Pca

Uncomment the following line to install the latest version of geeagri if needed.

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# !pip install -U geeagri

# !pip install -U geeagri

Import libraries¶

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import ee
import geemap
from geeagri.analysis import PCA
import matplotlib.pyplot as plt
import seaborn as sns

plt.rcParams["font.family"] = "DeJavu Serif"
plt.rcParams["font.serif"] = "Times New Roman"

import ee import geemap from geeagri.analysis import PCA import matplotlib.pyplot as plt import seaborn as sns plt.rcParams["font.family"] = "DeJavu Serif" plt.rcParams["font.serif"] = "Times New Roman"

Initialize a Map object¶

Authenticate and initialize Earth Engine. If it doesn't work, specify a project name

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# ee.Authenticate()
# ee.Initialize(project='your-project-id')

Map = geemap.Map(basemap="SATELLITE")
Map

# ee.Authenticate() # ee.Initialize(project='your-project-id') Map = geemap.Map(basemap="SATELLITE") Map

Import region of interest¶

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bbox = [-120.398369, 37.006574, -120.208168, 37.101924]
region = ee.Geometry.BBox(*bbox)
region_style = {"color": "red", "width": 1}
Map.addLayer(region, region_style, "Region")
Map.centerObject(region, 12)

bbox = [-120.398369, 37.006574, -120.208168, 37.101924] region = ee.Geometry.BBox(*bbox) region_style = {"color": "red", "width": 1} Map.addLayer(region, region_style, "Region") Map.centerObject(region, 12)

Load Sentinel-2 data and create a median composite¶

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image = (
    ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED")
    .filterBounds(region)
    .filterDate("2024-01-01", "2025-01-01")
    .filterMetadata("CLOUDY_PIXEL_PERCENTAGE", "less_than", 10)
    .select(["B.*"])
    .median()
    .multiply(0.0001)
    .clip(region)
)

image_vis = {"bands": ["B8", "B4", "B3"], "min": 0, "max": 0.3}

Map.addLayer(image, image_vis, "Sentinel 2 FCC")

image = ( ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED") .filterBounds(region) .filterDate("2024-01-01", "2025-01-01") .filterMetadata("CLOUDY_PIXEL_PERCENTAGE", "less_than", 10) .select(["B.*"]) .median() .multiply(0.0001) .clip(region) ) image_vis = {"bands": ["B8", "B4", "B3"], "min": 0, "max": 0.3} Map.addLayer(image, image_vis, "Sentinel 2 FCC")

Principal Component Analysis¶

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pca = PCA(image, region=region, scale=100)
pcs = pca.get_principal_components()

# Define visualization parameters
pca_vis = {
    "bands": ["pc1", "pc2", "pc3"],
    "min": -2,
    "max": 2,
}

# Add PCA layer
Map.addLayer(pcs, pca_vis, "PCA RGB Composite")

pca = PCA(image, region=region, scale=100) pcs = pca.get_principal_components() # Define visualization parameters pca_vis = { "bands": ["pc1", "pc2", "pc3"], "min": -2, "max": 2, } # Add PCA layer Map.addLayer(pcs, pca_vis, "PCA RGB Composite")

Plot the explained variance by principal components¶

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# Plot the explained variance by principle components
explained_variance = pca.get_explained_variance()

sns.barplot(
    data=explained_variance,
    x="pc",
    y="variance_explained",
    hue="variance_explained",
    palette="Greens",
    edgecolor="k",
)
plt.show()

# Plot the explained variance by principle components explained_variance = pca.get_explained_variance() sns.barplot( data=explained_variance, x="pc", y="variance_explained", hue="variance_explained", palette="Greens", edgecolor="k", ) plt.show()