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In this presentation, we present details on ways that the Joint Polar Satellite System (JPSS) Program uses its investments in environmental satellites to benefit not only the NOAA mission but other countries as well. The United States component of polar satellite, the NOAA-20 and the Suomi National Polar-orbiting Partnership (Suomi NPP) satellites continue to provide critical data and products to support user missions worldwide. New sensors are being built and integrated onto the next satellites in the JPSS Program. The JPSS Program is also starting to consider what its polar satellite program may look like after the last JPSS Satellite is launched.

Near real-time satellite-derived flood maps are invaluable to river forecasters and decision-makers for disaster monitoring and relief efforts. With support from the JPSS (Joint Polar Satellite System) Proving Ground and Risk Reduction (PGRR) Program, flood detection software has been developed using Suomi-NPP/VIIRS (Suomi National Polar-orbiting Partnership/Visible Infrared Imaging Radiometer Suite) imagery to automatically generate near real-time flood maps for National Weather Service (NWS) River Forecast Centers (RFC) in the USA. The software, which is called VIIRS NOAA GMU Flood Version 1.0 (hereafter referred to as VNG Flood V1.0), consists of a series of algorithms that include water detection, cloud shadow removal, terrain shadow removal, minor flood detection, water fraction retrieval, and floodwater determination. The software is designed for flood detection in any land region between 80°S and 80°N, and it has been running routinely with direct broadcast SNPP/VIIRS data at the Space Science and Engineering Center at the University of Wisconsin-Madison (UW/SSEC) and the Geographic Information Network of Alaska at the University of Alaska-Fairbanks (UAF/GINA) since 2014. Near real-time flood maps are distributed via the Unidata Local Data Manager (LDM), reviewed by river forecasters in AWIPS-II (the second generation of the Advanced Weather Interactive Processing System) and applied in flood operations. Initial feedback from operational forecasters on the product accuracy and performance has been largely positive. The software capability has also been extended to areas outside of the USA via a case-driven mode to detect major floods all over the world. Offline evaluation efforts include the visual inspection of over 10,000 VIIRS false-color composite images, an inter-comparison with MODIS automatic flood products and a quantitative validation using Landsat imagery. The steady performance from the 3-year routine process and the promising evaluation results indicate that VNG Flood V1.0 has a high feasibility for flood detection at the product level.

Hurricane Harvey made landfall as a Category-4 storm in the United States on 25 August 2017 in Texas, causing catastrophic flooding in the Houston metropolitan area and resulting in a total economic loss estimated to be about $125 billion. To monitor flooding in the areas affected by Harvey, we used data from sensors aboard the Suomi National Polar-Orbiting Partnership Satellite (SNPP) and the new Geostationary Operational Environmental Satellite (GOES)-16. The GOES-16 Advanced Baseline Imager (ABI) observations are available every 5 min at 1-km spatial resolution across the entire United States, allowing for the possibility of frequent cloud free views of the flooded areas; while the higher resolution 375-m imagery available twice per day from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the SNPP satellite can observe more details of the flooded regions. Combining the high spatial resolution from VIIRS with the frequent observations from ABI offers an improved capability for flood monitoring. The flood maps derived from the SNPP VIIRS and GOES-16 ABI observations were provided to the Federal Emergency Management Agency (FEMA) continuously during Hurricane Harvey. According to FEMA’s estimate on 3 September 2017, approximately 155,000 properties might have been affected by the floodwaters of Hurricane Harvey.

In this presentation, we present details on the use of data from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Joint Polar Satellite System (JPSS) to create flood products that are used by decisionmakers in response to flooding events worldwide. In the United States, floods cause more loss of life and property than any type of severe weather events. Billions of dollars are lost annually with hundreds of people losing their lives. The areal extent of flood waters are often difficult to define due to these floods occurring in remote areas and the difficulty in getting timely information in the middle of a crisis. Satellite imagery has proven to be an effect supplement to in-situ methods of defining flood extent. The VIIRS instrument has special advantages in flood detection. VIIRS has a 3000 km swath width and a 375 m spatial resolution. Its polar orbit allows for multiple passes over high latitudes. The recent launch of the JPSS-1 spacecraft to join the Suomi NPP spacecraft provides even more frequent passes. The success of the VIIRS Flood Product has led the way to the development of a flood product from the Advanced Baseline Imager (ABI) from the GOES-16 satellite. A blended JPSS and GOES flood product takes advantage of the high temporal resolution of the GOES satellites and the high spatial resolution of the JPSS satellites.

Terrain shadow is a big challenge to land products such as flood extent and snow cover from moderate-resolution optical satellite data. Because terrain shadows share similar spectral features with floodwaters, they can be easily detected as floodwaters by flood detection algorithms based on spectral features in visible, near-infrared, and short-wave infrared channels, which decreases the accuracy of flood detection substantially. However, because terrain shadows appear in mountainous areas with large surface roughness while floodwaters accumulate in low-lying areas with small surface roughness, analysis on surface roughness between terrain shadows and floodwaters can be very effective to distinguish one from the other. Root-mean-square height, internal height difference, and external height difference are used as principal quantitative surface roughness parameters in this study and calculated upon water objects that are clustered from a group of adjacent water pixels. This object-based method is applied in terrain shadow removal from SNPP/VIIRS Suomi National Polar Orbit Partnership/Visible Infrared Imager Radiometer Suite near-real-time flood maps and shows promising results according to the tests with 10,000+ VIIRS granules across global areas. Quantitative evaluation in the northwest of the USA also indicates that more than 99% terrain shadow pixels could be removed from VIIRS flood maps by this method, which significantly improves the accuracy of near-real-time flood detection from SNPP/VIIRS imagery.