![]() The cloud-free flood maps represented a continuous and reliable source for hydrologic studies specifically intended to improve flood inundation model simulations. It assumed the interpolation condition that water-covered boundaries change gradually with respect to VI’s space and time steps. To appropriately apply VI for this problem, an assumption of water-covered body persistent characteristics at the temporal and spatial resolutions of MODIS needed to be made. It has been used effectively in removing clouds from MODIS’s Snow-Covered Area (SCA) products ( Xia et al. VI is a three-dimensional (3D) interpolation method which employs available boundary information in space and time to construct a complete 3D surface of an object. To maintain the product’s original temporal resolution while eliminating clouds, this study proposed the Variational Interpolation (VI) algorithm ( Turk and O’Brien 1999) as an alternative data-driven method. 2014), which might fail to capture events that occur between the composite days. While this approach was appropriate in some cases, it increased the product’s latency ( Nigro et al. To the best of our knowledge, the only effort to remove clouds from flood maps came from the same group ( Policelli and Slayback 2017) when they created composite flood maps for 2, 3, and 7 days. Despite the importance of cloud removal, current studies attempting to remove clouds from flood maps are still in an early stage. During extreme events such as hurricanes or storms, clouds often block satellites’ visible band sensors from capturing floods ( Alsdorf et al. However, clouds are major limitations to flood mapping from space. In recent years, flood extent maps from satellite synthetic aperture radar (SAR), such as Envisat ASAR and ERS-2 SAR, were used to calibrate hydraulic models ( Di Baldassarre et al. Moll and Overmars (1990) were pioneers in calibrating a 1D hydraulic model by Landsat TM. Hence, coupling flood inundation models with observations from space for accurate simulations has been carried out for nearly three decades. Dynamic flood simulations by hydrologic models are beneficial for both operational applications and disaster management ( Bates and De Roo 2000 Begnudelli et al. (2014).įlood maps are an important input for the calibration of hydrologic and flood inundation models. More details of the evaluation process are referred to in Nigro et al. When validating with 53 flood events in 20, MWP captured 44% of the events from good (which means about half of the water-covered area is detected) to almost perfect (which means just about all of the water-covered area is detected Nigro et al. One of noticeable studies, the MODIS Water Product (MWP) was created using two bands, red and near-infrared (NIR), with a spatial resolution of 250 m ( Policelli and Slayback 2017). The results demonstrated the utility of the cloud-free maps, as simulated inundation maps had average POD, false alarm ratio (FAR), and Hanssen–Kuipers (HK) skill score of 0.87, 0.49, and 0.84, respectively, compared to POD, FAR, and HK of 0.70, 0.61, and 0.67 when original maps were used for calibration.įlood mapping using the rapid-response Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product is effective due to the product’s global coverage and accuracy ( Brakenridge and Anderson 2006). The second part of this study utilized the cloud-free flood maps for calibration of a hydrologic model to improve simulation of flood inundation maps. The resulting cloud-free flood maps, while maintaining the temporal resolution of the original MODIS product, showed an improvement of nearly 70% in average probability of detection (POD) (from 0.29 to 0.49) when validated with flood maps derived from Landsat-8 imagery. The VI algorithm estimated states of cloud-hindered pixels by constructing three-dimensional space–time surfaces based on assumptions of snow persistence. In this study, we implemented the Variational Interpolation (VI) algorithm to remove clouds from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) Snow-Covered Area (SCA) products. Flood mapping from satellites provides large-scale observations of flood events, but cloud obstruction in satellite optical sensors limits its practical usability.
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