A comprehensive land use and crop classification workflow that utilizes Machine Learning for multi-year analysis using satellite imagery from both Sentinel-2 and Landsat.
This workflow implements a Random Forest classifier trained on a single year's ground data to perform land use and crop classification across two decades of satellite data. The model leverages seasonal median composites and multiple vegetation indices to achieve robust classification performance.
Surface reflectance data
Surface reflectance data (Landsat 5, 7, 8, 9)
NDVI, NDWI, EVI2
Surface reflectance values from visible, NIR, and SWIR regions
Single year of training data for model training collected in Morocco Applied to classification across two decades (2000-2023)
Cloud masking and atmospheric correction Seasonal compositing using 45-day windows Calculation of vegetation indices (NDVI, NDWI, EVI)
Median value calculation per season Combination of spectral bands and indices Multi-sensor data harmonization
Random Forest classifier implementation Training on single-year ground reference data Hyperparameter optimization
Application to multi-year satellite data Annual land use/crop classification Output generation (Rasters) for analysis
Google Earth Engine account
This workflow is designed for researchers and practitioners in remote sensing, agriculture, and environmental science who need to perform large-scale land use classification with limited ground truth data.
The unique aspect of this workflow is its ability to leverage a single year's training data to perform classification across multiple years, making it particularly valuable for historical analysis where ground truth data may be limited.
The research paper related to this work will be available soon