Visible Infrared Imaging Radiometer Suite Research Articles

A spatiotemporal comprehensive constraint interpolation method for missing values in monthly composite Suomi-NPP VIIRS Cloud Mask images

ABSTRACT The Earth’s nighttime light imaging data collected by the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) are severely lost in summer, hindering the further application of VIIRS DNB data. In this article, a spatiotemporal comprehensive constraint interpolation method (SCIM) that accounts for the relative stationarity of temporal nighttime light, spatial correlations and heterogeneity among pixels is designed. Meanwhile, the method considers the seasonal fluctuations in nighttime light radiation, introduces spatial autocorrelation coefficients and spatial heterogeneity coefficients, and solves the problem of missing VIIRS DNB data. First, 8 widely used temporal interpolation methods (TIM) are selected to perform temporal interpolation for missing image data. Second, the existing VIIRS monthly images are used to construct the constraint relationship between time and space, and the pixels that do not meet the constraint conditions in the TIM simulation image are removed. The inverse distance weight (IDW) method is applied to the removed null pixels to obtain the image simulated by the SCIM. Finally, the accuracy of the TIM and SCIM is evaluated. The results show that the accuracy of the SCIM-simulated image is better than that of the TIM-simulated image. The proposed approach improves the quality of the VIIRS DNB dataset.

Automatic identification and quantification of volcanic hotspots in Alaska using HotLINK: the hotspot learning and identification network

An increase in volcanic thermal emissions can indicate subsurface and surface processes that precede, or coincide with, volcanic eruptions. Space-borne infrared sensors can detect hotspots—defined here as localized volcanic thermal emissions—in near-real-time. However, automatic hotspot detection systems are needed to efficiently analyze the large quantities of data produced. While hotspots have been automatically detected for over 20 years with simple thresholding algorithms, new computer vision technologies, such as convolutional neural networks (CNNs), can enable improved detection capabilities. Here we introduce HotLINK: the Hotspot Learning and Identification Network, a CNN trained to detect hotspots with a dataset of −3,800 satellite-based, Visible Infrared Imaging Radiometer Suite (VIIRS) images from Mount Veniaminof and Mount Cleveland volcanoes, Alaska. We find that our model achieves an accuracy of 96% (F1-score 0.92) when evaluated on −1,700 unseen images from the same volcanoes, and 95% (F1-score 0.67) when evaluated on −3,000 images from six additional Alaska volcanoes (Augustine Volcano, Bogoslof Island, Okmok Caldera, Pavlof Volcano, Redoubt Volcano, Shishaldin Volcano). In comparison with an existing threshold-based hotspot detection algorithm, MIROVA (Coppola et al., Geological Society, London, Special Publications, 2016, 426, 181–205), our model detects 22% more hotspots and produces 12% fewer false positives. Additional testing on −700 labeled Moderate Resolution Imaging Spectroradiometer (MODIS) images from Mount Veniaminof demonstrates that our model is applicable to this sensor’s data as well, achieving an accuracy of 98% (F1-score 0.95). We apply HotLINK to 10 years of VIIRS data and 22 years of MODIS data for the eight aforementioned Alaska volcanoes and calculate the radiative power of detected hotspots. From these time series we find that HotLINK accurately characterizes background and eruptive periods, similar to MIROVA, but also detects more subtle warming signals, potentially related to volcanic unrest. We identify three advantages to our model over its predecessors: 1) the ability to detect more subtle volcanic hotspots and produce fewer false positives, especially in daytime images; 2) probabilistic predictions provide a measure of detection confidence; and 3) its transferability, i.e., the successful application to multiple sensors and multiple volcanoes without the need for threshold tuning, suggesting the potential for global application.

CNN-BiLSTM: A Novel Deep Learning Model for Near-Real-Time Daily Wildfire Spread Prediction

Wildfires significantly threaten ecosystems and human lives, necessitating effective prediction models for the management of this destructive phenomenon. This study integrates Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) modules to develop a novel deep learning model called CNN-BiLSTM for near-real-time wildfire spread prediction to capture spatial and temporal patterns. This study uses the Visible Infrared Imaging Radiometer Suite (VIIRS) active fire product and a wide range of environmental variables, including topography, land cover, temperature, NDVI, wind informaiton, precipitation, soil moisture, and runoff to train the CNN-BiLSTM model. A comprehensive exploration of parameter configurations and settings was conducted to optimize the model’s performance. The evaluation results and their comparison with benchmark models, such as a Long Short-Term Memory (LSTM) and CNN-LSTM models, demonstrate the effectiveness of the CNN-BiLSTM model with IoU of F1 Score of 0.58 and 0.73 for validation and training sets, respectively. This innovative approach offers a promising avenue for enhancing wildfire management efforts through its capacity for near-real-time prediction, marking a significant step forward in mitigating the impact of wildfires.

