Validation of S-NPP VIIRS Day-Night Band and M Bands Performance Using Ground Reference Targets of Libya 4 and Dome C

This paper provides methodologies developed and implemented by the NASA VIIRS Calibration Support Team (VCST) to validate the S-NPP VIIRS Day-Night band (DNB) and M bands calibration performance. The Sensor Data Records produced by the Interface Data Processing Segment (IDPS) and NASA Land Product Evaluation and Algorithm Testing Element (PEATE) are acquired nearly nadir overpass for Libya 4 desert and Dome C snow surfaces. In the past 3.5 years, the modulated relative spectral responses (RSR) change with time and lead to 3.8% increase on the DNB sensed solar irradiance and 0.1% or less increases on the M4-M7 bands. After excluding data before April 5th, 2013, IDPS DNB radiance and reflectance data are consistent with Land PEATE data with 0.6% or less difference for Libya 4 site and 2% or less difference for Dome C site. These difference are caused by inconsistent LUTs and algorithms used in calibration. In Libya 4 site, the SCIAMACHY spectral and modulated RSR derived top of atmosphere (TOA) reflectance are compared with Land PEATE TOA reflectance and they indicate a decrease of 1.2% and 1.3%, respectively. The radiance of Land PEATE DNB are compared with the simulated radiance from aggregated M bands (M4, M5, and M7). These data trends match well with 2% or less difference for Libya 4 site and 4% or less difference for Dome C. This study demonstrate the consistent quality of DNB and M bands calibration for Land PEATE products during operational period and for IDPS products after April 5th, 2013

Artificially lit surface of Earth at night increasing in radiance and extent

This is the author accepted manuscript. The final version is available from AAAS via the DOI in this record.A central aim of the “lighting revolution” (the transition to solid-state lighting technology) is decreased energy consumption. This could be undermined by a rebound effect of increased use in response to lowered cost of light. We use the first-ever calibrated satellite radiometer designed for night lights to show that from 2012 to 2016, Earth’s artificially lit outdoor area grew by 2.2% per year, with a total radiance growth of 1.8% per year. Continuously lit areas brightened at a rate of 2.2% per year. Large differences in national growth rates were observed, with lighting remaining stable or decreasing in only a few countries. These data are not consistent with global scale energy reductions but rather indicate increased light pollution, with corresponding negative consequences for flora, fauna, and human well-being.This article is based upon work from COST Action ES1204 LoNNe, supported by COST (European Cooperation in Science and Technology). The authors acknowledge the funding received by ERA-PLANET (www.era-planet.eu) funded by the EC as part of H2020 (contract no. 689443). NOAA’s participation was funded by NASA’s VIIRS science program, contract number NNH15AZ01I. ASM’s contribution was funded by ORISON project (H2020-INFRASUPP-2015-2) Cities at Night

Multi-nighttime-light data comparison analysis based on image quality values and lit fishing vessel identification effect

Fisheries provide high-quality protein for many people, and their sustainable use is of global concern. Light trapping is a widely used fishing method that takes advantage of the phototropism of fish. Remote sensing technology allows for the monitoring of lit fishing vessels at sea from the air at night, which supports the sustainable management of fisheries. To investigate the potential of different nighttime light remote sensing data for lit fishing vessel identification and applications, we used the fuzzy evaluation method to quantitatively assess images in terms of their radiometric and geometric quality, and Otsu’s method to compare the effects of lit fishing vessel identification. Three kinds of nighttime lighting data from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS), Visible infrared imaging radiometer suite day/night band (VIIRS/DNB), and Luojia1-01(LJ1-01) were analyzed, compared, and application pointers were constructed. The results are as follows. ①In the image radiation quality evaluation, the information entropy, clarity, and noise performance of the LJ1-01 image are higher than those of the DMSP/OLS and VIIRS/DNB images, where the information entropy value of the LJ1-01 image is nearly 10 times that of VIIRS/DNB and 23 times that of DMSP/OLS. The average gradient value is 14 times that of the image from VIIRS/DNB and 1,600 times that of DMSP/OLS, while its noise is only about 2/3 of the VIIRS/DNB image and 1/3 of the DMSP/OLS image. In the geometric quality assessment, the geometric positioning accuracy and ground sampling accuracy of the VIIRS/DNB image is the best among the three images, with a relative difference percentage of 100.1%, and the LJ1-01 and DMSP/OLS images are relatively lower, at 96.9% and 92.3%, respectively. ② The detection of squid fishing vessels in the Northwest Pacific is taken as an example to compare the identification effects of three types of data: DMSP/OLS, VIIRS/DNB, and LJ1-01. Among these data, DMSP/OLS can effectively identify the position of the lit fishing boat, and VIIRS/DNB images can accurately estimate the spatial position and number of lit fishing boats with large distances. However, in the case of fishing boats gathering or clustering, the number of fishing vessels could not be identified. This led to the detected number of lit fishing vessels being less than the real value. For the VIIRS/DNB and LJ1-01 images with a 5′×8′ span in the same spatiotemporal range using the same batch of pelagic squid fishing vessels, LJ1-01 extracted 18 fishing vessels. VIIRS/DNB extracted 15, indicating that LJ1-01 can distinguish multiple fishing vessels in the lighted overlapping area, thus accurately identifying the number of fishing vessels. The application pointing table generated based on the results of the three data analyses can provide a reference for sensor/image selection for nighttime light remote sensing fishery applications and a basis for more refined fishing vessel identification, extraction, and monitoring

