Visible Infrared Imaging Radiometer Suite Research Articles

Outdoor artificial light at night and risk of conversion from mild cognitive impairment to dementia

Abstract Background A few studies have suggested that exposure to lighting during night hours, i.e. light at night (LAN), may increase the risk of dementia. In this study, we aimed to evaluate the association between exposure to outdoor artificial LAN and risk of conversion to dementia in an Italian cohort of subjects with mild cognitive impairment (MCI). Methods We recruited subjects with a diagnosis of MCI at the Cognitive Neurology Clinic of Modena Hospital in the period 2008-2014, and we followed these subjects up to 2021 for conversion to dementia. We collected their residential history and we assessed LAN exposure at subjects’ residences using satellite imagery data available from the Visible Infrared Imaging Radiometer Suite (VIIRS) for the period 2014-2022. Using a Cox-proportional hazards model adjusted for relevant confounders, we computed the hazard ratio (HR) of dementia with 95% confidence interval (CI) according to increasing LAN exposure through linear, categorical, and non-linear restricted-cubic spline models. Results Out of 53 recruited subjects, 34 converted to dementia of any type including 26 Alzheimer’s dementia. In linear regression analysis, LAN exposure was positively associated with dementia conversion (HR 1.03, 95% CI 1.00-1.06 for 1-unit increase). Using as reference the lowest tertile, subjects at both intermediate and highest tertiles of LAN exposure showed increased risk of dementia conversion (HRs 2.26, 95% CI 0.88-5.85, and 2.89, 95% CI 1.10-7.58). In spline regression analysis, the risk linearly increased up to a LAN exposure of 30 nW/cm2/sr, above which a plateau seemed to be reached. Results were almost confirmed when limited to conversion to Alzheimer’s dementia, except for an almost linear relation. Conclusions Our findings suggest that exposure to outdoor artificial LAN may increase conversion from MCI to any type of dementia, especially above 30 nW/cm2/sr, while such relation appears to be almost linear for Alzheimer’s dementia. Key messages • Light at night exposure above 30 nW/cm2/sr was associated with risk of conversion to dementia. • Light at night showed almost linear association with risk of conversion to Alzheimer’s dementia.

Three-dimensional distribution of aerosols of multiple types at daily scale using TROPOMI spaceborne observations

We present new satellite-based daily observations of the 3D distribution of aerosols from most common types and origins, both over land and ocean, derived from TROpospheric Ozone Monitoring Instrument (TROPOMI) measurements. This is done using the so-called AEROS5P (AEROsol Sentinel 5 Precursor) approach which retrieves vertical profiles of aerosol extinction from passive measurements of high-resolution spectral reflectance in the visible and near infrared for cloud-free conditions. Previous work demonstrated the potential of this approach, but it was initially applicable only to biomass burning aerosols with limited spatial coverage over ocean. The new methodology presented here generalizes the method for the most common aerosol types and improves spatial coverage. We take advantage of an operational aerosol type product derived from Visible Infrared Imaging Radiometer Suite (VIIRS) observations, combined with those from TROPOMI, to choose a priori aerosol set of intensive properties (particle size and refractive index). AEROS5P shows good agreement in aerosol optical depth, when compared to observations from AErosol RObotic NETwork and other widely used satellite measurements. Additionally, the vertical distribution of aerosol plumes of different types derived from AEROS5P agrees well with lidar coincident observations from the Advanced Topographic Laser Altimeter System spaceborne sensor and ground-based measurements. We utilize AEROS5P to analyze the three-dimensional distribution of aerosols for four different scenarios. It traces the three-dimensional pathways of smoke from Canadian wildfires reaching the Eastern USA in July 2023, desert dust from the Namibian deserts over the southeast Atlantic Ocean confined near the surface during the same month, fine aerosol pollution over Western Europe during June 2019, and an extreme dust event mixed with fine aerosol pollution over Northern China in March 2021. These observations provide valuable insights into aerosol transport patterns, surface-level air quality impacts, and can potentially be used to assess the radiative effects at various atmospheric layers.