Performance of two semi-analytical algorithms in deriving water inherent optical properties in the Southern Ocean

Remotely sensed inherent optical properties (IOPs) are key proxies for synoptic mapping of primary production and carbon export in the global ocean. However, the IOPs inversion algorithms are scarcely evaluated in the Southern Ocean (SO) because of limited field observations. In this study, the performance of two widely used semi-analytical algorithms (SAAs), i.e., the quasi-analytical algorithm (QAA) and the generalized IOP model (GIOP), were evaluated using a compiled in situ bio-optical dataset in SO, as well as measurements from the Visible Infrared Imaging Radiometer Suite (VIIRS). Evaluations with in situ data show that QAA and GIOP have comparable performance in retrieving the total absorption coefficient (a(λ)), absorption coefficients of phytoplankton (aph(λ)), and that of detritus and colored dissolved organic matter (adg(λ)). Overall, it was found that remotely sensed a(λ) and aph(λ) by both SAAs agreed well with field measurements, with the mean absolute percentage difference (MAPD) of derived a(λ) and aph(λ) in the blue-green bands being ∼20% and ∼40%, respectively. However, derived adg(λ) by both SAAs were higher than the measured values at the lower end (adg(443) < ∼0.01 m-1), but lower at the higher end (adg(443) > ∼0.02 m-1), with MAPD of ∼60%. Results of this effort suggest confident products of a(λ) and aph(λ) from VIIRS in SO, but more dedicated efforts on the measurements and evaluation of adg(λ) in SO would be desired.

Evaluation of VIIRS Thermal Emissive Bands Long-Term Calibration Stability and Inter-Sensor Consistency Using Radiative Transfer Modeling

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12–M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites (S-NPP, NOAA-20, and NOAA-21) over eight months spanning from 18 March to 30 November 2023. The field of interest is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.102 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily VIIRS operational data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference observed in overlapping TEBs between VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. It is also found that the remnant scene temperature dependence of NOAA-21 versus NOAA-20/S-NPP M13 O-O BT difference after accounting for SRF difference is ~0.0033 K/K, an order of magnitude smaller than the corresponding rates in the direct BT comparisons between NOAA-21 and NOAA-20/S-NPP. Our study confirms the versatility and effectiveness of the RTM-based TEB quality evaluation method in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing a robust mechanism for assessing inter-sensor consistency. Moreover, for M12 operating as a shortwave infrared channel, it is found that the daytime O-B BT differences of S-NPP M12 exhibit greater seasonal variability compared to the nighttime data, which can be attributed to the idea that M12 radiance is affected by the reflected solar radiation during the daytime. Furthermore, in this study, we’ve also characterized the spatial distributions of inter-VIIRS BT differences, identifying variations among VIIRS M TEBs, as well as spatial discrepancies between the daytime and nighttime data.

Propagation of NO2 originated in intense fires in the Paraná River Delta analyzed from satellite observations

