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At least 19 records

Ten Years of VIIRS Land Surface Temperature Product Validation

The Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature (LST) has been operationally produced for a decade since the Suomi National Polar-orbiting Partnership (SNPP) launched in October 2011. A comprehensive evaluation of its accuracy and precision will be helpful for product users in climate studies and atmospheric models. In this study, the VIIRS LST is validated with ground observations from multiple high-quality radiation networks, including six stations from the Surface Radiation budget (SURFRAD) network, two stations from the Baseline Surface Radiation Network (BSRN), and 13 stations from the Atmospheric Radiation Measurement (ARM) network, to evaluate its performance over various land-cover types. The VNP21A1 LST was validated against the same ground observations as a reference. The results yield a close agreement between the SNPP VIIRS LST and ground LSTs with a bias of -0.4 K and a RMSE of 1.96 K over six SURFRAD sites; a bias of -0.2 K and a RMSE of 1.93 K over two BSRN sites; and a bias of -0.1 K and a RMSE of 1.7 K over the 13 ARM sites. The time series of the LST errors over individual sites indicate seasonal cycles. The data anomaly over the BSRN site in Cabauw and the SURFRAD site in Desert Rock is revealed and discussed in this study. In addition, a method using Landsat-8 data is applied to quantify the heterogeneity level of each ground station and the results provide promising insights. The validation results demonstrate the maturity of the JPSS VIIRS LST products and their readiness for various application studies.

54 ENVIRONMENTAL SCIENCES↗

Validation of the Sentinel-5 Precursor TROPOMI cloud data with Cloudnet, Aura OMI O 2 –O 2 , MODIS, and Suomi-NPP VIIRS

Accurate knowledge of cloud properties is essential to the measurement of atmospheric composition from space. In this work we assess the quality of the cloud data from three Copernicus Sentinel-5 Precursor (S5P) TROPOMI cloud products: (i) S5P OCRA/ROCINN_CAL (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks;Clouds-As-Layers), (ii) S5P OCRA/ROCINN_CRB (Clouds-as-Reflecting Boundaries), and (iii) S5P FRESCO-S (Fast Retrieval Scheme for Clouds from Oxygen absorption bands – Sentinel). Target properties of this work are cloud-top height and cloud optical thickness (OCRA/ROCINN_CAL), cloud height (OCRA/ROCINN_CRB and FRESCO-S), and radiometric cloud fraction (all three algorithms). The analysis combines (i) the examination of cloud maps for artificial geographical patterns, (ii) the comparison to other satellite cloud data (MODIS, NPP-VIIRS, and OMI O2–O2), and (iii) ground-based validation with respect to correlative observations (30 April 2018 to 27 February 2020) from the Cloudnet network of ceilometers, lidars, and radars. Zonal mean latitudinal variation of S5P cloud properties is similar to that of other satellite data. S5P OCRA/ROCINN_CAL agrees well with NPP VIIRS cloud-top height and cloud optical thickness and with Cloudnet cloud-top height, especially for the low (mostly liquid) clouds. For the high clouds, S5P OCRA/ROCINN_CAL cloud-top height is below the cloud-top height of VIIRS and of Cloudnet, while its cloud optical thickness is higher than that of VIIRS. S5P OCRA/ROCINN_CRB and S5P FRESCO cloud height are well below the Cloudnet cloud mean height for the low clouds but match on average better with the Cloudnet cloud mean height for the higher clouds. As opposed to S5P OCRA/ROCINN_CRB and S5P FRESCO, S5P OCRA/ROCINN_CAL is well able to match the lowest CTH mode of the Cloudnet observations. Peculiar geographical patterns are identified in the cloud products and will be mitigated in future releases of the cloud data products.

54 ENVIRONMENTAL SCIENCES↗

Statistical moments of VIIRS night-time lights

Annual VIIRS global night-time lights traditionally report mean radiance levels after filtering to remove moonlit and cloudy data followed by outlier removal. The mean is the first statistical moment, but there are three other statistical moments (variance, skew and kurtosis) that are currently not produced globally. In this study, we generated VIIRS day/night band multiyear and annual statistical moments for a widely dispersed set of test areas. The moments were calculated from 15 arc second nightly temporal profiles spanning 2012-2020, filtered to exclude cloudy and sunlit data, with radiance adjustments to reduce view angle and lunar illuminance effects. The moment data were examined in two ways: (1) Geospatial grids-which reveal zonation and temporal changes present in urban areas and (2) Scattergrams of moment pairs. The variance versus mean scattergram exhibits several distinct data clusters used to define five zones: core lighting, dark-erratics, mid-erratics, bright-erratics and bright and seteady. The results indicate that the information content of VIIRS night-time lights can be augmented if all four statistical moments are calculated.