Development of VIIRS-OLCI chlorophyll-a product for the coastal estuaries

Coastal waters require monitoring of chlorophyll-a concentration (Chl-a) in a wide range of Chl-a from a few mg/m3 to hundreds of mg/m3, which is of interest to the fisheries industry, evaluation of climate change effects, ecological modeling and detection of Harmful Algal Blooms (HABs). Monitoring can be carried out from the Visible Infrared Imaging Radiometer Suite (VIIRS) and Ocean and Land Colour Instrument (OLCI) Ocean Color (OC) satellite sensors, which are currently on orbit and are expected to be the main operational OC sensors at least for the next decade. A Neural Network (NN) algorithm, which uses VIIRS M3-M5 reflectance bands and an I1 imaging band, was developed to estimate Chl-a in the Chesapeake Bay, for the whole range of Chl-a from clear waters in the Lower Bay to extreme bloom conditions in the Upper Bay and the Potomac River, where Chl-a can be used for bloom detection. The NN algorithm demonstrated a significant improvement in the Chl-a retrieval capabilities in comparison with other algorithms, which utilize only reflectance bands. OLCI NIR/red 709/665 nm bands red edge 2010 algorithm denoted as RE10 was also explored with several atmospheric corrections from EUMETSAT, NOAA and NASA. Good consistency between the two types of algorithms is shown for the bloom conditions and the whole range of waters in the Chesapeake Bay (with RE10 switch to OC4 for lower Chl-a) and these algorithms are recommended for the combined VIIRS-OLCI product for the estimation of Chl-a and bloom monitoring. The algorithms were expanded to the waters in Long Island Sound, demonstrating good performance.

Spatiotemporal Analysis of Open Biomass Burning in Guangxi Province, China, from 2012 to 2023 Based on VIIRS

Open biomass burning has significant adverse effects on regional air quality, climate change, and human health. Extensive open biomass burning is detected in most regions of China, and capturing the characteristics of open biomass burning and understanding its influencing factors are important prerequisites for regulating open biomass burning. The characteristics of open biomass burning have been widely investigated at the national scale, with regional studies often focusing on northeast China, but few studies have examined regional discrepancies in spatiotemporal variations over a long timescale in Guangxi province. In this study, we used the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG), combined with land cover data and high-resolution remote sensing images, to extract open biomass burning (crop residue burning and forest fire) fire points in Guangxi province from 2012 to 2023. We explored the spatial density distribution and temporal variation of open biomass burning using spatial analysis methods and statistical methods, respectively. Furthermore, we analyzed the driving forces of open biomass burning in Guangxi province from natural (topography, climate, and plant schedule), policy, and social (crop production and cultural customs) perspectives. The results show that open biomass burning is concentrated in the central, eastern, and southern parts of the study area, where there are frequent agricultural activities and abundant forests. At the city level, the highest numbers of fire points were found in Baise, Yulin, Wuzhou, and Nanning. The open biomass burning fire points exhibited large annual variation, with high levels from 2013 to 2015 and a remarkable decrease from 2016 to 2020 under strict control measures; however, inconsistent enforcement led to a significant rebound in fire points from 2021 to 2023. Forest fires are the predominant type of open biomass burning in the region, with forest fires and crop residue burning accounting for 76.82% and 23.18% of the total, respectively. The peak period for crop residue burning occurs in the winter, influenced mainly by topography, planting schedules, crop production, and policies, while forest fires predominantly occur in the winter and spring, primarily influenced by topography, climate, and cultural customs. The results indicate that identifying the driving forces behind spatiotemporal variations is essential for the effective management of open biomass burning.

Monitoring the Amazon River plume from satellite observations

ABSTRACT Most of our knowledge about the Amazon River plume is limited from the field experiments or modeling due to various challenges for satellite remote sensing, i.e., constant cloud coverage, atmospheric correction, and difficulties to differentiate the Amazon River plume from normal Atlantic Ocean water. In this study, observations of ocean properties from the Visible Infrared Imaging Radiometer Suite (VIIRS) and sea surface salinity (SSS) from the Soil Moisture Active Passive (SMAP) missions in 2021 are used as an example to demonstrate the capability to monitor the Amazon River plume on daily basis and characterize the change of the ocean environment driven by the offshoots and migration of the plume in the western tropical Atlantic Ocean. The start, development, evolvement, dispersion, and dissipation of the Amazon River plume in 2021 in the western tropical Atlantic Ocean were effectively monitored. The extensive influence of the Amazon River runoff mostly occurred between June and November. The water diffuse attenuation coefficient at 490 nm (K d (490)) and SSS were significantly impacted by the Amazon River plume in the vast region of the western tropical Atlantic Ocean. The routes of the Amazon River plume that flowed into the Atlantic Ocean were highly variable. High turbidity, low salinity plume water with K d (490) reaching ~1.0 m−1 and SSS ~25 practical salinity unit (psu) was found in a region over 500 km from the Amazon River Estuary. The comparison of the Amazon River plume in 2020 and 2022 further shows the complication of the driving forcing and inter-annual variability of the Amazon River plume. The combination of gap-free satellite ocean color and SSS observations provided a new tool to monitor the short-term variability of the Amazon River plume and assess its seasonal and interannual impact on the ocean environment in the western tropical Atlantic Ocean.

