LST Random Forest Regression Model
Predicts Land Surface Temperature (°C) from Landsat 9 spectral indices.
Features
longitudelatitudendvindbindwielevationalbedo
Target
lst_c— Land Surface Temperature in Celsius
Performance
| Split | RMSE | MAE | R² |
|---|---|---|---|
| Train | 0.7878 | 0.5133 | 0.9891 |
| Test | 2.0728 | 1.4003 | 0.9288 |
5-fold CV R²: 0.9178 ± 0.0183
Usage
import joblib, numpy as np
rf = joblib.load("rf_lst_model.joblib")
# [longitude, latitude, ndvi, ndbi, ndwi, elevation, albedo]
sample = np.array([[121.7, 31.2, 0.33, -0.09, -0.40, 4.0, 0.18]])
print(rf.predict(sample))
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