A Step-by-Step Methodology for Implementing Machine Learning in Remote Sensing Data Analysis

Machine learning (ML) has become indispensable for extracting information from large-scale, high-dimensional remote sensing data. Despite its growing adoption, practitioners new to the field often struggle to bridge the gap between theoretical understanding and practical model implementation. This article presents a systematic, end-to-end methodology that guides beginners through the complete ML pipeline for remote sensing data analysis, from data preparation and feature engineering to model training, evaluation, and hyperparameter optimization. Three widely used regression algorithms, namely Linear Regression, Random Forest, and Gradient Boosting, are implemented and compared, with performance assessed using standard error metrics and diagnostic tools, including residual plots, Q-Q plots, and actual v/s predicted charts. Hyperparameter tuning is conducted using GridSearchCV and RandomizedSearchCV. Although the methodology is demonstrated through wheat yield prediction using time-series remote sensing and agrometeorological data, the design is problem-agnostic and transferable to other regression tasks in remote sensing. Implemented entirely in Python within Jupyter Notebook, the workflow is reproducible and serves as a practical, step-by-step guide for students and early-career researchers seeking to design, train, and evaluate robust ML models without prior AI/ML expertise.