Water and water-related disasters, e.g. flooding, landslide and droughts, are affecting the world and it is important for decision makers and experts to build the capacity of forecasting hydrodynamic disasters and safeguarding people’s life and property. This study focuses on building capacity of decision makers and analysts with an aim to create and support a robust framework for decision makers in integrated water resource management. Capacity building training workshops has been conducted using Coupled Routing and Excess Storage (CREST) and Ensemble Framework for Flash Flood Forecasting (EF5) distributed hydrological modeling jointly developed by University of Oklahoma (OU) and National Aeronautics and Space Administration (NASA), involving Regional Center for Mapping of Resources for Development (RCMRD), ICPAC and Kenyan Meteorological department (KMD). The OU_Applied Science Team (AST) in collaboration with RCMRD provided an EF5 hydrologic model website to KMD to visualize and forecast streamflow. Further, an advanced EF5 training was conducted in East Africa and a system to collect citizen reports to gather observations of flooding was developed.  This effort will improve awareness of and access to available services through providing user-tailored services to inform development of decision-making processes and build the capacity of SERVIR hubs and their partners to provide high quality services, creating a stronger network at the regional and international level. The study will guide users/forecasters on how to use EF5 operationally and enhances development of an impact-based flood early warning system with users, linking hydrologic forecasts with vulnerability assessment and risk analysis to mitigate the potential negative impact to the public and properties.   Key words: Capacity building, drought, flood, hydrologic model, landslide, streamflow.

This presentation goes over the last 10 years of effort to prototype various aspects fo the HyspIRI Intelligent Payload Module (IPM). This was a mission requirement and various protoytpe efforts were conducted to bring risk down. The effort goes over various hardware and software protoytpe efforts.

Abstract The Republic of Namibia, located along the arid and semiarid coast of southwest Africa, is highly dependent on reliable forecasts of surface and groundwater storage and fluxes. Since 2009, the University of Oklahoma (OU) and National Aeronautics and Space Administration (NASA) have engaged in a series of exercises with the Namibian Ministry of Agriculture, Water, and Forestry to build the capacity to improve the water information available to local decision-makers. These activities have included the calibration and implementation of NASA and OU’s jointly developed Coupled Routing and Excess Storage (CREST) hydrological model as well as the Ensemble Framework for Flash Flood Forecasting (EF5). Hydrological model output is used to produce forecasts of river stage height, discharge, and soil moisture. To enable broad access to this suite of environmental decision support information, a website, the Namibia Flood Dashboard, hosted on the infrastructure of the Open Science Data Cloud, has been developed. This system enables scientists, ministry officials, nongovernmental organizations, and other interested parties to freely access all available water information produced by the project, including comparisons of NASA satellite imagery to model forecasts of flooding or drought. The local expertise needed to generate and enhance these water information products has been grown through a series of training meetings bringing together national government officials, regional stakeholders, and local university students and faculty. Aided by online training materials, these exercises have resulted in additional capacity-building activities with CREST and EF5 beyond Namibia as well as the initial implementation of a global flood monitoring and forecasting system.

Earth Observing 1 (E0-1) satellite has an imaging spectrometer (hyperspectral) instrument called Hyperion. The satellite is able to image any spot on Earth in the nadir looking direction every 16 days. With slewing of the satellite and allowing for up to a 23 degree view angle, any spot on the Earth can be imaged approximately every 2 to 3 days. EO-1 has been used to track many natural hazards such as wildfires, volcanoes and floods. An enhanced capability that is sought is the ability to image natural hazards in a daily time series for space based imaging spectrometers. The Hyperion can not provide this capability on EO-1 with the present polar orbit. However, a constellation of cubesats each powered with Intelligent Payload Modules, and each with copies of a commercial imaging spectrometer, positioned strategically in the same orbit, can be used to provide daily coverage, cost-effectively.

