NASA's Black Marble Product Suite: Validation Strategy

NASA's Black Marble nighttime lights product suite (VNP46) is available at 500m resolution since January 2012 with data fro the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP). The retrieval algorithm, developed and implemented for routine global processing at NASA's Land Science Investigator-led Processing System (SIPS), utilizes all high-quality, cloud-free, atmospheric-terrain, vegetation, snow, lunar and stray light corrected radiances to estimate daily nighttime lights (NTL) and other intrinsic surface optical properties. Extensive benchmark tests at representative spatial and temporal scales were conducted on the VNP46 time series record to characterize the uncertainties stemming from upstream data sources. Current and planned validation activities under the Group on Earth Observations (GEO) Human Planet Initiative are aimed at evaluating the products at difference geographic locations and time periods representing the full range of retrieval conditions

Global Satellite Observations for Smart Cities

The smart city approach requires collection of interdisciplinary data and information from multiple sources and integration with modern technologies to provide a new and cost-effective way for researchers and decision makers to study and manage cities. In this book chapter, we introduce NASA satellite-based global and regional observations with emphasis on the hydrologic cycle (e.g., precipitation, wind, temperature, soil moisture) for smart cities. These products, consisting of both near-real-time and historical datasets, are publicly available free of charge and can be used for global and regional research and applications. Examples of using these datasets in smart cities are included. The chapter is organized as follows, first, a brief overview of NASA global satellite-based data products, followed by data services and tools, two examples of using satellite-based datasets in megacities, and finally summary and future plans

Multiple Angle Observations Would Benefit Visible Band Remote Sensing Using Night Lights

The spatial and angular emission patterns of artificial and natural light emitted, scattered, and reflected from the Earth at night are far more complex than those for scattered and reflected solar radiation during daytime. In this commentary, we use examples to show that there is additional information contained in the angular distribution of emitted light. We argue that this information could be used to improve existing remote sensing retrievals based on night lights, and in some cases could make entirely new remote sensing analyses possible. This work will be challenging, so we hope this article will encourage researchers and funding agencies to pursue further study of how multi‐angle views can be analyzed or acquired

Commentary: Multiple Angle Observations Would Benefit Visible Band Remote Sensing using Night Lights

The spatial and angular emission patterns of artificial and natural light emitted, scattered, and reflected from the Earth at night are far more complex than those for scattered and reflected solar radiation during daytime. In this commentary, we use examples to show that there is additional information contained in the angular distribution of emitted light. We argue that this information could be used to improve existing remote sensing retrievals based on night lights, and in some cases could make entirely new remote sensing analyses possible. This work will be challenging, so we hope this article will encourage researchers and funding agencies to pursue further study of how multi-angle views can be analyzed or acquired

Remote Sensing Information Sciences Research Group, Santa Barbara Information Sciences Research Group, year 3

Research continues to focus on improving the type, quantity, and quality of information which can be derived from remotely sensed data. The focus is on remote sensing and application for the Earth Observing System (Eos) and Space Station, including associated polar and co-orbiting platforms. The remote sensing research activities are being expanded, integrated, and extended into the areas of global science, georeferenced information systems, machine assissted information extraction from image data, and artificial intelligence. The accomplishments in these areas are examined

The Anchor, Volume 94.21: April 8, 1982

The Anchor began in 1887 and was first issued weekly in 1914. Covering national and campus news alike, Hope College’s student-run newspaper has grown over the years to encompass over two-dozen editors, reporters, and staff. For much of The Anchor\u27s history, the latest issue was distributed across campus each Wednesday throughout the academic school year (with few exceptions). As of Fall 2019 The Anchor has moved to monthly print issues and a more frequently updated website. Occasionally, the volume and/or issue numbering is irregular

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

Ableitung des Bruttoinlandprodukts von Brasilien auf Basis von Nacht- Satellitenbildern und weiteren Geodaten durch Anwendung eines Machine-Learning Modells

