Surface Water Mapping and Flood Monitoring in the Mekong Delta Using Sentinel-1 SAR Time Series and Otsu Threshold

Abstract

The annual flood and the alteration in hydrological regimes are the most vital concerns in the Vietnamese Mekong Delta (VMD). Although synthetic aperture radar (SAR) Sentinel-1 imagery is widely used for water management, only a few studies have used Sentinel-1 data for mapping surface water and monitoring flood events in the VMD. This study developed an algorithm to implement (i) automatic Otsu threshold on a series of Sentinel-1 images to extract surface water and (ii) time series analyses on the derived surface water maps to detect flood water extent in near-real-time (NRT). Specifically, only cross-polarized VH was selected after an assessment of different Sentinel-1 polarizations. The dynamic Otsu thresholding algorithm was applied to identify an optimal threshold for each pre-processed Sentinel-1 VH image to separate water from non-water pixels for producing a time series of surface water maps. The derived Sentinel-1 surface water maps were visually compared with the Sentinel-2 Full Resolution Browse (FRB) and statistically examined with the Sentinel-2 Multispectral Instrument (MSI) surface water maps, which were generated by applying the Otsu threshold on the normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) images. The visual comparison showed a strong correspondence between the Sentinel-1 surface water maps and Sentinel-2 FRB images in three periods, including rice’s sowing season, flood period, and rice’s maturation stage. A good statistical agreement suggested that the performance of the dynamic Otsu thresholding algorithm on Sentinel-1 image time series to map surface water is effective in river areas (R2 = 0.97 and RMSE = 1.18%), while it is somewhat lower in paddy field areas (R2 = 0.88 and RMSE = 3.88%). Afterward, a flood mapping algorithm in NRT was developed by applying the change-detection-based time series analyses on the derived Sentinel-1 surface water maps. Every single pixel at the time is respectively referred to its state in the water/non-water and flooded/non-flooded maps at the previous time to be classified into a flooded or non-flooded pixel. The flood mapping algorithm enables updates at each time step to generate temporal flood maps in NRT for monitoring flood water extent in large-scale areas. This study provides a tool to rapidly generate surface water and flood maps to support water management and risk reduction in the VMD. The future improvement of the current algorithm is discussed.

Flood Mapping and Impact Assessment in Agricultural Land in Hoa Vang, Da Nang Using Remote Sensing and Google Earth Engine

“…Ngưỡng Otsu được ước lượng như sau: 𝜎 2 (𝑡) = 𝑃 𝑤 (𝑡) × 𝜎 𝑤 2 (𝑡) + 𝑃 𝑛𝑤 (𝑡) × 𝜎 𝑛𝑤 2 (𝑡) (1) Ở đây, 𝜎 là tổng trọng số của phương sai của các lớp nước và không có nước. 𝑃𝑤, 𝜎𝑤, 𝑃𝑛𝑤 và 𝜎𝑛𝑤 là xác suất và phương sai của các lớp nước (𝑤) và không có nước (𝑛𝑤) được tách ra bằng một ngưỡng 𝑡 tương ứng [30].…”

Efficient agricultural monitoring: a methodology for assessing individual farmer adherence to rice-planting schedule for tertiary irrigation system under the Muda Irrigation Scheme using Earth observation datasets

“…However, using SAR to extract field-level agricultural information can be challenging because of its sensitivity to soil, vegetation, and random noise (Vreugdenhil et al, 2018). The vertical-horizontal (VH) polarization of SAR is suitable for monitoring rice practices because of its reduced susceptibility to the double-bounce phenomenon during rice sowing, in contrast to verticalvertical (VV) polarization (Tran et al, 2022). Two recent studies were conducted on rice tillage and planting period determination based on VH-SAR (Fatchurrachman et al, 2023;Hashemi et al, 2022).…”

Spatiotemporal Pattern, Evolutionary Trend, and Driving Forces Analysis of Ecological Quality in the Irtysh River Basin (2000–2020)

“…When calculating the RSEI at different time intervals, the use of dynamic thresholds is essential to avoid overestimation or underestimation of water bodies. The Otsu thresholding algorithm, based on maximizing the inter-class variance and minimizing the intra-class variance in the image histogram, has been shown to be the most efficient method for obtaining thresholds in surface water extraction [36][37][38].…”