Surface Water Monitoring within Cambodia and the Vietnamese Mekong Delta over a Year, with Sentinel-1 SAR Observations

Binh Pham-Duc,Filipe Aires,Catherine Prigent

doi:10.3390/w9060366

Abstract

This study presents a methodology to detect and monitor surface water with Sentinel-1 Synthetic Aperture Radar (SAR) data within Cambodia and the Vietnamese Mekong Delta. It is based on a neural network classification trained on Landsat-8 optical data. Sensitivity tests are carried out to optimize the performance of the classification and assess the retrieval accuracy. Predicted SAR surface water maps are compared to reference Landsat-8 surface water maps, showing a true positive water detection of ∼90% at 30 m spatial resolution. Predicted SAR surface water maps are also compared to floodability maps derived from high spatial resolution topography data. Results show high consistency between the two independent maps with 98% of SAR-derived surface water located in areas with a high probability of inundation. Finally, all available Sentinel-1 SAR observations over the Mekong Delta in 2015 are processed and the derived surface water maps are compared to corresponding MODIS/Terra-derived surface water maps at 500 m spatial resolution. Temporal correlation between these two products is very high (99%) with very close water surface extents during the dry season when cloud contamination is low. This study highlights the applicability of the Sentinel-1 SAR data for surface water monitoring, especially in a tropical region where cloud cover can be very high during the rainy seasons.

Highlights

Studying the spatial and temporal distribution of surface water resources is critical, especially in highly populated areas and in regions under climate change pressure
An inter-comparison between Sentinel-1 estimate and other existing estimates is the only way to evaluate the new wetland product based on Synthetic Aperture Radar (SAR) Sentinel-1 data
This study presents a methodology to monitor and quantify surface water under all weather conditions within Cambodia and the Mekong Delta in Vietnam, using high quality Sentinel-1 SAR

Summary

Introduction

Studying the spatial and temporal distribution of surface water resources is critical, especially in highly populated areas and in regions under climate change pressure. With an increased number of Earth-observation satellites providing a large diversity of remote sensing data, there is the potential to monitor the surface water at regional to global scale. Several methods have already been proposed to detect and monitor surface water with visible and. [1] used positive values of the Normalized Difference Water Index (NDWI) to classify water bodies. [2] applied a threshold on NIR reflectances of the NOAA/AVHRR satellite to delineate lakes. [3] detected surface water by identifying the positive values of the Modification of Normalized Difference Water Index (MNDWI). [4] combined NIR data and the Normalized Difference Vegetation Index (NDVI) to detect surface water bodies.

Methods

Results

Conclusion