In February 2017, the Earth Observing One (EO-1) satellite mission successfully completed sixteen years and three months of Earth imaging by its two unique instruments, the Hyperion and the Advanced Land Imager (ALI). Both instruments have served as prototypes for new orbital sensors. Hyperion has provided the only available global sample of the Earth's surface with: (i) passive optical mid-morning observations at moderate spatial resolution (30 m) to match the Landsat series; and (ii) spectral coverage over almost the full optical spectrum in 10 nm contiguous bands, in visible through shortwave infrared (VSWIR, 0.4-2.5 μm) wavelengths. Consequently, Hyperion is a heritage platform for future full-spectrum VSWIR orbital spectrometers, including the German mission, EnMAP (2019), and the NASA pre-Phase A (yet unscheduled) mission, the Hyperspectral InfraRed Imager (HyspIRI), defined by the 2007 Decadal Survey conducted by the US National Research Council. We provide an overview of the mission's lifetime and Hyperion's scientific and application accomplishments, including calibration & validation activities, data quality evaluations during end of mission precession changes to the orbit and overpass time, and the development of a user-friendly science quality archive.

This is a presentation on a technique for using Unmanned Aerial Systems with intelligent sensing to create science quality data. In this particular case, the team used two spectrometers with high spectral resolution and developed a technique to have the spectrometers self calibrate so that it is possible to take the measurements within the allocated 10 minute flight times. Because there is both upwelling and downwelling foreoptics on each of the two spectrometers, it becomes slow to set the calibration up for all four possibilities which relates to the integration time and then take the measurements. If light conditions change too rapidly then the measurements won't be scaled so that the spectra max is at least 85% of the instrument max but not saturated. This process was more difficult then it appeared due to the complexity of determining the integration time before light conditions changed and before the battery runs our of charge to fly the Unmanned Aerial System. This presentation goes over the results for the first few flight days and the planned future flight experiments.

Project Matsu is a collaboration between the Open Commons Consortium and NASA focused on developing open source technology for the cloud-based processing of Earth satellite imagery and for detecting fires and floods to help support natural disaster detection and relief. We describe a framework for efficient analysis and reanalysis of large amounts of data called the Matsu “Wheel” and the analytics used to process hyperspectral data produced daily by NASA’s Earth Observing-1 (EO-1) satellite. The wheel is designed to be able to support scanning queries using cloud computing applications, such as Hadoop and Accumulo. A scanning query processes all, or most, of the data in a database or data repository. In contrast, standard queries typically process a relatively small percentage of the data. The wheel is a framework in which multiple scanning queries are grouped together and processed in turn, over chunks of data from the database or repository. Over time, the framework brings all data to each group of scanning queries. With this approach, contention and the overall time to process all scanning queries can be reduced. We describe our Wheel analytics, including an anomaly detector for rare spectral signatures or anomalies in hyperspectral data and a land cover classifier that can be used for water and flood detection. The resultant products of the analytics are made accessible through an API for further distribution. The Matsu Wheel allows many shared data services to be performed together to efficiently use resources for processing hyperspectral satellite image data and other, e.g., large environmental datasets that may be analyzed for many purposes.

Project Matsu is a collaboration between the Open Commons Consortium and NASA focused on developing open source technology for the cloud-based processing of Earth satellite imagery. A particular focus is the development of applications for detecting fires and floods to help support natural disaster detection and relief. Project Matsu has developed an open source cloud-based infrastructure to process, analyze, and reanalyze large collections of hyperspectral satellite image data using OpenStack, Hadoop, MapReduce, Storm and related technologies. We describe a framework for efficient analysis of large amounts of data called the Matsu "Wheel." The Matsu Wheel is currently used to process incoming hyperspectral satellite data produced daily by NASA's Earth Observing-1 (EO-1) satellite. The framework is designed to be able to support scanning queries using cloud computing applications, such as Hadoop and Accumulo. A scanning query processes all, or most of the data, in a database or data repository. We also describe our preliminary Wheel analytics, including an anomaly detector for rare spectral signatures or thermal anomalies in hyperspectral data and a land cover classifier that can be used for water and flood detection. Each of these analytics can generate visual reports accessible via the web for the public and interested decision makers. The resultant products of the analytics are also made accessible through an Open Geospatial Compliant (OGC)-compliant Web Map Service (WMS) for further distribution. The Matsu Wheel allows many shared data services to be performed together to efficiently use resources for processing hyperspectral satellite image data and other, e.g., large environmental datasets that may be analyzed for many purposes.

