Daytime measurements of reflected sunlight in the visible spectrum have been a staple of Earth-viewing radiometers since the advent of the environmental satellite platform. At night, these same optical-spectrum sensors have traditionally been limited to thermal infrared emission, which contains relatively poor information content for many important weather and climate parameters. These deficiencies have limited our ability to characterize the full diurnal behavior and processes of parameters relevant to improved monitoring, understanding and modeling of weather and climate processes. Visible-spectrum light information does exist during the nighttime hours, originating from a wide variety of sources, but its detection requires specialized technology. Such measurements have existed, in a limited way, on USA Department of Defense satellites, but the Suomi National Polar-orbiting Partnership (NPP) satellite, which carries a new Day/Night Band (DNB) radiometer, offers the first quantitative measurements of nocturnal visible and near-infrared light. Here, we demonstrate the expanded potential for nocturnal low-light visible applications enabled by the DNB. Via a combination of terrestrial and extraterrestrial light sources, such observations are always available—expanding many current existing applications while enabling entirely new capabilities. These novel low-light measurements open doors to a wealth of new interdisciplinary research topics while lighting a pathway toward the optimized design of follow-on satellite based low light visible sensors.

Floods cause extensive global damage annually, making effective monitoring essential. While satellite observations have proven invaluable for flood detection and tracking, comprehensive global flood datasets spanning extended time periods remain scarce. In this study, we introduce a deep learning flood detection model that leverages the cloud-penetrating capabilities of Sentinel-1 Synthetic Aperture Radar (SAR) satellite imagery, enabling consistent flood extent mapping through cloud cover and in both day and night conditions. By applying this model to 10 years of SAR data, we create a unique, longitudinal global flood extent dataset with predictions unaffected by cloud coverage, offering comprehensive and consistent insights into historically flood-prone areas over the past decade. We use our model predictions to identify historically flood-prone areas in Ethiopia and demonstrate real-time disaster response capabilities during the May 2024 floods in Kenya. Additionally, our longitudinal analysis reveals potential increasing trends in global flood extent over time, although further validation is required to explore links to climate change. To maximize impact, we provide public access to both our model predictions and a code repository, empowering researchers and practitioners worldwide to advance flood monitoring and enhance disaster response strategies.

Maritime ships operating on-board illumination at night appear as point sources of light to highly sensitive low-light imagers on-board environmental satellites. Unlike city lights or lights from offshore gas platforms, whose locations remain stationary from one night to the next, lights from ships typically are ephemeral. Fishing boat lights are most prevalent near coastal cities and along the thermal gradients in the open ocean. Maritime commercial ships also operate lights that can be detected from space. Such observations have been made in a limited way via U.S. Department of Defense satellites since the late 1960s. However, the Suomi National Polar-orbiting Partnership (S-NPP) satellite, which carries a new Day/Night Band (DNB) radiometer, offers a vastly improved ability for users to observe commercial shipping in remote areas such as the Arctic. Owing to S-NPP’s polar orbit and the DNB’s wide swath (~3040 km), the same location in Polar Regions can be observed for several successive passes via overlapping swaths—offering a limited ability to track ship motion. Here, we demonstrate the DNB’s improved ability to monitor ships from space. Imagery from the DNB is compared with the heritage low-light sensor, the Operational Linescan System (OLS) on board the Defense Meteorological Support Program (DMSP) satellites, and is evaluated in the context of tracking individual ships in the Polar Regions under both moonlit and moonless conditions. In a statistical sense, we show how DNB observations of ship lights in the East China Sea can be correlated with seasonal fishing activity, while also revealing compelling structures related to regional fishery agreements established between various nations.

