Global intercomparison of hyper-resolution ECOSTRESS coastal sea surface temperature measurements from the space station with VIIRS-N20

The ECOSTRESS multi-channel thermal radiometer on the Space Station has an unprecedented spatial resolution of 70 m and a return time of hours to 5 days. It resolves details of oceanographic features not detectable in imagery from MODIS or VIIRS, and has open-ocean coverage, unlike Landsat. We calibrated two years of ECOSTRESS sea surface temperature observations with L2 data from VIIRS-N20 (2019–2020) worldwide but especially focused on important upwelling systems currently undergoing climate change forcing. Unlike operational SST products from VIIRS-N20, the ECOSTRESS surface temperature algorithm does not use a regression approach to determine temperature, but solves a set of simultaneous equations based on first principles for both surface temperature and emissivity. We compared ECOSTRESS ocean temperatures to well-calibrated clear sky satellite measurements from VIIRS-N20. Data comparisons were constrained to those within 90 min of one another using co-located clear sky VIIRS and ECOSTRESS pixels. ECOSTRESS ocean temperatures have a consistent 1.01 °C negative bias relative to VIIRS-N20, although deviation in brightness temperatures within the 10.49 and 12.01 µm bands were much smaller. As an alternative, we compared the performance of NOAA, NASA, and U.S. Navy operational split-window SST regression algorithms taking into consideration the statistical limitations imposed by intrinsic SST spatial autocorrelation and applying corrections on brightness temperatures. We conclude that standard bias-correction methods using already validated and well-known algorithms can be applied to ECOSTRESS SST data, yielding highly accurate products of ultra-high spatial resolution for studies of biological and physical oceanography in a time when these are needed to properly evaluate regional and even local impacts of climate change.National Aeronautics and Space Administration | Ref. 80NSSC20K007

Machine Learning Approach to Retrieving Physical Variables from Remotely Sensed Data

Scientists from all over the world make use of remotely sensed data from hundreds of satellites to better understand the Earth. However, physical measurements from an instrument is sometimes missing either because the instrument hasn\u27t been launched yet or the design of the instrument omitted a particular spectral band. Measurements received from the instrument may also be corrupt due to malfunction in the detectors on the instrument. Fortunately, there are machine learning techniques to estimate the missing or corrupt data. Using these techniques we can make use of the available data to its full potential. We present work on four different problems where the use of machine learning techniques helps to extract more information from available data. We demonstrate how missing or corrupt spectral measurements from a sensor can be accurately interpolated from existing spectral observations. Sometimes this requires data fusion from multiple sensors at different spatial and spectral resolution. The reconstructed measurements can then be used to develop products useful to scientists, such as cloud-top pressure, or produce true color imagery for visualization. Additionally, segmentation and image processing techniques can help solve classification problems important for ocean studies, such as the detection of clear-sky over ocean for a sea surface temperature product. In each case, we provide detailed analysis of the problem and empirical evidence that these problems can be solved effectively using machine learning techniques

CIRA annual report FY 2016/2017

Reporting period April 1, 2016-March 31, 2017

CIRA annual report FY 2017/2018

Reporting period April 1, 2017-March 31, 2018

CIRA annual report FY 2015/2016

Reporting period April 1, 2015-March 31, 2016

CIRA annual report FY 2014/2015

Reporting period July 1, 2014-March 31, 2015

A Year in Space for the CubeSat Multispectral Observing System: CUMULOS

CUMULOS is a three-camera system flying as a secondary payload on the Integrated Solar Array and Reflectarray Antenna (ISARA) mission with the goals of researching the use of uncooled commercial infrared cameras for Earth remote sensing and demonstrating unique nighttime remote sensing capabilities. Three separate cameras comprise the CUMULOS payload: 1) a visible (VIS) Si CMOS camera, 2) a shortwave infrared (SWIR) InGaAs camera, and 3) a longwave infrared (LWIR) vanadium oxide microbolometer. This paper reviews on-orbit operations during the past year, in-space calibration observations and techniques, and Earth remote sensing highlights from the first year of space operations. CUMULOS operations commenced on 8 June 2018 following the successful completion of the primary ISARA mission. Some of the unique contributions from the CUMULOS payloads include: 1) demonstrating the use of bright stars for on-orbit radiometric calibration of CubeSat payloads, 2) acquisition of science-quality nighttime lights data at 130-m resolution, and 3) operating the first simple Earth observing infrared payloads successfully flown on a CubeSat. Sample remote sensing results include images of: cities at night, ship lights (including fishing vessels), oil industry gas flares, serious wildfires, volcanic activity, and daytime and nighttime clouds. The CUMULOS VIS camera has measured calibrated nightlights imagery of major cities such as Los Angeles, Singapore, Shanghai, Tokyo, Kuwait City, Abu Dhabi, Jeddah, Istanbul, and London at more than 5x the resolution of VIIRS. The utility of these data for measuring light pollution, and mapping urban growth and infrastructure development at higher resolution than VIIRS is being studied, with an emphasis placed on Los Angeles. The Carr , Camp and Woolsey fires from the 2018 California fire season were imaged with all three cameras and results highlight the excellent wildfire imaging performance that can be achieved by small sensors. The SWIR camera has exhibited extreme sensitivity to flare and fire hotspots, and was even capable of detecting airglow-illuminated nighttime cloud structures by taking advantage of the strong OH emissions within its 0.9-1.7 micron bandpass. The LWIR microbolometer has proven successful at providing cloud context imagery for our nightlights mapping experiments, can detect very large fires and the brightest flare hotspots, and can also image terrain temperature variation and urban heat islands at 300-m resolution. CUMULOS capabilities show the potential of CubeSats and small sensors to perform several VIIRS-like nighttime mission areas in which wide area coverage can be traded for greater resolution over a smaller field of view. The sensor has been used in collaboration with VIIRS researchers to explore these mission areas and side-by-side results will be presented illustrating the capabilities as well as the limitations of small aperture LEO CubeSat systems

Early On-Orbit Performance of the Visible Infrared Imaging Radiometer Suite Onboard the Suomi National Polar-Orbiting Partnership (S-NPP) Satellite

The Visible Infrared Imaging Radiometer Suite (VIIRS) is one of the key environmental remote-sensing instruments onboard the Suomi National Polar-Orbiting Partnership spacecraft, which was successfully launched on October 28, 2011 from the Vandenberg Air Force Base, California. Following a series of spacecraft and sensor activation operations, the VIIRS nadir door was opened on November 21, 2011. The first VIIRS image acquired signifies a new generation of operational moderate resolution-imaging capabilities following the legacy of the advanced very high-resolution radiometer series on NOAA satellites and Terra and Aqua Moderate-Resolution Imaging Spectroradiometer for NASA's Earth Observing system. VIIRS provides significant enhancements to the operational environmental monitoring and numerical weather forecasting, with 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns, providing the sensor data records for 23 environmental data records including aerosol, cloud properties, fire, albedo, snow and ice, vegetation, sea surface temperature, ocean color, and nigh-time visible-light-related applications. Preliminary results from the on-orbit verification in the postlaunch check-out and intensive calibration and validation have shown that VIIRS is performing well and producing high-quality images. This paper provides an overview of the onorbit performance of VIIRS, the calibration/validation (cal/val) activities and methodologies used. It presents an assessment of the sensor initial on-orbit calibration and performance based on the efforts from the VIIRS-SDR team. Known anomalies, issues, and future calibration efforts, including the long-term monitoring, and intercalibration are also discussed