Omar,
Depending on what metric you want to model on Landsat:
See the GitHub library for source code:
https://github.com/NASA-DEVELOP/METRIC
Dear FC Prinsloo,
Thank you for your response. My aim is to estimate actual evapotranspiration by METRIC model for landsat 8 by python.
To implement the METRIC model for Landsat 8 using Python, you will need to perform the following steps:
Acquire Landsat 8 data: Download Landsat 8 images for your area of interest from the USGS EarthExplorer (https://earthexplorer.usgs.gov/) or other satellite data providers. You'll need the Level-1 or Level-2 surface reflectance data.
Install required libraries: Ensure you have installed the necessary Python libraries, such as rasterio, numpy, gdal, and others.
Preprocess Landsat 8 data: Convert the digital numbers (DN) in the Landsat 8 images to top-of-atmosphere reflectance and brightness temperature values.
Calculate vegetation indices: Compute the Normalized Difference Vegetation Index (NDVI) and other indices, such as the Enhanced Vegetation Index (EVI), if needed.
Estimate land surface temperature (LST): Calculate land surface temperature using the thermal bands of Landsat 8.
Estimate surface emissivity: Calculate the surface emissivity using NDVI and LST values.
Calculate net radiation (Rn): Estimate net radiation using the Landsat 8 images and meteorological data, such as incoming solar radiation.
Estimate soil heat flux (G): Calculate soil heat flux using NDVI and net radiation values.
Obtain meteorological data: Acquire local meteorological data for air temperature, wind speed, and relative humidity from weather stations or other sources.
Estimate reference evapotranspiration (ET0): Calculate reference evapotranspiration using the FAO Penman-Monteith equation with the acquired meteorological data.
Compute ET fraction (ETf): Calculate the ET fraction using the surface energy balance equation, which includes net radiation, soil heat flux, and sensible heat flux.
Estimate actual evapotranspiration (ETa): Multiply the reference evapotranspiration (ET0) by the ET fraction (ETf) to obtain the actual evapotranspiration (ETa) values.
Export results: Save the calculated ETa values as a raster file or other desired formats.
import rasterio
import numpy as np
from osgeo import gdal
def download_landsat_data():
pass
def preprocess_landsat_data():
pass
def calculate_vegetation_indices():
pass
def estimate_lst():
pass
def estimate_surface_emissivity():
pass
def calculate_net_radiation():
pass
def estimate_soil_heat_flux():
pass
def acquire_meteorological_data():
pass
def calculate_reference_et():
pass
def compute_et_fraction():
pass
def estimate_actual_et():
pass
def export_results():
pass
if __name__ == "__main__":
download_landsat_data()
preprocess_landsat_data()
calculate_vegetation_indices()
estimate_lst()
estimate_surface_emissivity()
calculate_net_radiation()
estimate_soil_heat_flux()
acquire_meteorological_data()
calculate_reference_et()
compute_et_fraction()
estimate_actual_et()
export_results()
You'll need to fill in the functions with the appropriate calculations and methods. Some existing Python libraries, such as PyMETRIC, can help you.
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