Using MODIS/Terra and Landsat imageries to improve surface water quantification in Sylhet, Bangladesh

Abstract

Bangladesh has experienced multiple freshwater issues including salinization from monsoonal floods and groundwater over-pumping that induces severe land subsidence. Therefore, using satellite observations to virtually build a monitoring network becomes an efficient and innovative means. We focus on the Sylhet Mymensingh haor area that has the highest annual precipitation and the largest inundation area in northeastern Bangladesh. The modified normalized difference water index is first used to extract water area from MODIS and Landsat-5/-7/-8 optical imageries. A weekly flood chance model is then created from a sequence of images to recover water extent from the cloud-covered images. Using MODIS images for water identification achieves an overall accuracy of 84% in rainy season and 41% in dry season as validated with Sentinel-1A radar images. This model can be further used to refine the Shuttle Radar Topography Mission digital elevation model (DEM). As compared with ICESat laser altimetry, the root-mean-square of the height difference is improved from 1.65 m to 1.16 m after DEM modification. By combining the recovered water area and the refined DEM, surface water volume (WV) is quantified. A comparison with the Gravity Recovery And Climate Experiment (GRACE) gravimetry retrieved equivalent water heights (EWHs) in 2002-2015 is conducted, where the correlation coefficient and root-mean-square of the EWH difference are 91.7% and 0.09 m, respectively.

Remote Sensing-Based Research for Monitoring Progress towards SDG 15 in Bangladesh: A Review

“…Besides Dhaka city, other areas of Bangladesh received attention in terms of quantifying water bodies using remotely sensed data. For example, the study by Tseng et al (2019) focused on surface water area (WA) quantification of the Sylhet-Mymensingh Haor Area that receives the highest annual precipitation in Bangladesh [47]. Their primary focus was to develop a surface WA quantification method (i.e., flood chance modeling) by combining multiple satellite data including MODIS, Landsat optical satellite image, Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), Gravity Recovery and Climate Experiment (GRACE) observations, Sentinel-1A images, and Ice, Cloud, and land Elevation Satellite (ICESat) elevation products.…”

Introduction to the special issue on Tibet: Contemporary geodetic-geophysical observations and interpretations

“…(6) Flood detection using satellite imageries Tseng et al (2019) built a satellite-based, efficient surface water monitoring system in Syhlhet, an important economic zone of Bangladesh that has long suffered from groundwater loss and land subsidence. A potential link to the study of Tseng et al (2019) with that of Hwang et al (2019) is the effect of the declining water storage around the Yarlung Tsangpo River, which is the upstream of the Brahmaputra River that flows by Syhlhet and into the Bay of Bengal.…”

“…Figure 3 shows the coverage of the SAR images over the Himalayas from Sentinel-1A, which is in a 12-day repeat observing cycle. The SAR-related works in this special issue are Du et al (2019), Liu et al (2019), and Tseng et al (2019). With more publicly available SAR images from satellite missions like ESA's Sentinel-1A/-1B, and JAXA's ALOS and ALOS-2, publicly available SAR data processing software systems such as GMTSAR and others, SAR estimates of glacier melt rate, landslide and surface deformation in Tibet have been demonstrated.…”