Spatial and Temporal Change Analysis of Urban Built-Up Area via Nighttime Lighting Data—A Case Study with Yunnan and Guizhou Provinces

As urbanization accelerates, characteristics of urban spatial expansion play a significant role in the future utilization of land resources, the protection of the ecological environment, and the coordinated development of population and land. In this study, Yunnan and Guizhou provinces were selected as the study area, and the 2013–2021 National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light (NTL) data were utilized for spatial and temporal change analysis of urban built-up areas. Firstly, the built-up areas in Yunnan and Guizhou provinces were extracted through ENUI (Enhanced Nighttime Lighting Urban Index) indices, and then the urban expansion speed and urban center of gravity migration were constructed and used to explore and analyze the spatial and temporal change and expansion characteristics of built-up areas in Yunnan and Guizhou provinces. The results showed the following. (1) Due to the complementarity between data types, such as NTL, EVI, NDBI, and NDWI, ENUI has better performance in expressing urban characteristics. (2) Influenced by national and local policies, such as “One Belt, One Road”, transportation infrastructure construction, geographic location, the historical background, and other factors, the urban expansion rate of Yunnan and Guizhou provinces in general showed a continuous advancement from 2013 to 2021, and there were three years in which the expansion rate was positive. (3) The center of gravity migration distance of most cities in Guizhou Province shows a trend of increasing and then decreasing, while the center of gravity migration distance in Yunnan Province shows a trend of continuous decrease in general. From the perspective of migration direction, Guizhou Province has the largest number of migrations to the northeast, while Yunnan Province has the largest number of migrations to the southeast. (4) Influenced by policy, economy, population, geography, and other factors, urban compactness in Yunnan and Guizhou provinces continued to grow from 2013 to 2021. The results of this study can help us better understand urbanization in western China, reveal the urban expansion patterns and spatial characteristics of Yunnan and Guizhou provinces, and provide valuable references for development planning and policymaking in Yunnan and Guizhou provinces.

Extracting Wetlands in Coastal Louisiana from the Operational VIIRS and GOES-R Flood Products

Visible Infrared Imaging Radiometer Suite (VIIRS) and Advanced Baseline Imager (GOES-R ABI) flood products have been widely used by the National Weather Service (NWS) for river flood monitoring, and by the Federal Emergency Management Agency (FEMA) for rescue and relief efforts. Some water bodies, like wetlands, are detected as water but not marked as permanent or normal water, which may result in their misclassification as floodwaters by VIIRS and GOES-R flood products. These water bodies generally do not cause significant property damage or fatalities, but they can complicate the identification of truly hazardous floods. This study utilizes the severe Louisiana flood event caused by Hurricane Ida to demonstrate how to differentiate wetlands from real-hazard flooding. Since Hurricane Ida made landfall in 2021, and there was no major flood event in 2022, VIIRS and ABI flood data from 2021 and 2022 were selected. The difference in annual total flooding days between 2021 and 2022 was calculated and combined with long-time flood frequency to distinguish non-hazard floodwaters due to wetlands identified from real-hazard floods caused by the hurricane. The results were compared with the wetlands from the change detection analysis. The confusion matrix analysis indicated an accuracy of 91.58%, precision of 89.97%, and F1-score of 76.63% for the VIIRS flood products. For the GOES-R ABI flood products, the confusion matrix analysis yielded an accuracy of 86.88%, precision of 97.49%, and F1-score of 75.21%. The accuracy and F1-score values for the GOES-R ABI flood products are slightly lower than those for the VIIRS flood products, possibly due to their lower spatial resolution, but still within a feasible range.

