Investigating Radar Time Series for Hydrological Characterisation in the Lower Mekong Basin

Abstract

Radar remote sensing is beneficial for retrieval of hydrological information such as soil moisture and flood extents due to the strong influence of water on the radar signal. The proper monitoring and analysis of such temporally dynamic phenomena requires dense time series data. Radar time series data is also useful for mitigating uncertainties in individual images, e.g. for the mapping of permanent water bodies. This chapter reviews capabilities, potentials and challenges of spaceborne radar time series data for the mapping of permanent water bodies, the monitoring of floods, and the retrieval of soil moisture content. The focus is put on the Lower Mekong Basin (LMB) in Southeast Asia. Two thirds of the LMB's population of 60 million people live directly from agriculture and fisheries. The Mekong River's resources are under pressure among others from an increasing population, intensified agriculture, and the expansion of hydropower. A thorough understanding of water resources in the LMB is therefore crucial to the sustainable development in the region. The chapter provides an outline of radar remote sensing for retrieval of hydrological information as well as an overview of the relevant operational capabilities of radar missions. A map of permanent water bodies of the entire Lower Mekong Basin derived from a time series of ENVISAT Advanced Synthetic Aperture Radar (ASAR) data is presented. Potentials and challenges of flood monitoring with SAR are illustrated with ASAR imagery showing the evolution of the floods that occurred around Tonle Sap Lake in Cambodia in 2011. Finally, the spatial and temporal dynamics of soil moisture across the LMB are analysed with the use of 14 years of scatterometer time series data acquired by the ERS-1, ERS-2, Metop-A and Metop-B satellites. The average seasonal soil moisture cycle was computed at the subcatchment level. An anomaly analysis of the temporal soil moisture dynamics revealed large inter-annual variability across the Lower Mekong Basin.

Flooding Extent Mapping for Synthetic Aperture Radar Time Series Using River Gauge Observations

“…So far, many methods have been developed, of which the most popular choose the radar backscatter threshold below which a SAR data pixel is labeled as flooded either in an automated way [1], [2] or by expert judgment [3]. These simple robust methods have limited applicability in difficult case studies, such as with complex topography or abundant vegetation, and therefore were modified or adapted in more complex methods [4], [5]. Recent examples of complex flood mapping methods used ancillary data and region growing [6], local correction algorithms of radar layover, and emergent vegetation [7], elevation-based filtering and processing in local tiles [8], or classification, including multistep classification, often followed by data transformation [9]- [12].…”

Sentinel-1 Satellite Data as a Tool for Monitoring Inundation Areas near Urban Areas in the Mexican Tropical Wet

“…In this method, water is separated from land in the intensity images, but the accuracy of the results is based on the ability to differentiate the land pixels vs. water in the intensity domain. There are several modifications to the thresholding method: [29,32,35,[40][41][42][43][44]. There are also different ways to improve the thresholding method.…”