Wildfires in the Paraná River Delta started to be a common situation in the year 2008 and during the first years of the 2020́s decade: 2020, 2021, 2022. These fires affected natural areas, primarily the islands within the delta, situated in front of Rosario city (32.95 °S, 60.67 °W, 25 meters above sea level (m.a.s.l.)), Argentina. They mostly occur during the period between June and September each year and are more intense when a strong La Niña event is present. The aim of this study is to examine the association between the propagation of high intensity nitrogen dioxide (NO2) plumes and the significant fire events that occurred in the Paraná River Delta from 6 to 17 September 2022. Satellite data of NO2 obtained from the TROPOspheric Monitoring Instrument (TROPOMI) on board the European Space Agency (ESA) Sentinel-5 Precursor satellite, air mass forward trajectories performed with The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT), Fire Radiative Power (FRP) data provided by Visible Infrared Imaging Radiometer Suite (VIIRS) on board The National Oceanic and Atmospheric Administration - 20 (NOAA-20) and Suomi National Polar-orbiting Partnership (S-NPP) satellites, Aerosol Optical Depth (AOD) from VIIRS S-NPP and Smoke Injection Height from Multi-Angle Implementation of Atmospheric Correction (MAIAC) Thermal Technique were employed. The trajectories followed by the air masses coming from the major fire sources locations nearby Rosario city and its surrounding area, were analyzed at altitudes of 20, 500, 1000, 1500 and 2000 m above ground level (AGL) in order to evaluate the impact of the NO2 clouds present in the atmosphere and carried by the winds, at different sites of Rosario and its adjacent region. The NO2 plumes has the potential to pose a significant health risk for this region, inhabited by an approximate population of one and a half million individuals. This risk is contingent upon the altitude where the highest levels of NO2 in the tropospheric column are found. Tropospheric vertical column densities (VCDs) of NO2 in the range of 4.68×1014 to 1.66×1016 molecules/cm2 were determined. The results indicate a robust correlation between elevated tropospheric NO2 VCD values and intense fires, as characterized by their FRP. Additionally, high tropospheric NO2 VCDs were observed to impact cities and towns located in close proximity to the fires. In particular, the unusually high tropospheric NO2 VCDs determined for the towns of Fighiera and Pueblo Esther, situated along the coast and near the Paraná River Delta, are noteworthy. This is uncommon considering they are small towns in rural areas that typically have good air quality. The results regarding FRP, provided by VIIRS on board NOAA-20 and S-NPP satellites, indicate that the fire characterization of hotspots during the considered period had a mean value in the range of 20.17-21.47 MW. On specific days (12, 13 and 16 September 2022), the cumulative (total) daily values were very high, ranging from 2000 to 10000 MW. Furthermore, on 12 and 13 September 2022, the smoke aerosols did not reach significant altitudes in the atmosphere, remaining within the range of 0 to 1500 m AGL. During the fire period, AOD values close to 1 were observed within the fire plume. Notably, Puerto San Martín (0.27), San Lorenzo (0.22), and Ibarlucea (0.20) recorded higher average AOD values compared to other sites. The remaining sites generally exhibited lower average AOD values, with a slight distinction between rural locations (Pueblo Esther: 0.14 and Fighiera: 0.14) and the city center of Rosario (0.09).

VIIRS Version 2 Deep Blue Aerosol Products

AbstractNASA's Deep Blue aerosol project has developed global aerosol data records using consistent retrieval algorithms applied to various satellite sensors. The primary components of these data records are derived from the series of Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar‐orbiting Partnership (SNPP) and the National Oceanic and Atmospheric Administration or NOAA‐20+ satellites as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), among others. These instruments provide over 23 years of measurements with similar radiometric characteristics for aerosol retrievals. The algorithms used for the initial Version 1 SNPP VIIRS data set were based on the MODIS Collection 6.1 Deep Blue algorithm over land and Satellite Ocean Aerosol Retrieval (SOAR) algorithm over water. For VIIRS Version 2 data reprocessing, major updates have been made to the algorithm suite, including better accounting for effects of surface pressure, improved determination of surface reflectance, and the inclusion of fine‐mode aerosol optical models to better represent anthropogenic aerosols over land. Cross‐calibration gain factors are derived for the NOAA‐20 VIIRS measurements to be consistent with the SNPP VIIRS, which allows the use of a unified algorithm package for both instruments. Comparisons against AERONET observations indicate that the Version 2 AOD data from SNPP VIIRS are significantly better than the Version 1 counterpart over land and slightly degraded over water in exchange for better spatial coverage. The AOD data from SNPP and NOAA‐20 VIIRS are comparable, indicating that cross‐calibration enables the creation of consistent aerosol data records using the series of VIIRS sensors.