The Geostationary Operational Environmental Satellite‐R (GOES‐R) series started a new era for the U.S. geostationary satellite observing system. The Advanced Baseline Imager (ABI) onboard the GOES‐R series has fine temporal (30 s to 10 min) and spatial resolutions (0.5–2 km), and 16 spectral bands. However, due to the lack of an infrared sounder, the ABI is used to continue the legacy atmospheric profile (LAP) products that the previous GOES Sounder has, including the legacy atmospheric moisture profile, legacy atmospheric temperature profile, total precipitable water, layered precipitable water, and derived atmospheric stability indices. The ABI LAP retrieval algorithms have been developed under the GOES‐R series Algorithm Working Group (AWG) program funded by the GOES‐R Program Office. The LAP products from GOES‐16 have been operational and validated with a series of reference data sets including radiosonde observations, the Global Positioning System from SuomiNet, the Advanced Microwave Scanning Radiometer 2 total precipitable water measurements, as well as global operational analysis from National Oceanic and Atmospheric Administration and European Centre for Medium‐Range Weather Forecasts models, for almost a year (from 2017 to 2018) to assure the data quality for applications. In addition, the LAP products have been successfully demonstrated at the Hazardous Weather Testbed experiments in the summer of 2017 and the spring of 2018. Both validation results and Hazardous Weather Testbed demonstrations indicate that the GOES‐R series LAP products meet the product requirements and provide added value over NWP short‐range forecasts, especially for middle‐upper tropospheric moisture, in situation awareness and nowcasting.

The COVID-19 pandemic has infected almost 73 million people and is responsible for over 1.63 million fatalities worldwide since early December 2019, when it was first reported in Wuhan, China. In the early stages of the pandemic, social distancing measures, such as lockdown restrictions, were applied in a non-uniform way across the world to reduce the spread of the virus. While such restrictions contributed to flattening the curve in places like Italy, Germany, and South Korea, it plunged the economy in the United States to a level of recession not seen since WWII, while also improving air quality due to the reduced mobility. Using daily Earth observation data (Day/Night Band (DNB) from the National Oceanic and Atmospheric Administration Suomi-NPP and NO₂ measurements from the TROPOspheric Monitoring Instrument TROPOMI) along with monthly averaged cell phone derived mobility data, we examined the economic and environmental impacts of lockdowns in Los Angeles, California; Chicago, Illinois; Washington DC from February to April 2020—encompassing the most profound shutdown measures taken in the U.S. The preliminary analysis revealed that the reduction in mobility involved two major observable impacts: (i) improved air quality (a reduction in NO₂ and PM_2.5 concentration), but (ii) reduced economic activity (a decrease in energy consumption as measured by the radiance from the DNB data) that impacted on gross domestic product, poverty levels, and the unemployment rate. With the continuing rise of COVID-19 cases and declining economic conditions, such knowledge can be combined with unemployment and demographic data to develop policies and strategies for the safe reopening of the economy while preserving our environment and protecting vulnerable populations susceptible to COVID-19 infection.

Since 2 June 2020, unusual heavy and continuous rainfall from the Asian summer monsoon rainy season caused widespread catastrophic floods in many Asian countries, including primarily the two most populated countries, China and India. To detect and monitor the floods and estimate the potentially affected population, data from sensors aboard the operational polar-orbiting satellites Suomi National Polar-Orbiting Partnership (S-NPP) and National Oceanic and Atmospheric Administration (NOAA)-20 were used. The Visible Infrared Imaging Radiometer Suite (VIIRS) with a spatial resolution of 375 m available twice per day aboard these two satellites can observe floodwaters over large spatial regions. The flood maps derived from the VIIRS imagery provide a big picture over the entire flooding regions, and demonstrate that, in July, in China, floods mainly occurred across the Yangtze River, Hui River and their tributaries. The VIIRS 5-day composite flood maps, along with a population density dataset, were combined to estimate the population potentially exposed (PPE) to flooding. We report here on the procedure to combine such data using the Zonal Statistic Function from the ArcGIS Spatial Analyst toolbox. Based on the flood extend for July 2020 along with the population density dataset, the Jiangxi and Anhui provinces were the most affected regions with more than 10 million people in Jingdezhen and Shangrao in Jiangxi province, and Fuyang and Luan in Anhui province, and it is estimated that about 55 million people in China might have been affected by the floodwaters. In addition to China, several other countries, including India, Bangladesh, and Myanmar, were also severely impacted. In India, the worst inundated states include Utter Pradesh, Bihar, Assam, and West Bengal, and it is estimated that about 40 million people might have been affected by severe floods, mainly in the northern states of Bihar, Assam, and West Bengal. The most affected country was Bangladesh, where one third of the country was underwater, and the estimated population potentially exposed to floods is about 30 million in Bangladesh.