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Apply a reducer to all the pixels in a specific region.
Either the reducer must have the same number of inputs as the input image has bands, or it must have a single input and will be repeated for each band.
The region over which to reduce data. Defaults to the footprint of the image's first band.
scale
Float, default: null
A nominal scale in meters of the projection to work in.
crs
Projection, default: null
The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale.
crsTransform
List, default: null
The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection.
bestEffort
Boolean, default: false
If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed.
maxPixels
Long, default: 10000000
The maximum number of pixels to reduce.
tileScale
Float, default: 1
A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-07-13 UTC."],[[["\u003cp\u003e\u003ccode\u003eImage.reduceRegion()\u003c/code\u003e applies a reducer function to all pixels within a specified region of an image.\u003c/p\u003e\n"],["\u003cp\u003eThe reducer can either accept the same number of inputs as the image bands or a single input to be applied to each band.\u003c/p\u003e\n"],["\u003cp\u003eIt returns a dictionary containing the reducer's output, with keys representing band names and values corresponding to the calculated statistic.\u003c/p\u003e\n"],["\u003cp\u003eUsers can define parameters like scale, projection, and geometry to control the region and resolution of the reduction operation.\u003c/p\u003e\n"],["\u003cp\u003eMultiple reducers can be combined to calculate multiple statistics simultaneously, with output dictionary keys reflecting both band and statistic names.\u003c/p\u003e\n"]]],[],null,["# ee.Image.reduceRegion\n\nApply a reducer to all the pixels in a specific region.\n\n\u003cbr /\u003e\n\nEither the reducer must have the same number of inputs as the input image has bands, or it must have a single input and will be repeated for each band.\n\nReturns a dictionary of the reducer's outputs.\n\n| Usage | Returns |\n|---------------------------------------------------------------------------------------------------------------------------------------|------------|\n| Image.reduceRegion`(reducer, `*geometry* `, `*scale* `, `*crs* `, `*crsTransform* `, `*bestEffort* `, `*maxPixels* `, `*tileScale*`)` | Dictionary |\n\n| Argument | Type | Details |\n|----------------|---------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| this: `image` | Image | The image to reduce. |\n| `reducer` | Reducer | The reducer to apply. |\n| `geometry` | Geometry, default: null | The region over which to reduce data. Defaults to the footprint of the image's first band. |\n| `scale` | Float, default: null | A nominal scale in meters of the projection to work in. |\n| `crs` | Projection, default: null | The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. |\n| `crsTransform` | List, default: null | The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. |\n| `bestEffort` | Boolean, default: false | If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. |\n| `maxPixels` | Long, default: 10000000 | The maximum number of pixels to reduce. |\n| `tileScale` | Float, default: 1 | A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. |\n\nExamples\n--------\n\n### Code Editor (JavaScript)\n\n```javascript\n// A Landsat 8 surface reflectance image with SWIR1, NIR, and green bands.\nvar img = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044034_20210508')\n .select(['SR_B6', 'SR_B5', 'SR_B3']);\n\n// Santa Cruz Mountains ecoregion geometry.\nvar geom = ee.FeatureCollection('EPA/Ecoregions/2013/L4')\n .filter('us_l4name == \"Santa Cruz Mountains\"').geometry();\n\n// Display layers on the map.\nMap.setCenter(-122.08, 37.22, 9);\nMap.addLayer(img, {min: 10000, max: 20000}, 'Landsat image');\nMap.addLayer(geom, {color: 'white'}, 'Santa Cruz Mountains ecoregion');\n\n// Calculate median band values within Santa Cruz Mountains ecoregion. It is\n// good practice to explicitly define \"scale\" (or \"crsTransform\") and \"crs\"\n// parameters of the analysis to avoid unexpected results from undesired\n// defaults when e.g. reducing a composite image.\nvar stats = img.reduceRegion({\n reducer: ee.Reducer.median(),\n geometry: geom,\n scale: 30, // meters\n crs: 'EPSG:3310', // California Albers projection\n});\n\n// A dictionary is returned; keys are band names, values are the statistic.\nprint('Median band values, Santa Cruz Mountains ecoregion', stats);\n\n// You can combine reducers to calculate e.g. mean and standard deviation\n// simultaneously. The output dictionary keys are the concatenation of the band\n// names and statistic names, separated by an underscore.\nvar reducer = ee.Reducer.mean().combine({\n reducer2: ee.Reducer.stdDev(),\n sharedInputs: true\n});\nvar multiStats = img.reduceRegion({\n reducer: reducer,\n geometry: geom,\n scale: 30,\n crs: 'EPSG:3310',\n});\nprint('Mean & SD band values, Santa Cruz Mountains ecoregion', multiStats);\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\n# A Landsat 8 surface reflectance image with SWIR1, NIR, and green bands.\nimg = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044034_20210508').select(\n ['SR_B6', 'SR_B5', 'SR_B3']\n)\n\n# Santa Cruz Mountains ecoregion geometry.\ngeom = (\n ee.FeatureCollection('EPA/Ecoregions/2013/L4')\n .filter('us_l4name == \"Santa Cruz Mountains\"')\n .geometry()\n)\n\n# Display layers on the map.\nm = geemap.Map()\nm.set_center(-122.08, 37.22, 9)\nm.add_layer(img, {'min': 10000, 'max': 20000}, 'Landsat image')\nm.add_layer(geom, {'color': 'white'}, 'Santa Cruz Mountains ecoregion')\ndisplay(m)\n\n# Calculate median band values within Santa Cruz Mountains ecoregion. It is\n# good practice to explicitly define \"scale\" (or \"crsTransform\") and \"crs\"\n# parameters of the analysis to avoid unexpected results from undesired\n# defaults when e.g. reducing a composite image.\nstats = img.reduceRegion(\n reducer=ee.Reducer.median(),\n geometry=geom,\n scale=30, # meters\n crs='EPSG:3310', # California Albers projection\n)\n\n# A dictionary is returned keys are band names, values are the statistic.\ndisplay('Median band values, Santa Cruz Mountains ecoregion', stats)\n\n# You can combine reducers to calculate e.g. mean and standard deviation\n# simultaneously. The output dictionary keys are the concatenation of the band\n# names and statistic names, separated by an underscore.\nreducer = ee.Reducer.mean().combine(\n reducer2=ee.Reducer.stdDev(), sharedInputs=True\n)\nmulti_stats = img.reduceRegion(\n reducer=reducer,\n geometry=geom,\n scale=30,\n crs='EPSG:3310',\n)\ndisplay('Mean & SD band values, Santa Cruz Mountains ecoregion', multi_stats)\n```"]]