Systematically tracking the hourly progression of large wildfires using GOES satellite observations

Abstract. In the western United States, prolonged drought, a warming climate, and historical fuel buildup have contributed to larger and more intense wildfires as well as to longer fire seasons. As these costly wildfires become more common, new tools and methods are essential for improving our understanding of the evolution of fires and how extreme weather conditions, including heat waves, windstorms, droughts, and varying levels of active-fire suppression, influence fire spread. Here, we develop the Geostationary Operational Environmental Satellites (GOES)-Observed Fire Event Representation (GOFER) algorithm to derive the hourly fire progression of large wildfires and create a product of hourly fire perimeters, active-fire lines, and fire spread rates. Using GOES-East and GOES-West geostationary satellite detections of active fires, we test the GOFER algorithm on 28 large wildfires in California from 2019 to 2021. The GOFER algorithm includes parameter optimizations for defining the burned-to-unburned boundary and correcting for the parallax effect from elevated terrain. We evaluate GOFER perimeters using 12 h data from the Visible Infrared Imaging Radiometer Suite (VIIRS)-derived Fire Event Data Suite (FEDS) and final fire perimeters from the California's Fire and Resource Assessment Program (FRAP). Although the GOES imagery used to derive GOFER has a coarser resolution (2 km at the Equator), the final fire perimeters from GOFER correspond reasonably well to those obtained from FRAP, with a mean Intersection-over-Union (IoU) of 0.77, in comparison to 0.83 between FEDS and FRAP; the IoU indicates the area of overlap over the area of the union relative to the reference perimeters, in which 0 is no agreement and 1 is perfect agreement. GOFER fills a key temporal gap present in other fire tracking products that rely on low-Earth-orbit imagery, where perimeters are available at intervals of 12 h or longer or at ad hoc intervals from aircraft overflights. This is particularly relevant when a fire spreads rapidly, such as at maximum hourly spread rates of over 5 km h−1. Our GOFER algorithm for deriving the hourly fire progression using GOES can be applied to large wildfires across North and South America and reveals considerable variability in the rates of fire spread on diurnal timescales. The resulting GOFER product has a broad set of potential applications, including the development of predictive models for fire spread and the improvement of atmospheric transport models for surface smoke estimates. The resulting GOFER product has a broad set of potential applications, including the development of predictive models for fire spread and the improvement of atmospheric transport models for surface smoke estimates (https://doi.org/10.5281/zenodo.8327264, Liu et al., 2023).

Improving the quantification of fine particulates (PM2.5) concentrations in Malaysia using simplified and computationally efficient models

Air pollution assessment in urban and rural areas is really challenging due to high spatio-temporal variability of aerosols and pollutants and the uncertainties in measurements and modelling estimates. Nevertheless, accurate determination of the pollution sources and distribution of PM2.5 concentrations is especially important for source apportionment and mitigation strategies. This study provides estimates of PM2.5 concentrations across Malaysia in high spatial resolution, based on multi-satellite data and machine learning (ML) models, namely Random Forest (RF), Support Vector Regression (SVR) and extreme Gradient Boosting (XGBoost), also covering remote areas without measurement networks. The study aims to develop ML models that are simpler than previous works and demonstrate computational efficiency. Six sub-models were developed to represent different locations and seasons in Malaysia. Model 1 includes all data from 65 air-quality stations, Models 2 and 3 characterize urban/industrial and suburban sites, respectively, while Models 4 to 6 correspond to dry, wet, and inter-monsoon seasons, respectively. The RF technique exhibited slightly better performance compared to the XGBoost and SVR approaches. More specifically, for model 1, it exhibited a high correlation with a coefficient of determination (R2) of 0.64 and RMSE of 12.17 μg m−3, while similar results were obtained for models 3, model 4 and model 5. The lower performance (R2 = 0.16–0.94) observed in the wet and inter-monsoon seasons is due to fewer number of data used in model calibration. Integration of two AOD products from the Advanced Himawari Imager (AHI) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors together with gases pollutants from Sentinel 5P enabled seamless seasonal PM2.5 mapping over Malaysia, even for a short period of time. However, usage of data with insufficient information during the model training procedure, and lack of satellite data due to cloud contamination, can limit the PM2.5 prediction accuracy.

