README.md

metadata

tags:
  - random-forest
  - regression
  - land-surface-temperature
  - remote-sensing
  - landsat
library_name: sklearn

LST Random Forest Regression Model

Predicts Land Surface Temperature (°C) from Landsat 9 spectral indices.

Features

• longitude
• latitude
• ndvi
• ndbi
• ndwi
• elevation
• albedo

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))