Earth Observing 1 (E0-1) satellite has an imaging spectrometer (hyperspectral) instrument called Hyperion. The satellite is able to image any spot on Earth in the nadir looking direction every 16 days. With slewing of the satellite and allowing for up to a 23 degree view angle, any spot on the Earth can be imaged approximately every 2 to 3 days. EO-1 has been used to track many natural hazards such as wildfires, volcanoes and floods. An enhanced capability that is sought is the ability to image natural hazards in a daily time series for space based imaging spectrometers. The Hyperion can not provide this capability on EO-1 with the present polar orbit. However, a constellation of cubesats, each with the same imaging spectrometer, positioned strategically in the same orbit, can be used to provide daily coverage, cost-effectively.

This presentation provides information on the development of a small Unmanned Aerial System(UAS) with a low power, high performance Intelligent Payload Module (IPM) and a hyperspectral imager to enable intelligent gathering of science grade vegetation data over agricultural fields at about 150 ft. The IPM performs real time data processing over the image data and then enables the navigation system to move the UAS to locations where measurements are optimal for science. This is important because the small UAS typically has about 30 minutes of battery power and therefore over large agricultural fields, resource utilization efficiency is important. The key innovation is the shrinking of the IPM and the cross communication with the navigation software to allow the data processing to interact with desired way points while using Field Programmable Gate Arrays to enable high performance on large data volumes produced by the hyperspectral imager.

In 2007, the NASA Hyperspectral InfraRed Imager (HyspIRI) mission was recommended in Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond (Decadal Survey) to address critical science questions in multiple areas, in particular ecosystems and natural hazards. HyspIRI is comprised of two instruments, a visible to short-wavelength infrared (VSWIR) imaging spectrometer and a thermal infrared (TIR) multispectral imager, together with an Intelligent Payload Module (IPM) for onboard processing and rapid downlink of selected data. The VSWIR instrument will have 10nm contiguous bands and cover the 380–2500nm spectral range with 30m spatial resolution and a revisit of 16days. The TIR instrument will have 8 discrete bands in the 4–13μm range with 60m spatial resolution and a revisit of 5days. With these two instruments in low Earth orbit, HyspIRI will be able to address key science and applications questions in a wide array of fields, ranging from ecosystem function and diversity to human health and urbanization.

The authors on this paper are team members of the Earth Observing 1 (E0-1) mission which has flown an imaging spectrometer (hyperspectral) instrument called Hyperion for the past 15+ years. The satellite is able to image any spot on Earth in the nadir looking direction every 16 days and with slewing, of the satellite for up to a 23 degree view angle, any spot on the Earth can be imaged approximately every 2 to 3 days. EO-1 has been used to track many natural hazards such as wildfires, volcanoes and floods. An enhanced capability that has been sought is the ability to image natural hazards in a daily time series for space-based imaging spectrometers. The Hyperion cannot provide this capability on EO-1 with the present polar orbit. However, a constellation of cubesats, each with the same imaging spectrometer, positioned strategically can be used to provide daily coverage or even diurnal coverage, cost-effectively. This paper sought to design a cubesat constellation mission that would accomplish this goal and then to articulate the key tradeoffs.

This presentation lays out the data processing products that exist and are planned for the Matsu cloud for Earth Observing 1. The presentation focuses on a new feature called co-registration of Earth Observing 1 with Landsat Global Land Survey chips.