Satellite Hyperspectral Nighttime Light Observation and Identification with DESIS

The satellite imagery of nighttime lights (NTLs) has been studied to understand human activities, economic development, and more recently, the ecological impact of brighter night skies. The Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) offers perhaps the most advanced nighttime imaging capabilities to date, but its large pixel size and single band capture large-scale changes in NTL while missing granular but important details, such as lighting type and brightness. To better understand individual NTL sources in a region, the spectra of nighttime lights captured by the DLR Earth Sensing Imaging Spectrometer (DESIS) were extracted and compared against near-coincident VIIRS DNB imagery. The analysis shows that DESIS’s finer spatial and spectral resolutions can detect individual NTL locations and types beyond what is possible with the DNB. Extracted night light spectra, validated against ground truth measurements, demonstrate DESIS’s ability to accurately detect and identify narrow-band atomic emission lines that characterize the spectra of high-intensity discharge (HID) light sources and the broader spectral features associated with different light-emitting diode (LED) lights. These results suggest the possible application of using hyperspectral data from moderate-resolution sensors to identify lamp construction details, such as illumination source type and light quality in low-light contexts. NTL data from DESIS and other hyperspectral sensors may improve the scientific understanding of light pollution, lighting quality, and energy efficiency by identifying, evaluating, and mapping individual and small groups of light sources.

Revisiting the relationships between energy consumption, economic development and urban size: A global perspective using remote sensing data

Existing methods of measuring energy consumption require complex statistics and computing. A real-time and globally applicable approach for comparing energy consumption across different cities is still lacking. Additionally, the nonlinear relationships and varying thresholds of energy consumption in relation to economic activities and urbanization remain unconfirmed. This study aims to fill these gaps by utilizing Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data in 2015 and a top-down approach based on a multiple regression model to examine energy consumption in global cities employing a redefined urban boundary. It also explores the accurate relationship between energy consumption, population density (as a proxy of urbanization), and per capita gross domestic product (GDP) across different regions and urban sizes using generalized additive models and regression models. High-resolution gridded population and GDP datasets covering the entire planet are utilized for this purpose. The study also estimates the development potentiality. The study yields followings outcomes: Firstly, the top 30 cities with the highest per capita energy consumption account for over 0.66% of the total per capita energy consumption of all cities. Secondly, in East Asia (EA) and Southeast Asia (SEA), the per capita energy consumption decreases when per capita GDP reaches $40,000 and $75,000, respectively, while it remains stable in cities located in Western Europe (WE) and North America (NA) as per capita GDP increases. Thirdly, the per capita energy consumption declines with increasing urban population density until reaching 10,000 person/km2, 22,000 person/km2, and 4000 person/km2 in EA, SEA, and NA, respectively. Fourthly, in Central Asia (CA), megacities can save over 100 Mbtu/population when per capita GDP increases by $1000 compared to big cities. This pioneering study provides a comparable investigation of energy consumption at the global city level, exploring its relationship with urbanization and economy by employing a unified calculation standard. It will facilitate long-term energy-saving policies and urban planning strategies.

Air Quality and Health Impacts of Onshore Oil and Gas Flaring and Venting Activities Estimated Using Refined Satellite-Based Emissions.

Emissions from flaring and venting (FV) in oil and gas (O&G) production are difficult to quantify due to their intermittent activities and lack of adequate monitoring and reporting. Given their potentially significant contribution to total emissions from the O&G sector in the United States, we estimate emissions from FV using Visible Infrared Imaging Radiometer Suite satellite observations and state/local reported data on flared gas volume. These refined estimates are higher than those reported in the National Emission Inventory: by up to 15 times for fine particulate matter (PM2.5), two times for sulfur dioxides, and 22% higher for nitrogen oxides (NOx). Annual average contributions of FV to ozone (O3), NO2, and PM2.5 in the conterminous U.S. (CONUS) are less than 0.15%, but significant contributions of up to 60% are found in O&G fields with FV. FV contributions are higher in winter than in summer months for O3 and PM2.5; an inverse behavior is found for NO2. Nitrate aerosol contributions to PM2.5 are highest in the Denver basin whereas in the Permian and Bakken basins, sulfate and elemental carbon aerosols are the major contributors. Over four simulated months in 2016 for the entire CONUS, FV contributes 210 additional instances of exceedances to the daily maximum 8-hr average O3 and has negligible contributions to exceedance of NO2 and PM2.5, given the current form of the national ambient air quality standards. FV emissions are found to cause over $7.4 billion in health damages, 710 premature deaths, and 73,000 asthma exacerbations among children annually.