Der Begriff „Machine Learning“ ist mittlerweile im Alltag der meisten Menschen präsent, aber auch in der Wissenschaft spielt die Methode eine immer größer werdende Rolle in der Datenverarbeitung. Diese Masterarbeit widmet sich der Ableitung des Bruttoinlandsprodukts von Brasilien auf Basis von Nacht- Satellitenbildern und weiteren Geodaten durch die Anwendung eines Machine Learning Modells. Das Ziel dieser Arbeit ist die Erstellung eines Datensatzes, mit dem ein Machine Learning Modell trainiert werden kann, um das Bruttoinlandsprodukt bestimmter Regionen in Brasilien vorherzusagen. Die Forschungsfragen, die im Zuge dieser Arbeit behandelt werden, beschäftigen sich damit, ob das Modell auf unterschiedliche Testgebiete innerhalb Brasiliens angewandt werden kann und ob ein Zusammenhang zwischen der nächtlichen Beleuchtung und der Wirtschaftskraft einer Region besteht. Der Datensatz, der zur Beantwortung der Forschungsfragen erstellt werden muss, besteht aus Referenzdaten, die in einer Auflösung von 1x1km das BIP enthalten und den Inputdaten des Modells, die in der gleichen geometrischen Auflösung akquiriert werden müssen. Die Referenzdaten des Bruttoinlandsprodukts werden durch eine einkommensbasierte Disaggregation erstellt. Die Inputdaten hingegen bestehen aus Sentinel-2- und Black Marble Satellitenbildern, sowie aus den Sentinel-2 Bandkombinationen „NDVI“, „NDBI“ und „MNDWI“. Nach der Erstellung des Datensatzes werden unterschiedlichste Parameterkombinationen für das Modell getestet, um ein optimales Ergebnis zu erzielen. Beim Machine Learning Modell handelt es sich um ein Fusionsmodell aus einem Convolutional Neural Network (CNN) und einem Multilayer Perceptron (MLP), welches vom DLR speziell für diesen Einsatz entwickelt und zur Verfügung gestellt wurde. Durch die Anwendung des Vorhersagemodells konnten Ergebnisse für 14 der bevölkerungsreichsten Städte Brasiliens berechnet werden. Darunter Sao Paulo, mit einem Bruttoinlandsprodukt von 687.035.890 brasilianischen Real (R$). Das BIP konnte mit einem Determinationskoeffizienten R² von 0,64 und einer Pearson Korrelation R von 0,8 nachmodelliert werden. Die Genauigkeit der Modellierung variiert jedoch stark zwischen den unterschiedlichen Testgebieten. Es stellt sich heraus, dass die Werte durch die Modellierung geglättet werden und somit Ausreißer in den BIP-Werten verloren gehen. Weiters werden Kacheln, die sich in ruralen Regionen befinden, zu hohe Werte zugewiesen. Der Einfluss der nächtlichen Beleuchtung auf das BIP zeigt sich in der Verbesserung der Performance des Modells durch die Einbindung der Black Marble Daten. Weiterführende Forschung in diesem Bereich wäre eine Disaggregation der BIP-Daten nach Wirtschaftssektoren unter Einbindung von Landbedeckungs- und OpenStreetMap Daten, sowie eine weitere Optimierung des Machine Learning Modells hinsichtlich ruraler Räume

Atmospheric Research 2012 Technical Highlights

This annual report, as before, is intended for a broad audience. Our readers include colleagues within NASA, scientists outside the Agency, science graduate students, and members of the general public. Inside are descriptions of atmospheric research science highlights and summaries of our education and outreach accomplishments for calendar year 2012.The report covers research activities from the Mesoscale Atmospheric Processes Laboratory, the Climate and Radiation Laboratory, the Atmospheric Chemistry and Dynamics Laboratory, and the Wallops Field Support Office under the Office of Deputy Director for Atmospheres, Earth Sciences Division in the Sciences and Exploration Directorate of NASAs Goddard Space Flight Center. The overall mission of the office is advancing knowledge and understanding of the Earths atmosphere. Satellite missions, field campaigns, peer-reviewed publications, and successful proposals are essential to our continuing research