Estimation of nighttime PM2.5 concentrations over Seoul using Suomi NPP/VIIRS Day/Night Band

With rapid economic development and urban growth, Seoul experiences severe air pollution due to fine particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5), which is detrimental to human health. Although recent studies have extensively focused on estimating daytime PM2.5 concentrations using various types of satellite data, there remains a significant lack of research on nighttime PM2.5 estimations. This study estimated nighttime PM2.5 in Seoul from December 2018 to November 2019 using multiple linear regression (MLR) and random forest (RF) models. These models, which incorporated data on radiance, moon illumination fraction, and terrain height from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) on board the Suomi National Polar-orbiting Partnership satellite covering all moon phases, also utilized meteorological data from the ERA5 reanalysis by the European Centre for Medium-Range Weather Forecasts (ECMWF). To address multicollinearity, seasonal models were developed using forward stepwise regression and variance inflation factor analysis. DNB radiance analysis indicates that the high intensity of artificial light sources in Seoul significantly reduces the impact of moonlight, leading to notable changes in the DNB radiation associated with PM2.5 concentrations. Consequently, this study estimated nighttime PM2.5 over Seoul across all moon phases. These estimates were then validated through 10-fold cross-validation. The RF model exhibited superior accuracy, with a coefficient of determination (R2) of 0.65–0.90, compared to MLR, with R2 of 0.15–0.50, reflecting seasonal fluctuations in the model performance. The developed models can be applied to estimate reliable nighttime PM2.5 concentrations in megacities with strong artificial light sources, utilizing a comprehensive dataset from satellite observations for all moon phases. Additionally, our findings can serve as scientific data for establishing environmental policies by providing valuable insights into understanding air pollution primarily caused by PM2.5.

Remote sensing of turbid coastal and estuarine waters with VIIRS I (375 m) and M (750 m) bands

ABSTRACT The Visible Infrared Imaging Radiometer Suite (VIIRS) is a visible, near and shortwave, to thermal infrared multispectral scanning instrument operational on three polar orbiting satellites, Suomi-NPP, JPSS-1, and JPSS-2. In the present paper, the processing of VIIRS using ACOLITE is introduced, using the Dark Spectrum Fitting (DSF) algorithm for processing of the visible to shortwave infrared bands. ACOLITE now includes support for processing both the imaging (I) and moderate (M) resolution bands at 375 m and 750 m spatial resolution, respectively. In most conditions encountered in the present study, the SWIR bands (either I or M) are automatically selected by the DSF for performing the aerosol correction. The processing is evaluated for turbid water remote sensing via autonomous hyperspectral radiometry from four sites across coastal and estuarine waters: two sites in Belgium and one each in France and Argentina. Through analysis of hundreds of matchups between the satellite and in situ measurements, a generally good performance is found for both I and M bands, especially for bands with the largest water signal, i.e. bands between 490 and 670 nm, where on average relative differences of 10–15% were found. Reflectance biases are generally less than 0.01, with a negative sign in the green and red bands and a positive sign in the blue and NIR bands. Similar matchup results are found for the I and M red and NIR bands, with a slightly higher scatter for the NIR bands. An additional comparison with OCSSW/l2gen processing of the M band data is performed for various configurations. Overall, DSF performance is better in the visible bands, whereas l2gen outputs are more closely aligned with the in situ measurements in the NIR. On average, negative biases are found for all l2gen configurations, up to −0.02 in the blue bands. Using either the SWIR1 + 2 or SWIR1 + 3 bands for the aerosol correction gives the best performance for l2gen processing. For the three VIIRS instruments separately, the average spectral differences with in situ measurements are comparable, with the most important deviation occurring at the Suomi-NPP shortest blue bands, where DSF processing gives a larger positive bias, up to nearly 0.02. For these bands, results from l2gen correspond more closely across the three instruments – although with significant negative biases for all three sensors up to −0.02 – presumably due to the use of system vicarious calibration gains in that processor. An operational network of autonomous hyperspectral instruments provides validation data for any overpassing optical imaging satellite in its commissioning or operational phase and eliminates the need for spectral interpolation or band shifting. In the case of VIIRS specifically, the hyperspectral instruments provide adequate data for the validation of the 20, 40 and 80 nm wide bands. With three operational wide-swath instruments, which provide largely interoperable data, a high frequency of observations is available, especially for study areas at higher latitudes. The novel exploitation of the I bands is now possible, thanks to the free and open source availability of ACOLITE. The advantage of the higher resolution I band data, combined with multiple VIIRS overpasses per day, is demonstrated for mapping turbidity in nearshore regions with high spatial variabilty and for detecting under-resolved floating algae.

