Flood Monitoring Using Satellite-Based RGB Composite Imagery and Refractive Index Retrieval in Visible and Near-Infrared Bands

Abstract

Abstract:Satellite remote sensing provides significant information for the monitoring of natural disasters. Recently, on a global scale, floods have been increasing both in frequency and in magnitude. In order to map the inundation area, flooding events are investigated using unique RGB composite imagery based on the MODIS surface reflectance (MOD09GA) data obtained from the Terra satellite, which is used to visualize and analyze these events. This study proposes using an RGB combination of MODIS band 6 (1.64 µm), band 5 (1.24 µm), and band 2 (0.86 µm) data from the visible and the near-infrared spectral ranges to map flood events. The flooding events that were investigated in this study occurred on 25 October 2015 along the Pampanga River in the Philippines, and on 28 July 2016 along the Poyang and Dongting Lakes in China. In the case of the Pampanga River, the inundated areas were estimated with surface reflectance (R) thresholds of 0.0 ≤ R 6 ≤ 0.102, 0.0 ≤ R 5 ≤ 0.138, and 0.03 ≤ R 2 ≤ 0.148 for MODIS bands 6, 5, and 2, respectively, which were determined using Otsu's method. The total inundated area was estimated to be 487.75 km 2 . This estimate was indirectly compared with the results obtained from SENTINEL-1A Synthetic Aperture Radar (SAR) data. The total inundated area on 26 October 2015 for the case of the Pampanga River was estimated to be 486.37 km 2 using histogram analysis based on Otsu's method. For the flooding case in China, the total estimated inundated area using MODIS RGB imagery on 28 July 2016 and SAR on 3 August 2016 was 1148.25 km 2 and 1110.096 km 2 , respectively. In addition, RGB imagery results using MODIS 6-5-2 bands were supported by the refractive index retrieval along the inundation area. A threshold of 1.6 for the real part of the complex refractive index allows for the discrimination between the flooded and non-flooded areas using the Hong and ASH approximations. This study shows that the RGB composite techniques using advanced sensors with more bands and higher spatio-temporal resolutions, and supported by the refractive index retrieval method, are useful for estimating flood events.

Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data

“…This method has been used in multiple approaches in the literature aiming at separating pixels into inundated and non-inundated classes [10,[22][23][24]42]. In this experiment, MCET is replaced by the Otsu method, while the rest of the steps of the unsupervised approach are kept the same.…”

“…Various algorithms have been used in the literature for estimating thresholds partitioning water and non-water pixels inside an image subset based on their class distribution. Most prevalent algorithms include Otsu's algorithm [21] used in [10,13,[22][23][24] and Kittler and Illingworth's algorithm [25], which was used in [26][27][28]. Automatic thresholding approaches are distinguished into global and local thresholding approaches:…”

“…• Global approaches [23] estimate thresholds based on the histogram analysis of the complete image. The capability of a global thresholding algorithm to detect an optimal histogram threshold may be unsatisfying when the class proportions within the image are imbalanced [29].…”

A Comparative Study of Water Indexes and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery

“…Reference source not found.]. Although these inland surface water bodies only hold about 0.013% of Earth's total water, i.e., about 178,000 km 3 [2][3][4], they are important compartments in the global terrestrial water cycle and mapping inundation areas of inland surface water bodies is of great significance for flood prediction and prevention [5][6][7][8], flood risk and damage assessments [9][10][11][12][13][14][15][16][17], estimation of water storage in rivers, lakes and reservoirs [18][19][20], calculation of evaporation from wetlands and lakes/reservoirs [21], retrieval of lake water level and river stage [22][23][24][25], reservoir operation and management [26], and assessment of ecological functions and health in wetlands and marshes [27,28]. In addition to the above mentioned practical applications, surveying inland surface water body can provide critical measurements/observations for improving our understanding of the water cycle and inundation dynamics at multiple spatial and temporal scales [29][30][31].…”

Variability of Microwave Scattering in a Stochastic Ensemble of Measured Rain Drops

“…The retrieval of precipitation with radars and radiometers is important for a variety of environmental applications and human activities [1]. They provide accurate precipitation estimates that are crucial for monitoring extreme climate events, such as droughts [2][3][4], floods [5][6][7], and hailstorms [8,9]. Due to its global coverage and direct measurement, radars have become an essential tool to estimate precipitation, especially in complex terrains [10][11][12] and in sparsely populated areas affected by poor rain gauge coverage [13][14][15].…”