Impact of National Development Policies on Agricultural Land Use Dynamics in Chichaoua, Morocco: A Two-Decade Remote Sensing Analysis
GEE LULC Multi-year Crop Classification 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. The classification workflow requires the following inputs / configuration: - Surface reflectance data: from Sentinel-2 (2018 - 2023) and Land
Resource information
- Format
- js, py
- Original repository
- 18669491
- DOI
- 10.5281/zenodo.18669491
- Credit basis
- Creators in Zenodo record metadata
- Access
- Open files available from the original Zenodo deposit
- Last checked
- Nov 5, 2026
- License
- cc-by-4.0
- Last record update
- Nov 5, 2026
About this resource
GEE LULC Multi-year Crop Classification 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. The classification workflow requires the following inputs / configuration: - Surface reflectance data: from Sentinel-2 (2018 - 2023) and Landsat 5, 7, 8, and 9 (2000 - 2023). - Spectral Indices: derived layers including NDVI, NDWI, and EVI2. - Training data: A single year of reference data collected in Morocco (see table below) was used to train the model for application across the full two-decade study period (2000 - 2023). This training data is also included as a publicly accessible GEE asset within the workflow. Label Class Polygons No. Area (ha) 0 Vineyard 5 3.3 1 Bare ground 129 3083.8 2 Green peas 8 8.0 3 Olive trees 72 80.0 4 Double-crop 22 19.4 5 Alfalfa 30 2.0 6 Faba beans 3 1.8 7 Cereals 6 3.4 8 Citrus trees 33 104.2 9 Apricot trees 4 8.4 10 Watermelon 35 29.6 11 Water 4 2.1 12 Rainfed crops 24 66.2 - The Pre-processing / RF parameters are summarized as follows: Category Parameter / satellite Value Number of trees - 50 Variable per split - Square root of the total by default Predictor variables S2 B2-8A, B11, B12, NDVI, EVI2, NDWI Landsat B2-B7, NDVI, EVI2, NDWI Cloud masking S2 65% threshold / cloud probability dataset. Landsat 65% threshold / Pixel-QA band. Image compositing Number of composites 6 Days per composite 45 days Statistical metric Median Period used January 1st to February 15th (DOY 1 to DOY 46), February 15th to April 1st (DOY 46 to DOY 91), April 1st to May 15th (DOY 91 to DOY 135), and May 15th to July 1st (DOY 135 to DOY 182), July 1st to September 15th (DOY 182 to DOY 258), September 15th to November 1st (DOY 258 to DOY 305). Two separate algorithms are provided to handle the different sensor characteristics: - One algorithm for processing all Landsat imagery (2000 - 2023). - A second algorithm for processing all Sentinel-2 imagery (2018 - 2023). For users seeking to understand or replicate the workflow, comprehensive documentation is available in two forms: - Step-by-step guide: a README.md file with a detailed, step-by-step explanation of the entire process. - In-Code documentation: Every step of both algorithms is thoroughly explained through inline comments within the code itself. - The research paper available at : https://www.sciencedirect.com/science/article/pii/S2352938526001679?via%3Dihub#abs0010