
ee.data.deleteAsset(DEMO_FOLDER)
print(f'cleared leftover folder: {DEMO_FOLDER}')
except Exception:
pass
Create the parent folder — EE requires it to exist before a child write.
ee.data.createAsset({'type': 'Folder'}, DEMO_FOLDER)
print(f'created folder: {DEMO_FOLDER}')
Submit¶
Same (asset_id, band, AOI, scale) request as the sync download above, just routed through export_via="asset" + wait_for_export=False. The construct → authenticate steps are split onto their own lines.
async_gee = EarthLens(
data_source="gee",
start='1984-03-16',
end='1984-03-17',
dataset='JRC/GSW1_4/GlobalSurfaceWater',
variables=['occurrence'],
aoi=[31.1, 29.9, 31.3, 30.1],
cadence='raw',
path=str(OUT_DIR),
scale=90.0,
reducer='mean',
export_via='asset',
asset_id=DEMO_FOLDER,
wait_for_export=False,
)
async_gee.authenticate(service_account=SERVICE_ACCOUNT, service_key=SERVICE_KEY)
download() returns a TaskInfo per submitted bucket at the moment the task is queued — no blocking.
submitted = async_gee.download(progress_bar=False)
task_info = submitted[0]
print(f'submitted: id={task_info.id} state={task_info.state}')
print(f' description={task_info.description}')
List + wait¶
list_recent_tasks(description_prefix=...) returns every matching task across the current project. A real workflow would just poll later from a separate process — the wait here exists so the notebook shows the full success path end-to-end.
recent = list_recent_tasks(
description_prefix=task_info.description,
max_age_min=10,
)
print(f'list_recent_tasks matched {len(recent)} task(s):')
for t in recent:
print(f' {t.id} {t.state:<12} {t.description}')
wait_for_task_id blocks until the task reaches a terminal state. On a FAILED / CANCELLED result it raises RuntimeError; we cancel-if-still-running so we don't leak an in-flight task on notebook restart.
try:
final = wait_for_task_id(
task_info.id,
poll_seconds=10,
progress_bar=False,
)
print(f'final state: {final.state}')
except RuntimeError as exc:
print(f'wait_for_task_id raised: {exc}')
try:
cancel_task(task_info.id)
except Exception:
pass
Verify + clean up¶
Confirm the produced asset exists on Earth Engine, then delete it (and the surrounding demo folder) so we don't leak storage between notebook runs. The backend wrote the image at <DEMO_FOLDER>/<task description>.
produced = f'{DEMO_FOLDER}/{task_info.description}'
try:
meta = ee.data.getAsset(produced)
print(f'asset exists: type={meta.get("type")} ' f'name={meta.get("name")}')
ee.data.deleteAsset(produced)
print('asset deleted')
except Exception as exc:
print(f'verify/delete skipped: {exc}')
Always try to tear down the parent folder, even if the verify/delete step above was skipped.
try:
ee.data.deleteAsset(DEMO_FOLDER)
print(f'folder deleted: {DEMO_FOLDER}')
except Exception as exc:
print(f'folder delete skipped: {exc}')
What's on disk¶
The GeoTIFF is left under the per-notebook out/ directory for you to inspect. That directory is .gitignored — re-running the notebook overwrites it.
for p in sorted(OUT_DIR.iterdir()) if OUT_DIR.exists() else []:
print(f'{p} ({p.stat().st_size / 1024:.1f} KB)')