JavaScript for Google Earth Engine

JavaScript variables ko `const`, `let`, aur commonly encountered `var` syntax se define aur update karna.

JavaScript arrays se values access, update, aur iterate karna.

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

JavaScript values ko `ee.Number`, `ee.String`, `ee.List`, aur `ee.Dictionary` objects mein convert 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 ek image load karke uske bands, properties, 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 banana.

Filtered collection se median composite aur quality mosaic generate 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.

`.map()` se ImageCollection ki har image par reusable processing function apply karna.

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

Shared date property ke through do ImageCollections join 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 imagery ke liye speckle-reduction workflow apply 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.

Predictor image se stratified ya region-based training samples extract karna.

Samples ko reproducible training aur validation subsets mein split 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 disturbance layers create karna.

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.

Forest ya urban change result ko map layers, chart, aur exportable table ke roop mein present 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.

Climate summaries ko vegetation observations ke saath date-based analysis table mein combine karna.

Regional climate trend ko chart aur exportable time-series table mein present karna.

Analysis parameters ko configuration object mein organize karna.

Repeated processing steps ko small reusable functions mein refactor karna.

Multiple regions aur date windows par configurable workflow execute karna.

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

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

Map panels, labels, legends, charts, aur user controls ke saath basic Earth Engine app interface banana.

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

Ek career-relevant end-to-end GEE project ko input selection se validated outputs, exports, aur portfolio presentation tak complete karna.