The Strengths and Limitations in Using the Daily MODIS Open Water Likelihood Algorithm for Identifying Flood Events
AbstractDaily, 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 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. View Full-Text
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Ticehurst, C.; Guerschman, J.P.; Chen, Y. The Strengths and Limitations in Using the Daily MODIS Open Water Likelihood Algorithm for Identifying Flood Events. Remote Sens. 2014, 6, 11791-11809.
Ticehurst C, Guerschman JP, Chen Y. The Strengths and Limitations in Using the Daily MODIS Open Water Likelihood Algorithm for Identifying Flood Events. Remote Sensing. 2014; 6(12):11791-11809.Chicago/Turabian Style
Ticehurst, Catherine; Guerschman, Juan P.; Chen, Yun. 2014. "The Strengths and Limitations in Using the Daily MODIS Open Water Likelihood Algorithm for Identifying Flood Events." Remote Sens. 6, no. 12: 11791-11809.