Improving Hydrologic Modeling Using Cloud-Free MODIS Flood Maps

Abstract

Flood mapping from satellites provides large-scale observations of flood events, but cloud obstruction in satellite optical sensors limits its practical usability. 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. The VI algorithm estimated states of cloud-hindered pixels by constructing three-dimensional space–time surfaces based on assumptions of snow persistence. 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 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 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.

Assessment of the Usability of SAR and Optical Satellite Data for Monitoring Spatio-Temporal Changes in Surface Water: Bodrog River Case Study

“…Surface water bodies are no exception, and therefore can be used to map inundated areas with sufficient temporal and spatial resolution. Analysing the spatial extent and temporal pattern of flood inundation from remotely sensed imagery is of critical importance to flood mitigation and management [13,14]. The advance of remote sensing methods brought a new quality to the field, allowing for a faster inventory of large, remote areas and resulting in voluminous, detailed data sets [15,16].…”

A hydrological simulation dataset of the Upper Colorado River Basin from 1983 to 2019

“…Remote sensing products have become a valuable source of earth systems data and are often used on their own or to complement in-situ point observations. For example, a family of the Moderate Resolution Imaging Spectroradiometer (MODIS) products has been used globally to study various water cycle components and extremes such as snow 12,13 , flood 14,15 , and drought 16,17 . Remote sensing products' temporal and spatial resolutions may be limited for some applications.…”

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

“…The application of GeoAI in hydrological spatial prediction is diverse; it can be used, for example, in the risk mapping of hydrological extremes such as flood and drought [88][89][90]. In particular, GeoAI is widely applied in flood mapping, using satellite imagery, UAVs, high resolution LiDAR topographic data, and automatic water level sensors [91][92][93].…”