CIRA annual report FY 2016/2017

Reporting period April 1, 2016-March 31, 2017

The Suomi National Polar-Orbiting Partnership (SNPP): Continuing NASA Research and Applications

The Suomi National Polar-orbiting Partnership (SNPP) satellite was successfully launched into a polar orbit on October 28, 2011 carrying 5 remote sensing instruments designed to provide data to improve weather forecasts and to increase understanding of long-term climate change. SNPP provides operational continuity of satellite-based observations for NOAA's Polar-orbiting Operational Environmental Satellites (POES) and continues the long-term record of climate quality observations established by NASA's Earth Observing System (EOS) satellites. In the 2003 to 2011 pre-launch timeframe, NASA's SNPP Science Team assessed the adequacy of the operational Raw Data Records (RDRs), Sensor Data Records (SDRs), and Environmental Data Records (EDRs) from the SNPP instruments for use in NASA Earth Science research, examined the operational algorithms used to produce those data records, and proposed a path forward for the production of climate quality products from SNPP. In order to perform these tasks, a distributed data system, the NASA Science Data Segment (SDS), ingested RDRs, SDRs, and EDRs from the NOAA Archive and Distribution and Interface Data Processing Segments, ADS and IDPS, respectively. The SDS also obtained operational algorithms for evaluation purposes from the NOAA Government Resource for Algorithm Verification, Independent Testing and Evaluation (GRAVITE). Within the NASA SDS, five Product Evaluation and Test Elements (PEATEs) received, ingested, and stored data and performed NASA's data processing, evaluation, and analysis activities. The distributed nature of this data distribution system was established by physically housing each PEATE within one of five Climate Analysis Research Systems (CARS) located at either at a NASA or a university institution. The CARS were organized around 5 key EDRs directly in support of the following NASA Earth Science focus areas: atmospheric sounding, ocean, land, ozone, and atmospheric composition products. The PEATES provided the system level interface with members of the NASA SNPP Science Team and other science investigators within each CARS. A sixth Earth Radiation Budget CARS was established at NASA Langley Research Center (NASA LaRC) to support instrument performance, data evaluation, and analysis for the SNPP Clouds and the Earth's Radiant Budget Energy System (CERES) instrument. Following the 2011 launch of SNPP, spacecraft commissioning, and instrument activation, the NASA SNPP Science Team evaluated the operational RDRs, SDRs, and EDRs produced by the NOAA ADS and IDPS. A key part in that evaluation was the NASA Science Team's independent processing of operational RDRs and SDRs to EDRs using the latest NASA science algorithms. The NASA science evaluation was completed in the December 2012 to April 2014 timeframe with the release of a series of NASA Science Team Discipline Reports. In summary, these reports indicated that the RDRs produced by the SNPP instruments were of sufficiently high quality to be used to create data products suitable for NASA Earth System science and applications. However, the quality of the SDRs and EDRs were found to vary greatly when considering suitability for NASA science. The need for improvements in operational algorithms, adoption of different algorithmic approaches, greater monitoring of on-orbit instrument calibration, greater attention to data product validation, and data reprocessing were prominent findings in the reports. In response to these findings, NASA, in late 2013, directed the NASA SNPP Science Team to use SNPP instrument data to develop data products of sufficiently high quality to enable the continuation of EOS time series data records and to develop innovative, practical applications of SNPP data. This direction necessitated a transition of the SDS data system from its pre-launch assessment mode to one of full data processing and production. To do this, the PEATES, which served as NASA's data product testing environment during the prelaunch and early on-orbit periods, were transitioned to Science Investigator-led Processing Systems (SIPS). The distributed data architecture was maintained in this new system by locating the SIPS at the same institutions at which the CARS and PEATES were located. The SIPS acquire raw SNPP instrument Level 0 (i.e. RDR) data over the full SNPP mission from the NOAA ADS and IDPS through the NASA SDS Data Distribution and Depository Element (SD3E). The SIPS process those data into NASA Level 1, Level 2, and global, gridded Level 3 standard products using peer-reviewed algorithms provided by members of the NASA Science Team. The SIPS work with the NASA SNPP Science Team in obtaining enhanced, refined, or alternate real-time algorithms to support the capabilities of the Direct Readout Laboratory (DRL). All data products, algorithm source codes, coefficients, and auxiliary data used in product generation are archived in an assigned NASA Distributed Active Archive Center (DAAC)