-temporal variability in fire events due to crop residue burning and their impact on atmospheric variables using ground and remote sensingdata in Punjab

We attempted monitoring and mapping of the active fire events in Punjab, during the wheat and rice harvesting period of 2017-2021 from the Visible Infrared Imaging Radiometer Suite (VIIRS) at 375 m aboard. The analysis showed that the highest fire counts were observed in the central region, followed by the south-west and thelowest in the north-east region of Punjab during all the years. Moreover, during the wheat season, highest fire counts were observed in 2019, being 2535, 11062 and 6212 in north-east, central and south-west regions, respectively. However, during rice, highest fire counts were observed in 2020 in the north-east being 2857 and in 2021 the central and south-west regions being 40960 and 30351 respectively. In line with the number of fire counts, the highest concentration of gases and particulate matter obtained from the Central Pollution Control Board (CPCB) was also observed in the central zone. During the wheat harvesting season, central zone experienced the highest concentration of PM2.5 and PM10 in May 2018 and that of SO2 and O3 in May 2019. Similarly, during the rice harvesting season, central zone also experienced the highest concentration of PM2.5 and PM10 during November 2017 and that of SO2 in 2018. However, the highest concentration of NO2 was observed in October 2018 and that of O3 in October 2020 in the central region. Analysis of the concentration of NO2 and SO2 obtained from the Soumi-NPP satellite also had similar results to CPCB. Such a high concentration of gases and particulate matter might be attributed to crop residue burning a significant positive correlation was observed between fire counts and concentration of particulate matter (PM2.5 and PM10). In view of the alarming deterioration of air quality, there is a dire need to check this practice in the region by providing incentives and viable alternatives to the farmers.

Early Mission Calibration Performance of NOAA-21 VIIRS Reflective Solar Bands

The Visible Infrared Imaging Radiometer Suite (VIIRS) is one of the key instruments on the recently launched NOAA-21 (previously known as JPSS-2) satellite. The VIIRS, like its predecessors on the SNPP and NOAA-20 satellites, provides daily global coverage in 22 spectral bands from 412 nm to 12 μm. The geometrically and radiometrically calibrated observations are the basis for many operational applications and scientific research studies. A total of 14 of the 22 bands are reflective solar bands (RSBs), covering photon wavelengths from 412 nm to 2.25 μm. The RSBs were radiometrically calibrated prelaunch and have been regularly calibrated on orbit through the onboard solar diffuser (SD) and scheduled lunar observations. The on-orbit SD’s reflectance change is determined by the onboard solar diffuser stability monitor (SDSM). We review the calibration algorithms and present the early mission performance of the NASA N21 VIIRS RSBs. Using the calibration data collected at both the yaw maneuver and regular times, we derive the screen transmittance functions. The visible and near-infrared bands’ radiometric gains have been stable, nearly independent of time, and so were the radiometric gains of the shortwave-infrared bands after the second mid-mission outgassing. Further, we assess the Earth-view striping observed in the immediate prior collection (Collection 2.0) and apply a previously developed algorithm to mitigate the striping. The N21 VIIRS RSB detector signal-to-noise ratios are all above the design values with large margins. Finally, the uncertainties of the retrieved Earth-view top-of-the-atmosphere spectral reflectance factors at the respective typical spectral radiance levels are estimated to be less than 1.5% for all the RSBs, except band M11 whose reflectance factor uncertainty is 2.2%.

Enhancing burned area monitoring with VIIRS dataset: A case study in Sub-Saharan Africa

