F42d 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:      F4.2 Aggregating Images for Time Series
#  Checkpoint:   F42d
#  Author:       Ujaval Gandhi
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

chirps = ee.ImageCollection("UCSB-CHG/CHIRPS/PENTAD")
year = 2019
startDate = ee.Date.fromYMD(year, 1, 1)

endDate = startDate.advance(1, "year")

yearFiltered = chirps.filter(ee.Filter.date(startDate, endDate))
print(yearFiltered, "Date-filtered CHIRPS images")

print(startDate, "Start date")
print(endDate, "End date")

print("Start date as timestamp", startDate.millis())
print("End date as timestamp", endDate.millis())

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

# Aggregate this time series to compute monthly images.
# Create a list of months
months = ee.List.sequence(1, 12)


# Write a function that takes a month number
# and returns a monthly image.
def createMonthlyImage(beginningMonth):
    startDate = ee.Date.fromYMD(year, beginningMonth, 1)
    endDate = startDate.advance(1, "month")
    monthFiltered = yearFiltered.filter(ee.Filter.date(startDate, endDate))

    # Calculate total precipitation.
    total = monthFiltered.reduce(ee.Reducer.sum())
    return total.set(
        {
            "system:time_start": startDate.millis(),
            "system:time_end": endDate.millis(),
            "year": year,
            "month": beginningMonth,
        }
    )


# map() the function on the list of months
# This creates a list with images for each month in the list
monthlyImages = months.map(createMonthlyImage)

# Create an ee.ImageCollection.
monthlyCollection = ee.ImageCollection.fromImages(monthlyImages)
print(monthlyCollection)

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

# Create a point with coordinates for the city of Bengaluru, India.
point = ee.Geometry.Point(77.5946, 12.9716)

chart = ui.Chart.image.series(
    {
        "imageCollection": monthlyCollection,
        "region": point,
        "reducer": ee.Reducer.mean(),
        "scale": 5566,
    }
)
print(chart)


chart = ui.Chart.image.series(
    {
        "imageCollection": monthlyCollection,
        "region": point,
        "reducer": ee.Reducer.mean(),
        "scale": 5566,
    }
).setOptions(
    {
        "lineWidth": 1,
        "pointSize": 3,
        "title": "Monthly Rainfall at Bengaluru",
        "vAxis": {"title": "Rainfall (mm)"},
        "hAxis": {"title": "Month", "gridlines": {"count": 12}},
    }
)
print(chart)

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

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: F4.2 Aggregating Images for Time Series # Checkpoint: F42d # Author: Ujaval Gandhi # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ chirps = ee.ImageCollection("UCSB-CHG/CHIRPS/PENTAD") year = 2019 startDate = ee.Date.fromYMD(year, 1, 1) endDate = startDate.advance(1, "year") yearFiltered = chirps.filter(ee.Filter.date(startDate, endDate)) print(yearFiltered, "Date-filtered CHIRPS images") print(startDate, "Start date") print(endDate, "End date") print("Start date as timestamp", startDate.millis()) print("End date as timestamp", endDate.millis()) # ----------------------------------------------------------------------- # CHECKPOINT # ----------------------------------------------------------------------- # Aggregate this time series to compute monthly images. # Create a list of months months = ee.List.sequence(1, 12) # Write a function that takes a month number # and returns a monthly image. def createMonthlyImage(beginningMonth): startDate = ee.Date.fromYMD(year, beginningMonth, 1) endDate = startDate.advance(1, "month") monthFiltered = yearFiltered.filter(ee.Filter.date(startDate, endDate)) # Calculate total precipitation. total = monthFiltered.reduce(ee.Reducer.sum()) return total.set( { "system:time_start": startDate.millis(), "system:time_end": endDate.millis(), "year": year, "month": beginningMonth, } ) # map() the function on the list of months # This creates a list with images for each month in the list monthlyImages = months.map(createMonthlyImage) # Create an ee.ImageCollection. monthlyCollection = ee.ImageCollection.fromImages(monthlyImages) print(monthlyCollection) # ----------------------------------------------------------------------- # CHECKPOINT # ----------------------------------------------------------------------- # Create a point with coordinates for the city of Bengaluru, India. point = ee.Geometry.Point(77.5946, 12.9716) chart = ui.Chart.image.series( { "imageCollection": monthlyCollection, "region": point, "reducer": ee.Reducer.mean(), "scale": 5566, } ) print(chart) chart = ui.Chart.image.series( { "imageCollection": monthlyCollection, "region": point, "reducer": ee.Reducer.mean(), "scale": 5566, } ).setOptions( { "lineWidth": 1, "pointSize": 3, "title": "Monthly Rainfall at Bengaluru", "vAxis": {"title": "Rainfall (mm)"}, "hAxis": {"title": "Month", "gridlines": {"count": 12}}, } ) print(chart) # ----------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------

Display the interactive map¶

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Map

Map