99 landsat 9

Uncomment the following line to install geemap if needed.

Landsat 9 was successfully launched on Sept. 27, 2021. USGS has been providing Landsat data to the public since Feb. 10, 2022. Landsat 9 data can be downloaded from EarthExplorer. The Earth Engine team has been ingesting Landsat 9 into the Public Data Catalog. As of Feb. 14, 2022, although Landsat 9 data have not been publicly listed on the Earth Engine Datasets page, you can access the data through ee.ImageCollection('LANDSAT/LC09/C02/T1_L2') .

In [1]:

# !pip install geemap

# !pip install geemap

Import libraries.

In [2]:

import ee
import geemap

import ee import geemap

Create an interactive map.

In [3]:

Map = geemap.Map()

Map = geemap.Map()

Find out how many Landsat imgaes are available.

In [4]:

collection = ee.ImageCollection('LANDSAT/LC09/C02/T1_L2')
print(collection.size().getInfo())

collection = ee.ImageCollection('LANDSAT/LC09/C02/T1_L2') print(collection.size().getInfo())

37957

Create a median composite.

In [5]:

median = collection.median()

median = collection.median()

Apply scaling factors. See https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_L2#bands

In [6]:

def apply_scale_factors(image):
    opticalBands = image.select('SR_B.').multiply(0.0000275).add(-0.2)
    thermalBands = image.select('ST_B.*').multiply(0.00341802).add(149.0)
    return image.addBands(opticalBands, None, True).addBands(thermalBands, None, True)

def apply_scale_factors(image): opticalBands = image.select('SR_B.').multiply(0.0000275).add(-0.2) thermalBands = image.select('ST_B.*').multiply(0.00341802).add(149.0) return image.addBands(opticalBands, None, True).addBands(thermalBands, None, True)

In [7]:

dataset = apply_scale_factors(median)

dataset = apply_scale_factors(median)

Specify visualization parameters.

In [8]:

vis_natural = {
    'bands': ['SR_B4', 'SR_B3', 'SR_B2'],
    'min': 0.0,
    'max': 0.3,
}

vis_nir = {
    'bands': ['SR_B5', 'SR_B4', 'SR_B3'],
    'min': 0.0,
    'max': 0.3,
}

vis_natural = { 'bands': ['SR_B4', 'SR_B3', 'SR_B2'], 'min': 0.0, 'max': 0.3, } vis_nir = { 'bands': ['SR_B5', 'SR_B4', 'SR_B3'], 'min': 0.0, 'max': 0.3, }

Add data layers to the map.

In [9]:

Map.addLayer(dataset, vis_natural, 'True color (432)')
Map.addLayer(dataset, vis_nir, 'Color infrared (543)')
Map

Map.addLayer(dataset, vis_natural, 'True color (432)') Map.addLayer(dataset, vis_nir, 'Color infrared (543)') Map

Out[9]:

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Create linked maps for visualizing images with different band combinations. For more information on common band combinations of Landsat 8/9, see https://gisgeography.com/landsat-8-bands-combinations/

Specify visualization parameters.

In [10]:

vis_params = [
    {'bands': ['SR_B4', 'SR_B3', 'SR_B2'], 'min': 0, 'max': 0.3},
    {'bands': ['SR_B5', 'SR_B4', 'SR_B3'], 'min': 0, 'max': 0.3},
    {'bands': ['SR_B7', 'SR_B6', 'SR_B4'], 'min': 0, 'max': 0.3},
    {'bands': ['SR_B6', 'SR_B5', 'SR_B2'], 'min': 0, 'max': 0.3},
]

vis_params = [ {'bands': ['SR_B4', 'SR_B3', 'SR_B2'], 'min': 0, 'max': 0.3}, {'bands': ['SR_B5', 'SR_B4', 'SR_B3'], 'min': 0, 'max': 0.3}, {'bands': ['SR_B7', 'SR_B6', 'SR_B4'], 'min': 0, 'max': 0.3}, {'bands': ['SR_B6', 'SR_B5', 'SR_B2'], 'min': 0, 'max': 0.3}, ]

Specify labels for each layers.

In [11]:

labels = [
    'Natural Color (4, 3, 2)',
    'Color Infrared (5, 4, 3)',
    'Short-Wave Infrared (7, 6 4)',
    'Agriculture (6, 5, 2)',
]

labels = [ 'Natural Color (4, 3, 2)', 'Color Infrared (5, 4, 3)', 'Short-Wave Infrared (7, 6 4)', 'Agriculture (6, 5, 2)', ]

Create linked maps.

In [12]:

geemap.linked_maps(
    rows=2,
    cols=2,
    height="400px",
    center=[40, -100],
    zoom=4,
    ee_objects=[dataset],
    vis_params=vis_params,
    labels=labels,
    label_position="topright",
)

geemap.linked_maps( rows=2, cols=2, height="400px", center=[40, -100], zoom=4, ee_objects=[dataset], vis_params=vis_params, labels=labels, label_position="topright", )

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Input In [12], in <cell line: 1>()
----> 1 geemap.linked_maps(
      2     rows=2,
      3     cols=2,
      4     height="400px",
      5     center=[40, -100],
      6     zoom=4,
      7     ee_objects=[dataset],
      8     vis_params=vis_params,
      9     labels=labels,
     10     label_position="topright",
     11 )

AttributeError: module 'geemap' has no attribute 'linked_maps'

Create a split-panel map for comparing Landsat 8 and 9 images.

Retrieve two sample images.

In [13]:

landsat8 = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_015043_20130402')
landsat9 = ee.Image('LANDSAT/LC09/C02/T1_L2/LC09_015043_20211231')

landsat8 = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_015043_20130402') landsat9 = ee.Image('LANDSAT/LC09/C02/T1_L2/LC09_015043_20211231')

Apply scaling factors.

In [14]:

landsat8 = apply_scale_factors(landsat8)
landsat9 = apply_scale_factors(landsat9)

landsat8 = apply_scale_factors(landsat8) landsat9 = apply_scale_factors(landsat9)

Generate Earth Engine layers.

In [15]:

left_layer = geemap.ee_tile_layer(landsat8, vis_natural, 'Landsat 8')
right_layer = geemap.ee_tile_layer(landsat9, vis_natural, 'Landsat 9')

left_layer = geemap.ee_tile_layer(landsat8, vis_natural, 'Landsat 8') right_layer = geemap.ee_tile_layer(landsat9, vis_natural, 'Landsat 9')

Create a split-panel map.

In [16]:

Map = geemap.Map()
Map.split_map(left_layer, right_layer)
Map

Map = geemap.Map() Map.split_map(left_layer, right_layer) Map

Out[16]:

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In [ ]:

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Last update: 2022-03-14