152 extract timeseries to point

Uncomment the following line to install geemap if needed.

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# %pip install -U geemap

# %pip install -U geemap

Import libraries¶

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import matplotlib.pyplot as plt
import ee
import geemap

import matplotlib.pyplot as plt import ee import geemap

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Map = geemap.Map()
Map

Map = geemap.Map() Map

Specify Latitude, Longitude, & Date¶

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# Define the latitude and longitude
lat = 28.60
lon = 77.22

# Define time range
start_date = "2000-01-01"
end_date = "2015-12-31"

# Define the latitude and longitude lat = 28.60 lon = 77.22 # Define time range start_date = "2000-01-01" end_date = "2015-12-31"

Extract ERA5-Land Daily Timeseries¶

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# Initialize image collection
image_collection = ee.ImageCollection("ECMWF/ERA5_LAND/DAILY_AGGR")

# Define bands of interest (must match available band names exactly)
band_names = ["temperature_2m_min", "temperature_2m_max", "total_precipitation_sum"]

# Define the scale
scale = 11132

# Extract time series at specified latitude and longitude
result = geemap.extract_timeseries_to_point(
    lat=lat,
    lon=lon,
    image_collection=image_collection,
    band_names=band_names,
    start_date=start_date,
    end_date=end_date,
    scale=scale,
)

# Preview results
print(result.shape)
result.head()

# Initialize image collection image_collection = ee.ImageCollection("ECMWF/ERA5_LAND/DAILY_AGGR") # Define bands of interest (must match available band names exactly) band_names = ["temperature_2m_min", "temperature_2m_max", "total_precipitation_sum"] # Define the scale scale = 11132 # Extract time series at specified latitude and longitude result = geemap.extract_timeseries_to_point( lat=lat, lon=lon, image_collection=image_collection, band_names=band_names, start_date=start_date, end_date=end_date, scale=scale, ) # Preview results print(result.shape) result.head()

Extract CHIRPS Daily Precipitation Timeseries¶

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# Initialize image collection
image_collection = ee.ImageCollection("UCSB-CHG/CHIRPS/DAILY")

# Define the band name
band_names = ["precipitation"]

# Define the scale
scale = 5566

# Extract time series at specified latitude and longitude
result = geemap.extract_timeseries_to_point(
    lat=lat,
    lon=lon,
    image_collection=image_collection,
    band_names=band_names,
    start_date=start_date,
    end_date=end_date,
    scale=scale,
)

# Preview results
print(result.shape)
result.head()

# Initialize image collection image_collection = ee.ImageCollection("UCSB-CHG/CHIRPS/DAILY") # Define the band name band_names = ["precipitation"] # Define the scale scale = 5566 # Extract time series at specified latitude and longitude result = geemap.extract_timeseries_to_point( lat=lat, lon=lon, image_collection=image_collection, band_names=band_names, start_date=start_date, end_date=end_date, scale=scale, ) # Preview results print(result.shape) result.head()

Extract MODIS Terra Vegetation Indices Timeseries¶

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# Initialize image collection
image_collection = ee.ImageCollection("MODIS/061/MOD13Q1")

# Define the band names
band_names = ["NDVI", "EVI"]

# Define the scale
scale = 250

# Extract time series at specified latitude and longitude
result = geemap.extract_timeseries_to_point(
    lat=28.60,
    lon=77.22,
    image_collection=image_collection,
    band_names=band_names,
    start_date=start_date,
    end_date=end_date,
    scale=scale,
)

# Preview results
print(result.shape)
result.head()

# Initialize image collection image_collection = ee.ImageCollection("MODIS/061/MOD13Q1") # Define the band names band_names = ["NDVI", "EVI"] # Define the scale scale = 250 # Extract time series at specified latitude and longitude result = geemap.extract_timeseries_to_point( lat=28.60, lon=77.22, image_collection=image_collection, band_names=band_names, start_date=start_date, end_date=end_date, scale=scale, ) # Preview results print(result.shape) result.head()

Plot the Timeseries¶

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# Simple plot
plt.figure(figsize=(12, 5))

for band in ["NDVI", "EVI"]:
    plt.plot(
        result["time"], result[band] * 0.0001, label=band
    )  # apply scale factor of 0.0001

plt.title(f"NDVI & EVI Time Series at {lat, lon}")
plt.xlabel("Date")
plt.ylabel("Value")
plt.legend()
plt.tight_layout()
plt.show()

# Simple plot plt.figure(figsize=(12, 5)) for band in ["NDVI", "EVI"]: plt.plot( result["time"], result[band] * 0.0001, label=band ) # apply scale factor of 0.0001 plt.title(f"NDVI & EVI Time Series at {lat, lon}") plt.xlabel("Date") plt.ylabel("Value") plt.legend() plt.tight_layout() plt.show()