Résumé
Introduction: This paper presents a comprehensive analysis of rapeseed fields mapping using Sentinel-1 (S1) time series data. We applied a time series alignment method to enhance the accuracy of rapeseed fields detection, even in scenarios where reference label data are limited or not available.Methods: To this end, for five different study sites in France and North America, we first investigated the temporal transferability of the classifiers across several years within the same site, specifically using the Random Forest (RF) and InceptionTime algorithms. We then examined the spatiotemporal transferability of the classifiers when a classifier trained on one site and year was used to generate rapeseed fields map for another site and year. Next, we proposed an S1 time series alignment method to improve classification accuracy across sites and years by accounting for temporal shifts caused by differences in agricultural practices and climatic conditions between sites.Results and discussion:The main results demonstrated that rapeseed detection for 1 year, using training data from another year within the same site, achieved high accuracy, with F1 scores ranging from 85.5% to 97% for RF and from 88.2% to 98.3% for InceptionTime. When classifying using one-year training data from one site to classify another year in a different site, F1 scores varied between 48.8% and 97.7% for both RF and InceptionTime. Using a three year training dataset from one site to classify rapeseed fields in another site resulted in F1 scores ranging from 82.7% to 97.8% with RF and from 88.7% to 97.1% with InceptionTime. The proposed alignment method, designed to enhance classification using training and test data from different sites, improved F1 scores by up to 46.7%. These findings confirm the feasibility of mapping rapeseed with S1 images across various sites and years, highlighting its potential for both national and international agricultural monitoring initiatives.