Data_Development of a processing chain for identifying rice-growing areas using Sentinel-2 satellite imagery

This dataset and processing workflow were developed as part of the study “Development of a Processing Chain for Identifying Rice-Growing Areas Using Sentinel-2 Satellite Imagery.”

The objective of this work is to identify and map rice-growing areas in Manombo (Madagascar) using multispectral Sentinel-2 imagery and a combination of supervised classification and temporal analysis.

The dataset includes pre-processed Sentinel-2 images (surface reflectance), training samples, classification results, and the Google Earth Engine (GEE) script used to generate the results.

The workflow involves radiometric calibration, cloud masking, composite generation, spectral index computation, and classification using the Random Forest algorithm implemented in GEE.

These data support the reproducibility of the results presented in the associated article and can be used as a reference for rice mapping in tropical regions with similar agricultural practices.