47 OTHER INSTRUMENTATION↗

Monthly Quality-filtered Aggregation of NOAA Climate Data Record (CDR) of AVHRR (Version 5) and VIIRS (Version 1) Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)

This dataset contains gridded monthly Leaf Area Index (LAI) derived from the daily NOAA Climate Data Record (CDR) of AVHRR (Version 5) and VIIRS (Version 1) Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). This data record spans from 1981 to 2024 using data from NOAA polar orbiting satellites: NOAA-7, -9, -11, -14, -16, -17, -18, -19 and S-NPP. The data are projected on a 0.05 degree x 0.05 degree global grid, as in the original CDR. The original CDR is one of the Land Surface CDR products produced by the NASA Goddard Space Flight Center (GSFC) and the University of Maryland (UMD), which is accompanied by algorithm documentation, data flow diagram and source code for the NOAA CDR Program. This dataset is in the netCDF-4 file format following ACDD and CF Conventions. This dataset has applied quality assurance information to only include "OK" data from the original CDR in the monthly aggregation.

Vermote, Eric [NASA Goddard Space Flight Center (G↗

California wildfire spread derived using VIIRS satellite observations and an object-based tracking system

Changing wildfire regimes in the western US and other fire-prone regions pose considerable risks to human health and ecosystem function. However, our understanding of wildfire behavior is still limited by a lack of data products that systematically quantify fire spread, behavior and impacts. Here we develop a novel object-based system for tracking the progression of individual fires using 375 m Visible Infrared Imaging Radiometer Suite active fire detections. At each half-daily time step, fire pixels are clustered according to their spatial proximity, and are either appended to an existing active fire object or are assigned to a new object. This automatic system allows us to update the attributes of each fire event, delineate the fire perimeter, and identify the active fire front shortly after satellite data acquisition. Using this system, we mapped the history of California fires during 2012–2020. Our approach and data stream may be useful for calibration and evaluation of fire spread models, estimation of near-real-time wildfire emissions, and as means for prescribing initial conditions in fire forecast models.

54 ENVIRONMENTAL SCIENCES↗

A harmonized global nighttime light dataset 1992–2018

Nighttime light (NTL) data from the Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership satellite provide a great opportunity for monitoring human activities from regional to global scales. Despite the valuable records of nightscape from DMSP (1992–2013) and VIIRS (2012–2018), the potential of the historical archive of NTL observations has not been fully explored because of the severe inconsistency between DMSP and VIIRS. In this study, we generated an integrated and consistent NTL dataset at the global scale by harmonizing the inter-calibrated NTL observations from the DMSP data and the simulated DMSP-like NTL observations from the VIIRS data. The generated global DMSP NTL time-series data (1992–2018) show consistent temporal trends. This temporally extended DMSP NTL dataset provides valuable support for various studies related to human activities such as electricity consumption and urban extent dynamics.

54 ENVIRONMENTAL SCIENCES↗

Illuminating the Night: A Survey of Super-Resolution Methods for Nighttime Light Images

Nighttime Light (NTL) images provide critical insights into urbanization, disaster response, and energy consumption. The VIIRS Day/Night Band (DNB) sensor offers high-quality NTL imagery with daily revisit rates, but the available spatial resolution hinders fine-grained accurate analysis. Super-resolution techniques aim to increase the resolution of NTL images, enabling more detailed assessments of infrastructure, light pollution, economic activity, and power outages. However, existing state-of-the-art super-resolution methods designed for natural images struggle with the unique characteristics of NTL data. This work provides a comprehensive review of super-resolution methods across multiple image modalities, evaluates their effectiveness on VIIRS DNB data, and proposes a multi-modal super-resolution approach tailored to NTL imagery. The proposed approach integrates VIIRS DNB data with road networks and land use information to improve reconstruction accuracy and spatial detail. Code is available for this project at https://code.ornl.gov/viirs-sr/sr-demos.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Illuminating the Night: A Survey of Super-Resolution Methods for Nighttime Light Images