Since 2001, the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board the Aqua and Terra platforms has made great strides in providing information on global burned areas (BA). However, the MODIS mission is nearing its end. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensors, presented as the MODIS Aqua heritage, could be an excellent alternative to ensure the temporal continuity of this information at a moderate resolution. This paper describes and evaluates the effectiveness of our developed hybrid algorithm, which utilizes VIIRS reflectance and active fire products on the Google Earth Engine platform, in producing efficient information about BA. The study investigates the algorithm's performance in sub-Saharan Africa as the region of interest in 2019, using biweekly outputs and a spatial resolution of 250 m. The algorithm encompasses several steps, including pre-processing individual scenes, creating cloud-free composites, generating binary reference data for burned and non-burned areas, conducting a supervised classification using random forest, and performing region shaping. The VIIRS-BA final product, which includes three confidence levels (low, moderate, and high) known as the uncertainty layer, is compared to four other burned area products. The validation is conducted against 27 reference sampling units from the Sentinel-2 Burned Area Reference Database dataset, allowing for a comprehensive uncertainty assessment across five various biomes. The VIIRS-BA product identified 5.1 million km2 of BA, which was significantly larger than other global coarse resolution BA products such as FireCCI51, FireCCIS310, and MCD64A1 and close to the fine resolution FireCCISFD20 with a difference of 7.3%. The differences were less significant in biomes such as “Tropical Savannas” and “Temperate Grasslands” which are characterized by persistent biomass burning. Based on a stratified random sampling, the validation results demonstrate varying levels of accuracy for the VIIRS-BA product across different confidence levels. The commission error (CE) ranges from 7.8% to 23.4%, while the omission error (OE) falls between 29.4% and 58.8%. Notably, there is a significant reduction in OE (ranging from 40.7% to 50.5%) compared to global BA products like FireCCI51, FireCCIS310, and MCD64A1. When compared to VIIRS-BA, the FireCCISFD20 regional product has a 37% better OE performance. While VIIRS-BA shows great potential in detecting fires that global products miss, the VIIRS-BA with low confidence level tends to overestimate BA in regions with high fire activity. To address this, future versions of the algorithm will integrate the updated VIIRS reflectance data alongside VIIRS active fire from the National Oceanic and Atmospheric Administration to reduce CE and improve understanding spatial patterns.

A First Extension of the Robust Satellite Technique RST-FLOOD to Sentinel-2 Data for the Mapping of Flooded Areas: The Case of the Emilia Romagna (Italy) 2023 Event

Extreme meteorological events hit our planet with increasing frequency, resulting in an ever-increasing number of natural disasters. Flash floods generated by intense and violent rains are among the most dangerous natural disasters that compromise crops and cause serious damage to infrastructure and human lives. In the case of such a kind of disastrous events, timely and accurate information about the location and extent of the affected areas can be crucial to better plan and implement recovery and containment interventions. Satellite systems may efficiently provide such information at different spatial/temporal resolutions. Several authors have developed satellite techniques to detect and map inundated areas using both Synthetic Aperture Radar (SAR) and a new generation of high-resolution optical data but with some accuracy limits, mostly due to the use of fixed thresholds to discriminate between the inundated and unaffected areas. In this paper, the RST-FLOOD fully automatic technique, which does not suffer from the aforementioned limitation, has been exported for the first time to the mid–high-spatial resolution (20 m) optical data provided by the Copernicus Sentinel-2 Multi-Spectral Instrument (MSI). The technique was originally designed for and successfully applied to Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite data at a mid–low spatial resolution (from 1000 to 375 m). The processing chain was implemented in a completely automatic mode within the Google Earth Engine (GEE) platform to study the recent strong flood event that occurred in May 2023 in Emilia Romagna (Italy). The outgoing results were compared with those obtained through the implementation of an existing independent optical-based technique and the products provided by the official Copernicus Emergency Management Service (CEMS), which is responsible for releasing information during crisis events. The comparisons carried out show that RST-FLOOD is a simple implementation technique able to retrieve more sensitive and effective information than the other optical-based methodology analyzed here and with an accuracy better than the one offered by the CEMS products with a significantly reduced delivery time.

Improved estimation of daily blue-sky snow shortwave albedo from MODIS data and reanalysis information

Snow albedo is a key geophysical parameter that controls the energy exchanges between the atmosphere and Earth's surfaces and has been widely utilized in climatic and environmental change studies. However, recent studies have demonstrated that current albedo satellite products still have large uncertainties in snow-covered areas. In this study, we estimated the blue-sky shortwave albedo of snow surfaces using the eXtreme Gradient Boosting (XGBoost) algorithm with Moderate Resolution Imaging Spectroradiometer (MODIS) top-of-atmosphere (TOA) reflectance values, ERA-5 land reanalysis snow parameters (e.g., snow cover, snow density and snow depth water equivalent) and in situ measurements. In the XGBoost model, the MODIS MCD43 albedo values were input as prior knowledge, and the random sample validation results showed that the R2 and root mean square error (RMSE) values of this model were approximately 0.953 and 0.044, respectively. The typical sites for independent validation were subjected to in situ measurements at the UPE_L, AWS5, and CA_ARB sites. Finally, the retrieved XGBoost albedo values were compared with the official NASA MODIS (MCD43, collection 6), the Global Land Surface Satellite (GLASS), and the National Oceanic and Atmospheric Administration (NOAA) Visible Infrared Imaging Radiometer Suite (VIIRS) SURFALB albedo products. The validation results indicated that the proposed approach achieved much greater accuracy (RMSE = 0.052, bias = 0.002) than did the corresponding official MODIS (RMSE = 0.087, bias = −0.033), GLASS (RMSE = 0.089, bias = −0.031) and VIIRS SURFALB albedo (RMSE = 0.100, bias = −0.032) products. The improved shortwave albedo captured the rapid temporal changes in surface snow conditions.