Comparison of change-based and shape-based data fusion methods in fine-resolution land surface phenology monitoring with Landsat and Sentinel-2 data

Fine-resolution land surface phenology (LSP) is urgently required for applications on agriculture management and vegetation-climate interaction, especially over heterogeneous areas, such as agricultural lands and fragmented forests. The critical challenge of fine-resolution LSP monitoring is how to reconstruct the spatiotemporal continuous vegetation index time series. To solve this problem, various data fusion methods have been devised; however, the comprehensive inter-comparison is lacking across different spatial heterogeneity, data quality, and vegetation types. We divide these methods into two main categories: the change-based methods fusing satellite observations with different spatiotemporal resolutions, and the shape-based methods fusing prior knowledge of shape models and satellite observations. We selected four methods to rebuilt two-band enhanced vegetation index (EVI2) series based on the harmonized Landsat and Sentinel-2 (HLS) data, including two change-based methods, namely the Spatial and temporal Adaptive Reflectance Fusion Model (STARFM), the Flexible Spatiotemporal DAta Fusion (FSDAF), and two shape-based methods, namely the Multiple-year Weighting Shape-Matching (MWSM), and the Spatiotemporal Shape-Matching Model (SSMM). Four phenological transition dates were extracted, evaluated with PhenoCam observations and the 500 m Visible Infrared Imaging Radiometer Suite (VIIRS) phenology product. The 30 m transition dates show more spatial details and reveal more apparent intra-class and inter-class phenology variation compared with 500 m product. The four transition dates of SSMM and FSDAF (R2>0.74, MAD<15 days) show better agreement with PhenoCam-derived dates. The performance difference between fusion methods over various application scenarios are then analyzed. Fusion results are more robust when temporal frequency is higher than 15 observations per year. The shape-based methods are less sensitive to temporal sampling irregularity than change-based methods. Both change-based methods and shape-based methods cannot perform well when the region is heterogeneous. Among different vegetation types, SSMM-like methods have the highest overall accuracy. The findings in this paper can provide references for regional and global fine-resolution phenology monitoring.

Investigating the performance of SDGSAT-1/GIU and NPP/VIIRS nighttime light data in representing nighttime vitality and its relationship with the built environment: A comparative study in Shanghai, China

In recent years, a substantial body of research has demonstrated the close relationship between the built environment and urban nighttime vitality. While scholars have established slightly varied evaluation systems for the built environment, most have utilized the open-source nighttime light (NTL) data (2012 ∼ ) from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite to characterize nighttime vitality. However, with the release of the high spatial resolution NTL data from Glimmer Imager for Urbanization (GIU) sensor on the Sustainable Development Goals Satellite 1 (SDGSAT-1) by the Chinese Academy of Sciences (CAS), more options are now available for indicating nighttime vitality. Yet, no studies have so far measured the representational capability of this new NTL data. In this study, we selected Shanghai, one of the most urbanized cities in Asia, as our research area. We utilized both GIU NTL data and VIIRS NTL data to represent nighttime vitality and collected multidimensional geospatial big data as indicators of the built environment. Various regression models were employed to explore the differences in explanatory power when using different NTL data. To obtain more reliable conclusions, we have also divided the study area according to urban functional zone categories and conducted experiments on both the overall scale (entire study area) and local scale (different urban functional zones). The results showed that regression models using GIU NTL data had higher R2 values both at overall scale(0.529 > 0.268) and local scale (0.704 > 0.641, 0.701 > 0.408, 0.537 > 0.512, 0.674 > 0.589), indicating stronger explanatory power of GIU NTL data. Furthermore, we found that the use of GIU NTL data revealed an amplification effect, where the impact of certain representative built environment factors (such as road density, building height, and vegetation coverage) on nighttime vitality was magnified, demonstrating more pronounced spatial heterogeneity. Our study investigated the performance of the new open-source SDGSAT1/GIU NTL data in representing nighttime vitality and shows its potential application in research on the relationship between the built environment and nighttime vitality. Additionally, our study also reveals the differences in the impact of the built environment on nighttime vitality across different urban functional zones. All these results will provide more valuable references for city managers aspiring to enhance the nighttime vitality and help them to make more rational planning plans.