Nighttime Light (NTL) images provide critical insights into urbanization, disaster response, and energy consumption. The VIIRS Day/Night Band (DNB) sensor offers high-quality NTL imagery with daily revisit rates, but the available spatial resolution hinders fine-grained accurate analysis. Super-resolution techniques aim to increase the resolution of NTL images, enabling more detailed assessments of infrastructure, light pollution, economic activity, and power outages. However, existing state-of-the-art super-resolution methods designed for natural images struggle with the unique characteristics of NTL data. This work provides a comprehensive review of super-resolution methods across multiple image modalities, evaluates their effectiveness on VI-IRS DNB data, and proposes a multi-modal super-resolution approach tailored to NTL imagery. The proposed approach integrates VIIRS DNB data with road networks and land use information to improve reconstruction accuracy and spatial detail. Code is available for this project athttps://code.ornl.gov/viirs-sr/sr-demos.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Updating and Evaluating Anthropogenic Emissions for NOAA’s Global Ensemble Forecast Systems for Aerosols (GEFS-Aerosols): Application of an SO 2 Bias-Scaling Method

We updated the anthropogenic emissions inventory in NOAA’s operational Global Ensemble Forecast for Aerosols (GEFS-Aerosols) to improve the model’s prediction of aerosol optical depth (AOD). We used a methodology to quickly update the pivotal global anthropogenic sulfur dioxide (SO 2 ) emissions using a speciated AOD bias-scaling method. The AOD bias-scaling method is based on the latest model predictions compared to NASA’s Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2). The model bias was subsequently applied to the CEDS 2019 SO 2 emissions for adjustment. The monthly mean GEFS-Aerosols AOD predictions were evaluated against a suite of satellite observations (e.g., MISR, VIIRS, and MODIS), ground-based AERONET observations, and the International Cooperative for Aerosol Prediction (ICAP) ensemble results. The results show that transitioning from CEDS 2014 to CEDS 2019 emissions data led to a significant improvement in the operational GEFS-Aerosols model performance, and applying the bias-scaled SO 2 emissions could further improve global AOD distributions. The biases of the simulated AODs against the observed AODs varied with observation type and seasons by a factor of 3~13 and 2~10, respectively. The global AOD distributions showed that the differences in the simulations against ICAP, MISR, VIIRS, and MODIS were the largest in March–May (MAM) and the smallest in December–February (DJF). When evaluating against the ground-truth AERONET data, the bias-scaling methods improved the global seasonal correlation (r), Index of Agreement (IOA), and mean biases, except for the MAM season, when the negative regional biases were exacerbated compared to the positive regional biases. The effect of bias-scaling had the most beneficial impact on model performance in the regions dominated by anthropogenic emissions, such as East Asia. However, it showed less improvement in other areas impacted by the greater relative transport of natural emissions sources, such as India. The accuracies of the reference observation or assimilation data for the adjusted inputs and the model physics for outputs, and the selection of regions with less seasonal emissions of natural aerosols determine the success of the bias-scaling methods. A companion study on emission scaling of anthropogenic absorbing aerosols needs further improved aerosol prediction.

54 ENVIRONMENTAL SCIENCES↗

Comparing Coarse-Resolution Land Surface Temperature Products over Western Australia

Satellite-derived land surface temperature (LST) has commonly been used to monitor global temperature changes. The MODIS MYD11A2 product is the most common coarse-resolution product used for this purpose. An updated MODIS product (MYD21A2) and new VIIRS (VNP21A2) product have also recently become available. This study analyses eight-day, quality-controlled, LST imagery over Western Australia (WA) for the three products for an urban and a non-urban area for the years 2013, 2016, and 2019. An analysis of the data indicates that (i) the averaged daytime LST difference between the three products for Perth city over the three years was 1.32 °C, while at night it was 0.89 °C; (ii) the averaged daytime difference over the Kimberley region was 7.02 °C with a night average difference of 2.39 °C; and (iii) both the MYD21A2 and VNP21A2 products still appear to record anomalous monthly LST values, particularly in the humid Kimberley monsoonal months. The overall objective of the National Aeronautics and Space Administration (NASA) is to ensure that the LST values of the two satellite system products are comparable, so evidence of LST value differences will require further investigation, especially if the older product is to be replaced by the newer systems.

47 OTHER INSTRUMENTATION↗

How does urbanization affect CO 2 emissions of central heating systems in China? An assessment of natural gas transition policy based on nighttime light data

Understanding the different impacts of urbanization on sectorial carbon dioxide (CO 2 ) emissions at different spatial scales is of great importance for the evaluation of energy transition policies and reduction of environmental inequality. However, how urbanization affects the CO 2 emissions of central heating systems at high spatial resolution in China has not been fully studied before. Based on satellite-observed NPP-VIIRS nighttime light (NTL) data, we develop a 5 km x 5 km annual CO 2 emission inventory for coal boilers, thermal power plants (TPPs), and natural gas boilers in China's central heating systems for the period 2012-2017 by using the geographical and temporally weighted regression (GTWR) model. It is observed that nonurban areas generated 2-4 times the CO 2 emissions of coal boilers in urban areas. The largest increments of CO 2 emissions of gas boilers are observed in urban areas of the eastern (6.80 times) and central regions (2.86 times) in 2013-2014, due to the clean heating policy in the "2 + 26" cities in China. The effects of urbanization on CO 2 emissions from natural gas boilers are approximately 2-3 times those of coal boilers, and the differences are largest in western cities with only minor differences in northeastern cities. Overall, our results will aid in designing low-carbon development goals and provide micro-level information on central heating facilities in urbanized and less developed regions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Seasonal Variability in Local Carbon Dioxide Biomass Burning Sources Over Central and Eastern US Using Airborne In Situ Enhancement Ratios

