Predicting LST with Population, Rain, and Elevation using Random Forest Regression in Earth Engine

In this tutorial, you will learn how to use Google Earth Engine to predict Land Surface Temperature (LST) using population, rainfall, and elevation data. We will be using Random Forest Regression, a machine learning algorithm, to create our prediction model.

Script: https://code.earthengine.google.com/7...

First, we will access and import our data into Google Earth Engine. We will be using the Land Surface Temperature dataset from OpenLandMap, population data from WorldPop, rainfall data from OpenLandMap, and elevation data from NASADEM.

By the end of this tutorial, you will have the skills to use Google Earth Engine to predict LST using Random Forest Regression, as well as the knowledge to apply this technique to other datasets and locations.

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