Tearing Down Technical Barriers

New surface water extent and surface disturbance products from a NASA effort are providing needed resources to federal agencies.
The OPERA project uses data from satellite radar and optical instruments to produce surface water extent and surface disturbance data products. Surface displacement products soon will be added. Credit: NASA/JPL. Credits for inset images: Firth River Yukon and Water Data, USGS/John Jones; Eruption of the Kilauea Volcano and Volcano Data, ASI/NASA/JPL-Caltech; Fire-fighting helicopter with water bucket and fire data: Hansen, UMD/Google/ USGS/NASA.

Getting accurate, trustworthy satellite data about a wildfire, a hurricane making landfall, or life-threatening severe weather is a notable achievement. So is getting actionable, decision-ready science data products about that fire, hurricane, or storm into the hands of staff at federal agencies and to first responders in a timely manner. Unfortunately, getting from one to the other is often a long process that typically involves a high degree of technological savvy and data-processing prowess.

The Observational Products for End-Users from Remote Sensing Analysis (OPERA) project at NASA’s Jet Propulsion Laboratory (JPL) in Southern California seeks to change that with free and timely satellite data products for users ranging from federal agencies to the public.

“Quite often satellite missions are driven by science, applications, or technology demonstrations. In OPERA, we focus on fulfilling the operational needs identified by federal agencies who rely on our work,” said OPERA Project Manager Dr. David Bekaert. “We leverage cloud computing to turn massive amounts of satellite observations into analysis-ready products relevant to our federal stakeholders. Shortening the path from satellite observations to stakeholder decisions is a key driver behind the overall implementation and execution of OPERA.”

The OPERA project is managed by JPL, with partners from NASA’s Goddard Space Flight Center, the U.S. Geological Survey (USGS), the University of Maryland College Park, the University of Alaska Fairbanks, and Southern Methodist University. Every two years, the multi-agency Satellite Needs Working Group (SNWG) conducts a survey to identify gaps in the current suite of NASA datasets to meet U.S. federal agency needs. Following the 2018 survey, better products for detecting surface water extent, surface disturbance, and surface displacement were among the top 10 interagency needs. OPERA began development of these products in 2021.

OPERA’s Release 1 Products

As of June 2023, the OPERA project has released a near-global Dynamic Surface Water Extent (DSWx) product and a near-global Surface Disturbance (DIST) product. Both products are derived from the Harmonized Landsat Sentinel-2 (HLS) project, which uses data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 and Landsat 9 satellites and the Multispectral Instrument (MSI) aboard the European Space Agency’s (ESA) Sentinel-2A and -2B satellites to generate harmonized surface reflectance data products with near-global coverage.

California’s San Joaquin River experienced dramatic swelling during a series of atmospheric rivers in early 2023. The storms caused widespread flooding over farmlands in the Central Valley. River discharge went up by 380% to 14,000 cubic feet per second. OPERA Surface Water Extent data were consistent with in situ discharge gauge data and satellite images on the right. Credit: OPERA Project Team/Planet Labs, Inc.

The DSWx-HLS (Version 1) product (doi:10.5067/OPDSW-PL3V1) is a provisional Level-3 dataset offering near-global high-resolution surface water extent for lakes, rivers, reservoirs, and streams with a data record starting in April 2023. DSWx-HLS is primarily generated using HLS version 2 surface reflectance data from Landsat 8’s OLI and Sentinel-2’s MSI instruments. The DSWx-HLS data are distributed over Universal Transverse Mercator (UTM) projected map coordinates. Each UTM tile covers an area of 109.8 km squared at 30-meter pixel spacing. The product is distributed in Cloud Optimized GeoTIFF (COG) format as a set of 10 files, including water classification, classification confidence, land cover classification, terrain shadow, cloud/cloud-shadow classification, digital elevation model (DEM), and classification diagnostics. Also included in the DSWx-HLS suite is a DSWx-HLS CalVal Database (Version 1) offering a calibration and validation (CalVal) database for the DSWx-HLS provisional product. The CalVal database contains surface classification results that enable independent verification of OPERA data product accuracies. The DSWx-HLS product is currently available from NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC). Subsequent releases of surface water extent products will include radar observations from the Sentinel-1, Surface Water and Ocean Topography (SWOT), and NASA/Indian Space Research Organisation Synthetic Aperture Radar (NISAR) satellite missions.

The Mosquito Fire, caused by trees in contact with high-voltage power lines, started on September 6, 2022, and burned more than 120 square miles in the Tahoe and Eldorado National Forests. It was the largest California wildfire of 2022. The fire was active for more than 50 days and forced more than 10,000 people to evacuate, according to the Sacramento Business Journal. OPERA Surface Disturbance data were used to map the extent and degree of vegetation loss due to the fire. Credit: OPERA Project Team/Google.

OPERA’s DIST product suite, which is available from NASA’s Land Processes DAAC (LP DAAC), is composed of two products according to their temporal scope: a Disturbance Alert (DIST-ALERT-HLS; doi:10.5067/SNWG/OPERA_L3_DIST-ALERT-HLS_PROVISIONAL_V0.000) product released with the cadence of HLS imagery and an Annual Disturbance (DIST-ANN-HLS) product, which summarizes confirmed changes in the DIST-ALERT-HLS product from the previous year. The DIST-ALERT-HLS product is generated from the harmonized surface reflectance data from the OLI aboard Landsat 8 and Landsat 9 and the MSI aboard the Sentinel-2A and -2B satellites.

The combined system provides observations over land masses with a near-global coverage every few days. Each tile covers 109.8 km squared at 30-m pixel spacing. The DIST-ALERT-HLS product is provided in COG format, and each of its 19 layers is distributed as a separate file. These layers include vegetation disturbance status, current vegetation cover indicator, current vegetation anomaly value, historical vegetation cover indicator, maximum vegetation anomaly value, vegetation disturbance confidence layer, date of initial vegetation disturbance, number of detected vegetation loss anomalies, and vegetation disturbance duration.

According to OPERA Project Scientist Dr. Steven Chan, the release of the project’s Surface Water Extent and Surface Disturbance products is significant as they help address observational gaps among the existing products used by U.S. federal agencies.

“The OPERA’s DSWx-HLS product provides a near-global coverage and leverages observations from two satellite families, resulting in more frequent observations," Chan said. "This enables OPERA’s DSWx-HLS to monitor fast-changing surface conditions due to, for example, fast-moving storm systems.”

Providing stakeholders with more frequent observations to aid decision-making is what OPERA is all about.

“The gist of the OPERA project is to help our federal agency partners use the information as quickly as possible without worrying about a lot of the technical details,” said Chan. “We’re trying to tear down the [technical] barriers by providing a set of user-friendly data formats, so they can get the geophysical parameter that they’re most interested in and use it right away.”

“OPERA’s development and distribution of higher-level products goes beyond user-friendliness, it’s a hallmark of what NASA is moving toward in its on-going efforts to meet the needs of its federal agency partners,” said Bekaert. “[It’s] game-changing that NASA is going to multi-sensor observations and working to produce higher-level data products so federal agencies and others can spend more time integrating the data into decision-making and analysis, and less on data processing. With OPERA, we’re really getting to the next step.”

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