Spatiotemporal Protein Variations Based on VIIRS-Derived Regional Protein Algorithm in the Northern East China Sea

Over the past two decades, the environmental characteristics of the northern East China Sea (NECS) that make it a crucial spawning ground for commercially significant species have faced substantial impacts due to climate change. Protein (PRT) within phytoplankton, serving as a nitrogen-rich food for organisms of higher trophic levels, is a sensitive indicator to environmental shifts. This study aims to develop a regional PRT algorithm to characterize spatial and temporal variations in the NECS from 2012 to 2022. Employing switching chlorophyll-a and particulate organic nitrogen algorithms, the developed regional PRT algorithm demonstrates enhanced accuracy. Satellite-estimated PRT concentrations, utilizing data from the Visible Infrared Imaging Radiometer Suite (VIIRS), generally align with the 1:1 line when compared to in situ data. Seasonal patterns and spatial distributions of PRT in both the western and eastern parts of the NECS from 2012 to 2022 were discerned, revealing notable differences in the spatial distribution and major controlling factors between these two areas. In conclusion, the regional PRT algorithm significantly improves estimation precision, advancing our understanding of PRT dynamics in the NECS concerning PRT concentration and environmental changes. This research underscores the importance of tailored algorithms in elucidating the intricate relationships between environmental variables and PRT variations in the NECS.

Light-Pollution-Monitoring Method for Selected Environmental and Social Elements

Light pollution significantly interferes with animal and human life and should, therefore, be included in the factors that threaten ecosystems. The main aim of this research is to develop a methodology for monitoring environmental and social elements subjected to light pollution in anthropogenic areas. This research is based on yearly and monthly photographs acquired from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (Suomi NPP) satellite; land cover data from the CORINE Land Cover (CLC) program; and environmental data from the European Environment Agency (EEA) and the World Database on Protected Areas (WDPA). The processing of input data for further analyses, the testing of the methodology and the interpretation of the final results were performed in GIS-type software (ArcGIS Pro). Light pollution in the investigated area was analyzed with the use of maps generated for the years 2014 and 2019. The environmental and social elements were spatially identified in five light pollution classes. The research results demonstrate that the proposed methodology allows for the identification of environmental and social elements that emit light, as well as those that are subjected to light pollution. The methodology used in this work allows us to observe changes resulting from light pollution (decreasing or increasing the intensity). Owing to the use of publicly available data, the methodology can be applied to light pollution monitoring as part of spatial planning in anthropogenic areas. The proposed methodology makes it possible to cover the area exposed to light pollution and to observe (almost online) the environmental and social changes resulting from reductions in light emitted by anthropogenic areas.

Transitioning from MODIS to VIIRS Global Water Reservoir Product

Reservoirs play a crucial role in regulating water availability and enhancing water security. Here, we develop NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) based Global Water Reservoir (GWR) product, consisting of measurements of reservoir area, elevation, storage, evaporation rate, and evaporation loss for 164 large global reservoirs. The dataset is available at 8-day and monthly temporal resolutions. Since the Moderate Resolution Imaging Spectroradiometer (MODIS) is close to the end of its life, we further evaluated the consistency between MODIS and VIIRS-based GWR to ensure continuity to the 20+ year MODIS GWR product. Independent assessment of VIIRS reservoir storage (8-day) retrievals against in-situ measurements shows an average of R2 = 0.84, RMSE = 0.47 km3, and NRMSE = 16.45%. The evaporation rate has an average of R2 = 0.56, RMSE = 1.32 mm/day, and NRMSE = 28.14%. Furthermore, results show good consistency (R2 ≥ 0.90) between the VIIRS and MODIS-based product components, confirming that long-term data continuity can be achieved. This dataset can provide valuable insights for long-term trend analysis, hydrological modeling, and understanding hydroclimatic extremes in the context of reservoirs.

Assessment and characteristics of S-NPP VIIRS Deep Blue and Dark Target aerosol properties under clean, polluted and fire scenarios over the Amazon