Remote sensing of night lights: a review and an outlook for the future

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordRemote sensing of night light emissions in the visible band offers a unique opportunity to directly observe human activity from space. This has allowed a host of applications including mapping urban areas, estimating population and GDP, monitoring disasters and conflicts. More recently, remotely sensed night lights data have found use in understanding the environmental impacts of light emissions (light pollution), including their impacts on human health. In this review, we outline the historical development of night-time optical sensors up to the current state of the art sensors, highlight various applications of night light data, discuss the special challenges associated with remote sensing of night lights with a focus on the limitations of current sensors, and provide an outlook for the future of remote sensing of night lights. While the paper mainly focuses on space borne remote sensing, ground based sensing of night-time brightness for studies on astronomical and ecological light pollution, as well as for calibration and validation of space borne data, are also discussed. Although the development of night light sensors lags behind day-time sensors, we demonstrate that the field is in a stage of rapid development. The worldwide transition to LED lights poses a particular challenge for remote sensing of night lights, and strongly highlights the need for a new generation of space borne night lights instruments. This work shows that future sensors are needed to monitor temporal changes during the night (for example from a geostationary platform or constellation of satellites), and to better understand the angular patterns of light emission (roughly analogous to the BRDF in daylight sensing). Perhaps most importantly, we make the case that higher spatial resolution and multispectral sensors covering the range from blue to NIR are needed to more effectively identify lighting technologies, map urban functions, and monitor energy use.European Union Horizon 2020Helmholtz AssociationNatural Environment Research Council (NERC)Chinese Academy of ScienceLeibniz AssociationIGB Leibniz Institut

Cloud Detection And Trace Gas Retrieval From The Next Generation Satellite Remote Sensing Instruments

Thesis (Ph.D.) University of Alaska Fairbanks, 2005The objective of this thesis is to develop a cloud detection algorithm suitable for the National Polar Orbiting Environmental Satellite System (NPOESS) Visible Infrared Imaging Radiometer Suite (VIIRS) and methods for atmospheric trace gas retrieval for future satellite remote sensing instruments. The development of this VIIRS cloud mask required a flowdown process of different sensor models in which a variety of sensor effects were simulated and evaluated. This included cloud simulations and cloud test development to investigate possible sensor effects, and a comprehensive flowdown analysis of the algorithm was conducted. In addition, a technique for total column water vapor retrieval using shadows was developed with the goal of enhancing water vapor retrievals under hazy atmospheric conditions. This is a new technique that relies on radiance differences between clear and shadowed surfaces, combined with ratios between water vapor absorbing and window regions. A novel method for retrieving methane amounts over water bodies, including lakes, rivers, and oceans, under conditions of sun glint has also been developed. The theoretical basis for the water vapor as well as the methane retrieval techniques is derived and simulated using a radiative transfer model

CIRA annual report FY 2014/2015

Reporting period July 1, 2014-March 31, 2015

Microphysical Properties and Remote Sensing of High Ice Water Content Environments over the Contiguous United States

Aircraft flying through areas of high ice water content (HIWC) conditions can experience engine damage and/or failure. HIWC is typically associated with convection and the microphysical properties of HIWC in deep tropical oceanic convection are well documented thanks to four field campaigns in the past decade. Several experimental automated HIWC detection algorithms have also been built and verified using observations from these tropical field campaigns. HIWC is thought to be less common in extratropical convection, but instances of HIWC-related aircraft issues have been recorded in extratropical weather, even during winter. The relative frequency of extratropical HIWC compared to tropical HIWC is unknown, and since most HIWC detection algorithms were developed using data from the tropical HIWC field campaigns, they may not capture extratropical HIWC well. The HIWC detection algorithms also rely heavily on geostationary satellite observations and have reduced accuracy at night due to the lack of shortwave (SW) radiation. This thesis addresses these knowledge gaps and limitations by 1) developing a climatology of HIWC conditions 2) characterizing the environments and microphysical properties in five case of extratropical winter HIWC and 3) developing a method to extrapolate SW geostationary satellite observations through nighttime. The climatology developed uses ice water content (IWC) retrievals from satellite-borne radar and lidar and indicates HIWC is about as common in the extratropics as tropics, on average, but is typically found at lower altitudes. The cases of extratropical winter HIWC examined all involved elevated convection with maximum water vapor mixing ratios and total-column precipitable water of about 5 g kg-1 and 20 mm, respectively. The maximum IWC observed during these winter cases was about half of the maximum observed during the tropical HIWC field campaigns, but the size distribution of ice particles in the HIWC was similar. Finally, the method to extrapolate SW satellite imagery through night gave typical mean absolute errors for the extrapolated SW reflectance between 1.0 and 12.7%, depending on the wavelength. A method to predict the performance of the SW extrapolation was also developed so users can weight the information appropriately. Together, these works provide information and capabilities needed for improved situational awareness and HIWC detection algorithms.</p