We present observations of local enhancements in carbon dioxide (CO 2 ) from local emissions sources over three eastern US regions during four deployments of the Atmospheric Carbon Transport-America (ACT-America) campaign between summer 2016 and spring 2018. Local CO 2 emissions were characterized by carbon monoxide (CO) to CO 2 enhancement ratios (i.e., ΔCO/ΔCO 2 ) in air mass mixing observed during aircraft transects within the planetary boundary layer. By analyzing regional-scale variability of CO 2 enhancements as a function of ΔCO/ΔCO 2 enhancement ratios, observed relative contributions to CO 2 emissions were separated into fossil fuel and biomass burning (BB) regimes across regions and seasons. CO 2 emission contributions attributed to biomass burning (ΔCO/ΔCO 2 > 4%) were negligible during summer and fall in all regions but climbed to ∼9%–11% of observed combustion contributions in the South during winter and spring. Relative CO 2 fire emission trends matched observed winter and spring BB contributions, but conflictingly predicted similar levels of BB during the fall. Satellite fire data from MODIS and VIIRS suggested the use of higher spatial resolution fire data that might improve modeled BB emissions but were not able to explain the bulk of the discrepancy.

54 ENVIRONMENTAL SCIENCES↗

Sensitivity of Regional WRF‐Chem Air Quality and Weather Simulations to Biomass‐Burning Emission Data Sets: A Case Study of the Impact of Canadian Wildfire on the US°

This study focuses on the period from June 26 to 29, 2023, when record‐breaking Canadian wildfires severely impacted air quality in the Midwest United States. Using the Weather Research and Forecasting Model with Chemistry (WRF‐Chem) and four biomass‐burning data sets (Fire Inventory from NCAR version 1, Fire Inventory from NCAR version 2.5, Quick Fire Emissions Data set [QFED], and Regional ABI‐VIIRS Emission), we analyzed aerosol transport from Canada to the US and assessed the model's accuracy in predicting PM 2.5 , O 3 , CO and aerosol weather feedback. Model simulations were compared with ground‐based and remote sensing observations as well as field measurements from the Community Research on Climate and Urban Science (CROCUS) project. Our findings show that the movement of a low‐pressure system from the Great Lakes to the Atlantic, combined with the high‐pressure system over the Atlantic, caused the transport of aerosols from Canadian wildfires to the US. Results show WRF‐Chem significantly underestimated key atmospheric components: aerosol optical depth (AOD) by over 50%, PM 2.5 by 65%–90% and peak O 3 concentrations by 50%–55% across four biomass burning data sets. Additionally, CO and NO 2 concentrations were underpredicted. The substantial underestimation of PM 2.5 led to an overestimation of temperature by up to 3.6 °C primarily due to excessive downward shortwave radiation, which resulted from the underestimation of direct aerosol effects and an increase in sensible heat flux. Among the biomass‐burning data sets, QFED produced the most accurate AOD and PM 2.5 predictions due to improved wildfire emission estimates, leading to a 1.0 to 1.5 °C reduction in temperature overestimation during the daytime. These findings underscore the need for improving wildfire emission estimates for trace gases and aerosols to enhance air quality and weather feedback predictions.

WRF-chem model↗

CubeSat constellations provide enhanced crop phenology and digital agricultural insights using daily leaf area index retrievals

Satellite remote sensing has great potential to deliver on the promise of a data-driven agricultural revolution, with emerging space-based platforms providing spatiotemporal insights into precision-level attributes such as crop water use, vegetation health and condition and crop response to management practices. Using a harmonized collection of high-resolution Planet CubeSat, Sentinel-2, Landsat-8 and additional coarser resolution imagery from MODIS and VIIRS, we exploit a multi-satellite data fusion and machine learning approach to deliver a radiometrically calibrated and gap-filled time-series of daily leaf area index (LAI) at an unprecedented spatial resolution of 3 m. The insights available from such high-resolution CubeSat-based LAI data are demonstrated through tracking the growth cycle of a maize crop and identifying observable within-field spatial and temporal variations across key phenological stages. Daily LAI retrievals peaked at the tasseling stage, demonstrating their value for fertilizer and irrigation scheduling. An evaluation of satellite-based retrievals against field-measured LAI data collected from both rain-fed and irrigated fields shows high correlation and captures the spatiotemporal development of intra- and inter-field variations. Novel agricultural insights related to individual vegetative and reproductive growth stages were obtained, showcasing the capacity for new high-resolution CubeSat platforms to deliver actionable intelligence for precision agricultural and related applications.

