Orieschnig C. A. auteur aut IRD Belaud G. auteur aut IRD Venot Jean-Philippe auteur aut IRD Massuel Sylvain auteur aut IRD Ogilvie Andrew auteur aut IRD text journalArticle eng text/pdf reformatted digital access The increased open-access availability of radar and optical satellite imagery has engendered numerous land use and land cover (LULC) analyses combining these data sources. In parallel, cloud computing platforms have enabled a wider community to perform LULC classifications over long periods and large areas. However, an assessment of how the performance of classifiers available on these cloud platforms can be optimized for the use of multi-imagery data has been lacking for multi-temporal LULC approaches. This study provides such an assessment for the supervised classifiers available on the open-access Google Earth Engine platform: Naive Bayes (NB), Classification and Regression Trees (CART), Random Forest (RF), Gradient Tree Boosting (GTB), and Support Vector Machines (SVM). A multi-temporal LULC analysis using Sentinel-1 and 2 is implemented for a study area in the Mekong Delta. Classifier performance is compared for different combinations of input imagery, band sets, and training datasets. The results show that GTB and RF yield the highest overall accuracies, at 94% and 93%. Combining optical and radar imagery boosts classification accuracy for CART, RF, GTB, and SVM by 10-15 percentage points. Furthermore, it reduces the impact of limited training dataset quality for RF, GTB, and SVM. specialized CART google earth engine gradient tree boosting LULC Random Forest sentinel-1 and-2 SVM CAMBODGE 126 082 076 54 1 398-416 2021 https://www.documentation.ird.fr/hor/fdi:010082634 10.1080/22797254.2021.1948356 [F B010082634] https://www.documentation.ird.fr/hor/fdi:010082634 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2021-09/010082634.pdf IRD - Base Horizon / Pleins textes 2021-09-17 2025-02-24 fdi:010082634 fre