All weather land surface temperature estimation by combining thermal infrared and passive microwave radiometry: a study over India

ABSTRACT Land Surface Temperature (LST) plays a crucial role in water and energy cycle studies. However, clouds pose a significant challenge in obtaining continuous LST time series from Thermal Infrared (TIR) sensors. To overcome this challenge, this study leverages the potential of Passive Microwave Radiometry (PMR), which offers all-weather observation capabilities, albeit at a coarser spatial resolution to estimate all-weather LST over India. In this study, we trained a Random Forest (RF) model using clear sky LST from either Moderate Resolution Spectro Radiometer (MODIS) or Visible Infrared Imaging Radiometer Suite (VIIRS) and passive microwave observations from Advanced Microwave Scanning Radiometer-2 (AMSR2), enabling LST estimation at a 1 km spatial resolution under clear and cloudy conditions. The performance of the models was evaluated by comparing the RF simulated LST with observed LST data from MODIS and VIIRS satellites. The Root Mean Square Error (RMSE) for the RF models was found to be 2.17 K and 2.29 K respectively, with coefficient of determination (R2) values of 0.97 for both the models. Furthermore, comparisons with in-situ observations resulted in RMSE values of 2.24–3.70 K and 2.40–4.07 K for clear sky and cloudy sky LST, respectively, for MODIS. Similarly, for the VIIRS LST prediction model, the RMSE values were 2.61–3.51 K and 2.60–3.97 K for clear and cloudy sky conditions. These findings demonstrate the potential of using passive microwave radiometers to estimate LST and highlight the applicability of the RF model for LST estimation under overcast conditions.

Spatial variability in Arctic–boreal fire regimes influenced by environmental and human factors

Wildfire activity in Arctic and boreal regions is rapidly increasing, with severe consequences for climate and human health. Regional long-term variations in fire frequency and intensity characterize fire regimes. The spatial variability in Arctic–boreal fire regimes and their environmental and anthropogenic drivers, however, remain poorly understood. Here we present a fire tracking system to map the sub-daily evolution of all circumpolar Arctic–boreal fires between 2012 and 2023 using 375 m Visible Infrared Imaging Radiometer Suite active fire detections and the resulting dataset of the ignition time, location, size, duration, spread and intensity of individual fires. We use this dataset to classify the Arctic–boreal biomes into seven distinct ‘pyroregions’ with unique climatic and geographic environments. We find that these pyroregions exhibit varying responses to environmental drivers, with boreal North America, eastern Siberia and northern tundra regions showing the highest sensitivity to climate and lightning density. In addition, anthropogenic factors play an important role in influencing fire number and size, interacting with other factors. Understanding the spatial variability of fire regimes and its interconnected drivers in the Arctic–boreal domain is important for improving future predictions of fire activity and identifying areas at risk for extreme events.

Which Nighttime Lights Data Better Represent India’s Economic Activities and Regional Inequality?

This paper demonstrates how the estimation of economic activities and inequality for the Indian economy based on nighttime lights data from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite outperforms those obtained using traditional Defense Meteorological Satellite Program Operational Line-Scan System (DMSP-OLS) sources. In the extant literature, nighttime lights data have been frequently used as a proxy for economic activities, especially at granular geographical levels where gross domestic product data are otherwise unavailable. Since the traditional DMSP-OLS-based nighttime lights data suffer from measurement errors due to blurring and top coding, VIIRS-based nighttime lights data, which are devoid of such limitations, are gaining popularity. We show that at the subnational or district level in India, VIIRS satellite-based nighttime lights data are indeed a better proxy for economic activities and inequality. Our robust findings imply that all future scholarly works that employ nighttime lights data for the Indian economy should use VIIRS-based data for greater accuracy compared to other similar data sources.

Substantial Underestimation of Fine-Mode Aerosol Loading from Wildfires and Its Radiative Effects in Current Satellite-Based Retrievals over the United States.

