JavaScript for Google Earth Engine

JavaScript execution aur Earth Engine ki deferred server-side execution ke beech antar explain karna.

JavaScript values se `ee.Number`, `ee.String`, `ee.List`, aur `ee.Dictionary` objects construct karke unke server-side methods use karna.

Point, line, polygon, aur bounding-box `ee.Geometry` objects create karna.

Geometry aur attribute properties ke saath `ee.Feature` create karna.

Local vector data ko asset ke roop mein upload karke `ee.FeatureCollection` load karna.

Attribute aur spatial conditions ke through `FeatureCollection` filter karna.

Callback function aur `.map()` use karke har feature par server-side operation apply karna.

Console inspection aur limited `getInfo()` use karke client-server mistakes diagnose karna.

Earth Engine Data Catalog se suitable image load karke uske bands, properties, scale factors, aur acquisition metadata inspect karna.

Date, location, aur metadata filters se `ImageCollection` banana.

Raster bands ko select, rename, aur reorder karna.

Image arithmetic aur expressions se spectral index calculate karna.

Visualization parameters se single-band aur false-colour layers display karna.

Pixel masks create, combine, apply, aur invert karna.

Quality-assurance bands se cloud aur cloud-shadow masking function banakar use `ImageCollection` par map karna.

Filtered collection se median composite aur quality mosaic generate karna.

Projection, nominal scale, resampling, aur reprojection choices ko dataset ke according set karna.

`reduceRegion()` se ek area ke andar image summary statistic calculate karna.

`reduceRegions()` se multiple administrative ya field polygons ke zonal statistics calculate karna.

FeatureCollection properties ko aggregate karke grouped summary banana.

`ee.Image.pixelArea()` aur grouped reducers se class-wise area calculate karna.

Image dates aur custom properties set karke processed results ko track karna.

Scale, region, projection, aur file format specify karke processed image export karna.

Selected attributes ke saath analysis table ko CSV ya GeoJSON format mein export karna.

Landsat ya Sentinel-2 collection ke liye reusable optical preprocessing function implement karna.

ImageCollection ke har observation ke liye NDVI, EVI, ya NDWI band add karna.

Field ya region ke liye vegetation-index time-series chart generate karna.

Seasonal date windows se crop-monitoring composites banana.

Historical baseline ke against vegetation-index anomaly calculate karna.

Thresholds ke through crop-condition classes create karna.

Field-wise crop-condition area aur summary statistics export karna.

Optical imagery aur water index threshold se surface-water mask create karna.

Historical observations ki frequency se persistent-water layer derive karna.

Orbit direction, instrument mode, polarization, aur date se Sentinel-1 collection filter karna.

Sentinel-1 VV/VH backscatter units inspect karke pre-event aur post-event median composites banana aur visualize karna.

Pre-event aur post-event SAR composites se flood-change metric calculate karna.

Permanent water aur steep slopes exclude karke candidate flood extent refine karna.

Administrative polygons ke andar flooded area calculate aur export karna.

Reference polygons ko class labels dekar training dataset prepare karna.

Reference features ko fixed random seed se spatially non-overlapping training aur validation subsets mein partition karna.

Predictor image se training aur validation samples separately extract karke class balance inspect karna.

Selected predictor bands ke saath Random Forest classifier train karna.

Trained classifier ko image par apply karke land-cover map banana.

Confusion matrix se overall accuracy, producer’s accuracy, consumer’s accuracy, aur kappa calculate karna.

Feature importance inspect karke predictor-band contribution interpret karna.

Classified raster aur class-wise area table export karna.

Global Forest Change dataset se annual forest-loss pixels select karna.

Tree-cover threshold aur loss year se annual deforestation area calculate karna.

Administrative units ke liye annual forest-loss statistics tabulate karna.

Pre-event aur post-event imagery se NBR aur dNBR calculate karke disturbance-severity classes banana.

Built-up index aur supporting masks se urban candidate pixels derive karna.

Two-date classified maps se urban expansion identify karna.

Land-cover transitions ko grouped reducer se transition-area table mein summarize karna.

CHIRPS, ERA5, ya TerraClimate collection ko variable, date, aur region ke basis par select karna.

Dataset documentation ke according scale factors aur units correctly apply karna.

Monthly images se annual ya seasonal climate summaries calculate karna.

Long-term baseline se rainfall ya temperature anomaly calculate karna.

Rolling-window rainfall totals se simple drought indicator derive karna.

Shared date key aur Earth Engine join se climate summaries ko vegetation observations ke saath analysis table mein combine karna.

Regional climate time series par linear trend estimate karke slope ko chart aur exportable table mein report karna.

Analysis parameters ko configuration object mein organize karna.

Repeated processing steps ko small reusable functions mein refactor karna.

Server-side mapping se configurable workflow ko multiple regions ya date windows par apply karke combined result table banana.

Collection size, expected bands, properties, aur parameter ranges check karke processing se pehle common input failures detect karna.

`maxPixels`, `tileScale`, region size, aur intermediate operations adjust karke memory or timeout errors resolve karna.

Code comments, naming conventions, and a README use karke reproducible GEE project document karna.

Agriculture, forestry, water/flood, urban change, climate, ya LULC mein se ek theme choose karke project question, AOI, time period, datasets, outputs, aur validation plan define karna.

Selected dataset ke liye input-loading aur preprocessing section implement karke intermediate output verify karna.

Project ka core analysis implement karke ek decision-ready map aur summary table generate karna.

Visual cross-check, known event, holdout reference data, ya sensitivity test se output validate karke limitations record karna.

Final map aur table exports generate karke documented script, README, annotated visual, methods summary, aur key findings ko portfolio package mein assemble karna.