Google Earth Engine

Google Earth Engine

Purpose: use GEE's server-side model correctly. The recurring failure modes are client/server confusion (calling .getInfo() in loops, Python if on server objects), unbounded computation (timeouts from unscaled reductions), and silent default scales (statistics computed at the wrong resolution).

Mental model — everything is deferred

ee.Image, ee.ImageCollection, ee.FeatureCollection are server-side descriptions, not data. Nothing computes until an output is requested (getInfo, export, map tile). Consequences:

• Never use Python if/for on server values — use ee.Algorithms.If

sparingly, prefer .map() + filters. A Python loop that calls .getInfo() per element is the #1 GEE performance bug.

• .getInfo() blocks and transfers; use it for tiny scalars only.

Anything sized → Export (to Drive/GCS/Asset).

• Debug with .aggregate_array(), .first(), .limit(3) probes — not by

printing whole collections.

Canonical pipeline (Sentinel-2 cloud-free composite)

import ee
ee.Initialize(project="my-project")

aoi = ee.Geometry.Rectangle([27.0, 38.3, 27.4, 38.6])

def mask_s2(img):
    # Cloud Score+ is the current best practice (threshold ~0.5-0.65)
    cs = img.linkCollection(csplus, ["cs_cdf"]).select("cs_cdf")
    return img.updateMask(cs.gte(0.6))

csplus = ee.ImageCollection("GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED")
s2 = (ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED")
      .filterBounds(aoi)
      .filterDate("2025-05-01", "2025-09-30")
      .map(mask_s2))
composite = s2.median().clip(aoi)
ndvi = composite.normalizedDifference(["B8", "B4"]).rename("ndvi")

Collection choices: S2_SR_HARMONIZED (post-2022 offset harmonized), LANDSAT/LC08/C02/T1_L2 + friends (apply scale factors: optical *0.0000275 - 0.2), MODIS/061/... for daily/coarse, ERA5-Land for climate. Record collection IDs + date filters in the deliverable.

Reducers and zonal statistics — scale is not optional

stats = ndvi.reduceRegions(
    collection=districts,
    reducer=ee.Reducer.mean().combine(ee.Reducer.stdDev(), sharedInputs=True),
    scale=10,                    # ALWAYS explicit — native resolution
    tileScale=4,                 # raise when "computation timed out"
)

• scale defaults to the map zoom level in some paths — silently coarse

statistics. Always set it to the data's native resolution (or state the deliberate coarsening).

• bestEffort=True silently degrades scale to fit limits — avoid in

analysis; prefer tileScale + exports.

• Large reductions → Export.table.toDrive, not .getInfo().
• Weighted vs unweighted reducers differ at polygon edges

(.unweighted() for counts of whole pixels); state which you used.

Time series

• Build per-period composites with a mapped function over

ee.List.sequence of dates (monthly/seasonal medians), then reduce — don't export daily stacks you'll aggregate anyway.

• For per-pixel trends: ee.Reducer.sensSlope() (robust) or

linearFit; harmonic regression (.addBands of sin/cos terms) for phenology. Mask by count of valid observations — trends from 4 pixels of 200 possible are noise; report the count band.

• For break detection at archive scale (LandTrendr/CCDC available in GEE),

method selection follows change-detection.

Classification in GEE

ee.Classifier.smileRandomForest covers most cases. Training samples via image.sampleRegions; split train/test spatially (add a grid-cell attribute and filter — random randomColumn splits leak; see ml-experiment-standards → references/spatial-cv-protocol.md). Report per-class accuracy from errorMatrix; area estimates from a classified map still need design-based adjustment (change-detection / Olofsson).

Exports and hand-off

• Export.image.toDrive/toCloudStorage with explicit region, scale,

crs, maxPixels; use crsTransform when pixel alignment with an existing raster matters.

• Export > ~10⁸ pixels: shard by tiles or use toAsset intermediate.
• Hand off to the local Python stack (rasterio/xarray) via COG exports, or

xee for xarray-native access; visualize interactively with geemap.

Quotas and etiquette

Batch tasks queue (check task status; don't fire hundreds blindly). Interactive requests time out at ~5 min — long jobs go to batch export. Cache intermediate products as assets when a pipeline reuses them.

Verification protocol

1. Probe: composite.select("B4").projection().nominalScale().getInfo()

and band names — confirms scale/CRS assumptions before reductions.

1. Visual check in geemap at 2 zoom levels vs a basemap.
2. Cross-check one zonal statistic against a local computation on an

exported clip (catches scale/masking discrepancies).

1. Report: collection IDs, date ranges, mask method + threshold, scale,

reducer types.

Pitfalls checklist

• .getInfo() inside a loop (move logic server-side).
• Missing scale in reduceRegion(s) → zoom-dependent statistics.
• Landsat C2 used without scale factors → reflectance > 1.
• bestEffort=True hiding resolution degradation.
• Median composite including cloudy pixels (mask BEFORE reduce).
• Python conditionals on server-side objects (always false-y).
• Trend maps without valid-observation-count masking.

Execution contract

• Workflow: define collection and period; build a server-side mask and transform pipeline; test on a small region; compute; verify scale and projection; export reproducibly.
• Decision rules: use Earth Engine for planetary archives and scalable aggregation, local tools for sensitive or offline data, and batch exports for work beyond interactive limits.
• Verification protocol: probe bands, projection, scale, masks, and observation counts; inspect spatial samples; cross-check one exported statistic locally; record collection versions and parameters.
• Failure modes: stop for client-side loops, implicit scale, masked-pixel bias, quota-driven silent degradation, expired assets, or unbounded region operations.
• Deliverables: runnable script, collection and date manifest, mask and reducer parameters, task/export settings, verification evidence, and exported asset inventory.
• Source freshness: consult [the authoritative source registry](references/authoritative-sources.md) at execution time for catalog, API, quota, and policy changes.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: muend
• Source: muend/geoai-skills
• License: MIT

Install and usage instructions live in the source repository linked above.