The present study carries out the systematic performance evaluation of aerosol optical depth (AOD) products retrieved using Visible Infrared Imaging Radiometer Suite (VIIRS) Deep Blue (DB) and Dark Target (DT) onboard Suomi National Polar-orbiting Partnership (S-NPP) satellite over the Amazon Basin. Characteristics and uncertainty were evaluated under distinct air pollution scenarios such as a clean background in the wet season, polluted conditions in the dry season with biomass burning emissions and peak burning season with higher fire activity. VIIRS retrievals were also analyzed under aerosol loading, particle size and surface vegetation coverage against the Aerosol Robotic Network (AERONET) measurements at 9 sites in 2012–2022. VIIRS DB showed good accuracy, with 78% of AOD matchups falling within the expected error, 84% in the wet season, and 71% in the dry and burning seasons. In contrast, VIIRS DT indicated poor accuracy (64%) and trends overestimate the AOD in all air pollution scenarios. Both algorithms were sensitive to AERONET sites with high elevation and dark vegetated coverage characteristics. VIIRS DB and DT systematically overestimated AERONET AOD as increased aerosol loading. DB trends underestimate aerosol under background conditions and overestimate with coarse and fine particle predominance. Additionally, both algorithms indicated poor accuracy under forest type with a large positive bias. VIIRS DB demonstrated the highest accuracy in the presence of aerosol loading in sites characterized by mixed land cover type. This was observed in both coarse and fine mode scenarios for grassland. VIIRS DT demonstrated satisfactory accuracy under background conditions and dominance of coarse particles within grassland land cover type. For mixed land cover, satisfactory accuracy was found under intermediate aerosol loading conditions. DB algorithm showed greater uncertainty associated with the coarse particle aerosol for the full period and all polluted scenarios. Overall, VIIRS DT accuracy was more sensitive to varying air pollution scenarios.

Thermal infrared observations of a western United States biomass burning aerosol plume

Abstract. Biomass burning smoke particles, due to their submicron particle size in relation to the average thermal infrared (TIR) wavelength, theoretically have negligible signals at the TIR channels. However, nearly instantaneous longwave (LW) signatures of thick smoke plumes can be frequently observed at the TIR channels from remotely sensed data, including at 10.6 µm (IR window), as well as in water-vapor-sensitive wavelengths at 7.3, 6.8, and 6.3 µm (e.g., lower, middle, and upper troposphere). We systematically evaluated multiple hypotheses as to causal factors of these IR signatures of biomass burning smoke using a combination of data from the Aqua MODerate resolution Imaging Spectroradiometer (MODIS), Aqua Cloud and the Earth Radiant Energy System (CERES), Geostationary Operational Environmental Satellite 16/17 (GOES-16/17) Advanced Baseline Imager, and Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) and Cross-track Infrared Sounder (CrIS). The largely clear transmission of light through wildfire smoke in the near infrared indicates that coarse or giant ash particles are unlikely to be the dominant cause. Rather, clear signals in water vapor and TIR channels suggest that both co-transported water vapor injected to the middle to upper troposphere and surface cooling by the reduction of surface radiation by the plume are more significant, with the surface cooling effect of smoke aloft being the most dominant. Giving consideration of the smoke impacts on TIR and longwave, CERES indicates that large wildfire aerosol plumes are more radiatively neutral. Further, this smoke-induced TIR signal may be used to map very optically thick smoke plumes, where traditional aerosol retrieval methods have difficulties.

The Assessment of Industrial Agglomeration in China Based on NPP-VIIRS Nighttime Light Imagery and POI Data

Industrial agglomeration, as a typical aspect of industrial structures, significantly influences policy development, economic growth, and regional employment. Due to the collection limitations of gross domestic product (GDP) data, the traditional assessment of industrial agglomeration usually focused on a specific field or region. To better measure industrial agglomeration, we need a new proxy to estimate GDP data for different industries. Currently, nighttime light (NTL) remote sensing data are widely used to estimate GDP at diverse scales. However, since the light intensity from each industry is mixed, NTL data are being adopted less to estimate different industries’ GDP. To address this, we selected an optimized model from the Gaussian process regression model and random forest model to combine Suomi National Polar-Orbiting Partnership—Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data and points-of-interest (POI) data, and successfully estimated the GDP of eight major industries in China for 2018 with an accuracy (R2) higher than 0.80. By employing the location quotient to measure industrial agglomeration, we found that a dominated industry had an obvious spatial heterogeneity. The central and eastern regions showed a developmental focus on industry and retail as local strengths. Conversely, many western cities emphasized construction and transportation. First-tier cities prioritized high-value industries like finance and estate, while cities rich in tourism resources aimed to enhance their lodging and catering industries. Generally, our proposed method can effectively measure the detailed industry agglomeration and can enhance future urban economic planning.