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Application of Sentinel-1 data in mapping land-use and land cover in a complex seasonal landscape: a case study in coastal area of Vietnamese Mekong Delta

Abstract The advent of Sentinel-1 SAR data with high temporal and medium spatial resolutions along with its being unaffected by presence of cloud provided opportunities for using remote sensing in mapping LULCs with high temporal detail. The objective of this study is to explore the possibility of applying Sentinel-1 data together with OBIA and machine learning in classifying LULC in a complex agricultural landscape in the coastal area of Vietnamese Mekong Delta (VMD). Three approaches including Sentinel-1 alone, integrative Sentinel-1 and Sentinel-2 and lastly, only Sentinel-2 were deployed to examine chance of improvement of classification accuracy metrics on using Sentinel-1 data. The result showed that SAR data could capture seasonal patterns of different LULC classes. Also, supplementing Sentinel-1 can improve classification overall accuracy especially on integrating with optical data. However, Sentinel-1-alone approach performed poorly in most of LULC cases and bias of optical data can compromise classification accuracy of integrative approach.