HLS python code

Overview

This script processes HLS (HLS Sentinel-2-like) satellite data stored as individual TIF files representing different spectral bands. It stacks these bands into a single array and visualizes them as an RGB image using EarthPy.
Detailed Steps

    Import Libraries:
        os: For interacting with the operating system.
        glob: For file path pattern matching.
        matplotlib.pyplot: For plotting data.
        earthpy: Core EarthPy library.
        earthpy.spatial: EarthPy's spatial processing functions.
        earthpy.plot: EarthPy's plotting functions.

    Define HLS Data Directory:
        hls_data_path = r"C:\Users\cmcev\CSDA\HLS_data_dir": Specifies the directory where HLS data (TIF files) are stored.

    Generate Paths for Each Band:
        Uses glob to generate lists (band2_paths, band3_paths, band4_paths) of file paths for each band (B02.tif, B03.tif, B04.tif) within the hls_data_path.

    Stack HLS Bands:
        Concatenates all band paths (hls_paths) into a single list and sorts them.
        Uses earthpy.spatial.stack (es.stack) to stack these bands into a single NumPy array (hls_stack).
        Retrieves metadata (hls_meta_data) associated with the stacked array.

    Plot HLS Data as RGB Image:
        Uses earthpy.plot_rgb (ep.plot_rgb) to plot the stacked HLS data (hls_stack) as an RGB image.
        Specifies the order of bands for RGB composition (rgb=[2, 1, 0] corresponds to B04 as Red, B03 as Green, and B02 as Blue).
        Applies a linear stretch to enhance contrast (stretch="linear").

    Display the Plot:
        Uses plt.show() to display the RGB image plot.

Dependencies

    Python Libraries: os, glob, matplotlib, earthpy
    External Tools: Ensure HLS TIF files are correctly located and named within the specified directory.

Notes

    This script is useful for visualizing HLS satellite data, typically derived from Sentinel-2 imagery, providing insights into surface reflectance across different spectral bands.
    Ensure that the HLS data directory (hls_data_path) is correctly set to match the actual directory where your HLS TIF files are stored.

------------------------------------------------------------------------------------------------------------------------------------

import os
from glob import glob
import matplotlib.pyplot as plt
import earthpy as et
import earthpy.spatial as es
import earthpy.plot as ep

# Define the directory where your HLS data is stored
hls_data_path = r"C:\Users\cmcev\CSDA\HLS_data_dir"

# Generate a list of TIF files for each band
band2_paths = glob(os.path.join(hls_data_path, "HLS.S30.T14TQL.2024144T170851.v2.0.B02.tif"))
band3_paths = glob(os.path.join(hls_data_path, "HLS.S30.T14TQL.2024144T170851.v2.0.B03.tif"))
band4_paths = glob(os.path.join(hls_data_path, "HLS.S30.T14TQL.2024144T170851.v2.0.B04.tif"))

# Stack the HLS bands
# This creates a numpy array with each "layer" representing a single band
# You can use the nodata= parameter to mask nodata values
hls_paths = band2_paths + band3_paths + band4_paths
hls_paths.sort()
hls_stack, hls_meta_data = es.stack(hls_paths, nodata=-9999)

# Plot the HLS data as an RGB image with a linear stretch
ep.plot_rgb(hls_stack, rgb=[2, 1, 0], stretch="linear")
plt.show()