Wildfires generate abundant smoke primarily composed of fine-mode aerosols. However, accurately measuring the fine-mode aerosol optical depth (fAOD) is highly uncertain in most existing satellite-based aerosol products. Deep learning offers promise for inferring fAOD, but little has been done using multiangle satellite data. We developed an innovative angle-dependent deep-learning model (ADLM) that accounts for angular diversity in dual-angle observations. The model captures aerosol properties observed from dual angles in the contiguous United States and explores the potential of Greenhouse gases Observing Satellite-2's (GOSAT-2) measurements to retrieve fAOD at a 460 m spatial resolution. The ADLM demonstrates a strong performance through rigorous validation against ground-based data, revealing small biases. By comparison, the official fAOD product from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Multiangle Imaging Spectroradiometer (MISR) during wildfire events is underestimated by more than 40% over western USA. This leads to significant differences in estimates of aerosol radiative forcing (ARF) from wildfires. The ADLM shows more than 20% stronger ARF than the MODIS, VIIRS, and MISR estimates, highlighting a greater impact of wildfire fAOD on Earth's energy balance.

Early polar-orbiting satellite-based fire remote sensing during the 1960s

ABSTRACT Satellite-based sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) now routinely provide a detailed picture of global fire activity on a daily basis. These systems explicitly included biomass burning as a phenomenon of interest, and their design was heavily influenced by experience gained with the comparatively primitive Advanced Very High Resolution Radiometer (AVHRR) first deployed on the TIROS-N satellite in the late 1970s. The AVHRR is generally recognized as the earliest spaceborne sensor to provide a middle infrared (MWIR) channel sensitive to actively burning vegetation fires and other high-temperature, sub-pixel thermal anomalies, forming the basis of countless fire studies before higher quality MODIS data became available. Here, we consider in the context of active fire monitoring a sensor much older than the AVHRR, namely the High Resolution Infrared Radiometer (HRIR) on board NASA’s Nimbus-I, -II, and -III satellites, launched in 1964, 1966, and 1969, respectively. This early imaging sensor had a single MWIR channel primarily intended for night-time meteorological observations (a complementary daytime near-infrared channel was added for Nimbus-III) that could potentially serve as a source of pre-AVHRR active fire data. To this end, we inspected night-time HRIR imagery acquired over the western United States and found unambiguous evidence of multiple confirmed wildfires in 1966. In addition, we found strong evidence of actively burning fires in Africa, specifically the Democratic Republic of the Congo, western Zambia, and northern Namibia, and in northern Australia, commensurate with their fire seasons. Our findings indicate that it was actually the HRIR that acquired the earliest MWIR imagery that could be used for identifying biomass burning from space, well over a decade before the first AVHRR was deployed. We found in addition that the retrieval of fire radiative power (FRP) from higher dynamic-range HRIR observations was potentially possible pending refined calibration.

Developing an annual global Sub-National scale economic data from 1992 to 2021 using nighttime lights and deep learning

The Gross Domestic Product (GDP) per capita is one of the most widely used socioeconomic indicators, serving as an integral component for climate change impact analysis. However, a national scale assessment may induce considerable bias because it conceals any internal variations within a country. The lack of a long-term sub-national scale GDP data is a substantive hinderance. Leveraging the close relationship between nighttime lights and GDP, we address this gap by developing a novel methodological framework in two steps. First, under the modeling philosophy of spatial statistics, we developed a novel approach based on deep and machine learning techniques to establish a complex mapping between two inconsistent nighttime lights (NTL) datasets: the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP) and the National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (VIIRS). The models achieve accuracies ranging from 0.945 to 0.980 (correlation coefficients). By taking the estimations ensemble of the two techniques, the time series of DMSP data was extended to 2021. Next, a novel modeling strategy based on multi-layer perceptron was developed to derive the non-linear relationship between NTL and GDP per capita at sub-national scale to alleviate scale effects at this granularity, while explicitly capturing regional heterogeneity effect. The trained models achieve average accuracies of 0.967, 0.959, and 0.959 on the training, validation, and test sets, respectively. We evaluate the developed dataset at the global, national, and sub-national scales from various perspective, and the results offer solid evidence on the reliability of the estimated economic data. By linking to historical global climate change data, we quantify global economic losses attributed to extreme heat to demonstrate how the estimated GDP data can be useful in the climate change impact analysis.