The Strengths and Limitations in Using the Daily MODIS Open Water Likelihood Algorithm for Identifying Flood Events

Abstract

Abstract:Daily, or more frequent, maps of surface water have important applications in environmental and water resource management. In particular, surface water maps derived from remote sensing imagery play a useful role in the derivation of spatial inundation patterns over time. MODIS data provide the most realistic means to achieve this since they are daily, although they are often limited by cloud cover during flooding events, and their spatial resolutions (250-1000 m pixel) are not always suited to small river catchments. This paper tests the suitability of the MODIS sensor for identifying flood events through comparison with streamflow and rainfall measurements at a number of sites during the wet season in Northern Australia. This is done using the MODIS Open Water Likelihood (OWL) algorithm which estimates the water fraction within a pixel. On a temporal scale, cloud cover often inhibits the use of MODIS imagery at the start and lead-up to the peak of a flood event, but there are usually more cloud-free data to monitor the flood's recession. Particularly for smaller flood events, the MODIS view angle, especially when the view angle is towards the sun, has a strong influence on total estimated flood extent. Our results showed that removing pixels containing less than 6% water can eliminate most commission errors when mapping surface water. The exception to this rule was for some spectrally dark pixels occurring along the edge of the MODIS swath where the relative azimuth angle (i.e., angle between the MODIS' and sun's azimuth angle) was low. Using only MODIS OWL pixels with a low view angle, or a range distance of less than 1000 km, also improves the results and minimizes multi-temporal errors in flood identification and extent. Given these limitations, MODIS OPEN ACCESSRemote Sens. 2014, 6 11792 OWL surface water maps are sensitive to the dynamics of water movement when compared to streamflow data and does appear to be a suitable product for the identification and mapping of inundation extent at large regional/basin scales.

Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing

“…For example, errors in topographic forcing data (Jarihani et al, 2015), flood extent (Jarihani et al, 2014;Ticehurst et al, 2014) and water elevation time series (Baghdadi et al, 2011;Birkett and Beckley, 2010;Hall et al, 2012;Jarihani et al, 2013) could possibly add low to medium uncertainty (related to the error budget) within hydrodynamic model results (Table 7).…”

“…However, any error within remote sensing data is also a possible source of uncertainty in the hydrodynamic model results. For example, errors present in satellite derived flood inundation maps (i.e., MODIS OWL; (Guerschman et al, 2011;Ticehurst et al, 2014)) can produce bias in the hydrodynamic model calibration and consequently simulated flood hydrograph parameters. This error was higher for smaller flood events (< 2000 m 3 /s) that were mainly confined to the main channels due to higher uncertainty in inundation area when only extracting from multiple small channels obscured by vegetation canopy and islands.…”

MODIS-based multi-parametric platform for mapping of flood affected areas. Case study: 2006 Danube extreme flood in Romania

“…Nevertheless, MODIS is the only sensor able to provide global daily images, suitable to monitor and analyse large spatial and temporal plain floods like the one registered along the Danube in 2006. Cloud cover is always an issue when trying to map flood events using optical remote sensing data, especially during the rising stage of a flood event (Ticehurst et al, 2014). Again, the high temporal resolution of the MODIS images makes it relatively easy to find cloud free images for all the important moments of a large flood.…”

Flood Mapping Based on Multiple Endmember Spectral Mixture Analysis and Random Forest Classifier—The Case of Yuyao, China

“…Due to its synoptic view and continuous coverage of flooding events, remote sensing has been recognized as a powerful and effective tool to provide inundation maps in near real time according to many researches [1][2][3][4][5][6][7][8][9][10][11]. Generally, remotely sensed data used for flood monitoring are mainly collected from radar and optical satellites.…”

“…It is of great significance to map accurately the extent of inundated areas and the land cover types under water [4,5], which can assist in flood monitoring, relief works planning and damage assessment.…”