F62d Checkpoint

Import libraries¶

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import ee
import geemap

import ee import geemap

Create an interactive map¶

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Map = geemap.Map(center=[40, -100], zoom=4)

Map = geemap.Map(center=[40, -100], zoom=4)

Add Earth Engine Python script¶

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# Add Earth Engine dataset
image = ee.Image("USGS/SRTMGL1_003")

#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#  Chapter:      F6.2 Scaling Up in Earth Engine
#  Checkpoint:   F62d
#  Authors:      Jillian M. Deines, Stefania Di Tommaso, Nicholas Clinton, Noel Gorelick
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Specify helper functions.
s2mask_tools = require(
    "projects/gee-edu/book:Part F - Fundamentals/F6 - Advanced Topics/F6.2 Scaling Up/modules/s2cloudmask.js"
)

# Set the Region of Interest: Washington, USA.
roi = ee.FeatureCollection("TIGER/2018/States").filter(
    ee.Filter.equals("NAME", "Washington")
)

# Specify grid size in projection, x and y units (based on projection).
projection = "EPSG:4326"
dx = 2.5
dy = 1.5

# Dates over which to create a median composite.
start = ee.Date("2019-03-01")
end = ee.Date("2019-09-01")

# Make grid and visualize.
proj = ee.Projection(projection).scale(dx, dy)
grid = roi.geometry().coveringGrid(proj)

Map.addLayer(roi, {}, "roi")
Map.addLayer(grid, {}, "grid")

# Export info.
assetCollection = "path/to/your/asset/s2_composite_WA"
imageBaseName = "S2_median_"

# Get a list based on grid number.
gridSize = grid.size().getInfo()
gridList = grid.toList(gridSize)

# In each grid cell, export a composite
for i in range(0, gridSize, 1):

    # Extract grid polygon and filter S2 datasets for this region.
    gridCell = ee.Feature(gridList.get(i)).geometry()

    s2Sr = (
        ee.ImageCollection("COPERNICUS/S2_SR")
        .filterDate(start, end)
        .filterBounds(gridCell)
        .select(["B2", "B3", "B4", "B5"])
    )

    s2 = (
        ee.ImageCollection("COPERNICUS/S2")
        .filterDate(start, end)
        .filterBounds(gridCell)
        .select(["B7", "B8", "B8A", "B10"])
    )

    s2c = (
        ee.ImageCollection("COPERNICUS/S2_CLOUD_PROBABILITY")
        .filterDate(start, end)
        .filterBounds(gridCell)
    )

    # Apply the cloud mask.
    withCloudProbability = s2mask_tools.indexJoin(s2Sr, s2c, "cloud_probability")
    withS2L1C = s2mask_tools.indexJoin(withCloudProbability, s2, "l1c")
    masked = ee.ImageCollection(withS2L1C.map(s2mask_tools.maskImage))

    # Generate a median composite and export.
    median = masked.reduce(ee.Reducer.median(), 8)

    # Export.
    imagename = imageBaseName + "tile" + i
    Export.image.toAsset(
        {
            "image": median,
            "description": imagename,
            "assetId": assetCollection + "/" + imagename,
            "scale": 10,
            "region": gridCell,
            "maxPixels": 1e13,
        }
    )


#  -----------------------------------------------------------------------
#  CHECKPOINT
#  -----------------------------------------------------------------------

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: F6.2 Scaling Up in Earth Engine # Checkpoint: F62d # Authors: Jillian M. Deines, Stefania Di Tommaso, Nicholas Clinton, Noel Gorelick # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Specify helper functions. s2mask_tools = require( "projects/gee-edu/book:Part F - Fundamentals/F6 - Advanced Topics/F6.2 Scaling Up/modules/s2cloudmask.js" ) # Set the Region of Interest: Washington, USA. roi = ee.FeatureCollection("TIGER/2018/States").filter( ee.Filter.equals("NAME", "Washington") ) # Specify grid size in projection, x and y units (based on projection). projection = "EPSG:4326" dx = 2.5 dy = 1.5 # Dates over which to create a median composite. start = ee.Date("2019-03-01") end = ee.Date("2019-09-01") # Make grid and visualize. proj = ee.Projection(projection).scale(dx, dy) grid = roi.geometry().coveringGrid(proj) Map.addLayer(roi, {}, "roi") Map.addLayer(grid, {}, "grid") # Export info. assetCollection = "path/to/your/asset/s2_composite_WA" imageBaseName = "S2_median_" # Get a list based on grid number. gridSize = grid.size().getInfo() gridList = grid.toList(gridSize) # In each grid cell, export a composite for i in range(0, gridSize, 1): # Extract grid polygon and filter S2 datasets for this region. gridCell = ee.Feature(gridList.get(i)).geometry() s2Sr = ( ee.ImageCollection("COPERNICUS/S2_SR") .filterDate(start, end) .filterBounds(gridCell) .select(["B2", "B3", "B4", "B5"]) ) s2 = ( ee.ImageCollection("COPERNICUS/S2") .filterDate(start, end) .filterBounds(gridCell) .select(["B7", "B8", "B8A", "B10"]) ) s2c = ( ee.ImageCollection("COPERNICUS/S2_CLOUD_PROBABILITY") .filterDate(start, end) .filterBounds(gridCell) ) # Apply the cloud mask. withCloudProbability = s2mask_tools.indexJoin(s2Sr, s2c, "cloud_probability") withS2L1C = s2mask_tools.indexJoin(withCloudProbability, s2, "l1c") masked = ee.ImageCollection(withS2L1C.map(s2mask_tools.maskImage)) # Generate a median composite and export. median = masked.reduce(ee.Reducer.median(), 8) # Export. imagename = imageBaseName + "tile" + i Export.image.toAsset( { "image": median, "description": imagename, "assetId": assetCollection + "/" + imagename, "scale": 10, "region": gridCell, "maxPixels": 1e13, } ) # ----------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------

Display the interactive map¶

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Map

Map