54 ENVIRONMENTAL SCIENCES↗

A Multi-Analysis Approach for Estimating Regional Health Impacts from the 2017 Northern California Wildfires

In the evening of October 8 and early hours of October 9, 2017, high winds in Northern California downed trees and power lines, igniting some of the most devastating wildfires the state had seen, and within hours unhealthy air quality impacted millions of people. We simulated these air quality conditions using fire detection information from the MODIS, VIIRS, and GOES-16 ABI satellite instruments, and applying a set of three WRF–CMAQ simulations, one data fusion, and three machine learning methods. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ air quality modeling system. Interestingly, this approach did not necessarily improve results compared to the baseline case, which used a default time profile. However, this approach was key to simulating the initial 12-hr explosive fire activity and smoke impacts. The WRF-CMAQ simulations compared well with observational data for the October 8-15 time period and tended to overestimate concentrations October 16-20. To improve these results, we applied three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across the WRF-CMAQ, data fusion, and machine learning datasets, the best Pearson correlation was 0.5. The data fusion and machine learning results were biased low and WRF-CMAQ results were biased high. Finally, we applied the optimized PM2.5 exposure estimate in a short-term exposure-response function. Total estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to wildland fire smoke.

O'Neill, Susan↗

On the Use of Satellite Nightlights for Power Outages Prediction

Hurricanes are a dominant disaster in the Caribbean, always causing serious power outages throughout the islands. Hurricane Maria was a prime example, causing unimaginable destruction of the power infrastructure of Puerto Rico (PR). Consequently, one month after the hurricane landfall, approximately 80% of the population was still without power. After an event of such massive destruction, the electric power restoration process progresses very slowly. This timeline can be improved using power outage (PO) forecast models that help identify the vulnerable places before the hurricane landfall. Generally, these models are trained with historical power outages records, associated data on weather conditions, and additional information about the natural and built environments. However, PO records are often difficult to acquire, and, in many instances, the power utility companies may not record them. This study utilizes a satellite-based Visible Infrared Imaging Radiometer Suite (VIIRS) night light data product as a surrogate for the power delivery to predict hurricane-induced PO in areas having limited to nonexistent historical data records. The processed satellite data is then used along with geographic variables, and simulated weather data to formulate machine learning-based algorithms to predict PO for future hurricane events. These models are applied and validated in the context of the PR catastrophic storm, Hurricane Maria.

54 ENVIRONMENTAL SCIENCES↗

SPATIOTEMPORAL TRACKING OF WIDE AREA POWER OUTAGE FROM NIGHT-TIME LIGHT IMAGERY

Monitoring progress of power restoration following extreme events is essential for situational awareness about spatio-temporal distribution of populations without power and to help with response efforts. Because of the proprietary nature of restoration data, and the difficulty in obtaining power outage data from utility companies in near real-time (e.g., during hurricane Maria (2017)), this project used satellite derived nighttime lights data from Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) to (i) monitor wide area power outage and (ii) estimate impacted customers over time to assist with restoration. A discussion of the methodology and its implementation during hurricanes Maria (2017) and Eta (2020) is presented in this paper. Future work will focus on calibrating estimated customers based on light intensity and density distribution, and generation of restoration profiles for emergency response.

Kar, Bandana↗

Project report for year 1

In the first year of the project, we developed a fire event tracking system using active fire detections recorded by the Visible Infrared Imaging Radiometer Suite (VIIRS). We employed this algorithm to track all large fires that occurred in California from 2013 to 2020, and created a retrospective Fire Event Data Suite (FEDS) that contains the vector shapes of fire perimeters, active fire fronts, and the locations and fire radiative powers for each fire pixel at 12-hour temporal resolution for each fire event. Using the 2020 Creek Fire as a test bed, we also delineated hourly fire perimeters by combining the FEDS data with the hourly rates of spread derived from Geostationary Operational Environmental Satellite (GOES) Advanced Baseline Imager (ABI) fire data. This dataset will form the basis of the near-real-time hourly fire emissions product that we plan to develop at years 2 and 3 of the project.

54 ENVIRONMENTAL SCIENCES↗