Visible Infrared Imaging Radiometer Suite Research Articles

Satellite Evidence of HONO/NO2 Increase With Fire Radiative Power

AbstractWildfires are important sources of atmospheric reactive nitrogen. The reactive nitrogen species partitioning generally depends on fire characteristics. One reactive nitrogen compound, nitrous acid (HONO), is a source of hydroxyl radicals and nitric oxide, which can impact the oxidizing capacity of the atmosphere and fire plume chemistry and composition. We study the Australian wildfire season of 2019–2020, known as Black Summer, where numerous large and intense wildfires burned throughout the continent. We use HONO and nitrogen dioxide (NO2) from the TROPOspheric Monitoring Instrument (TROPOMI) and fire radiative power (FRP) from the Visible Infrared Imaging Radiometer Suite to investigate HONO and NO2 relationships with fire characteristics. The ratio of HONO to NO2 increases linearly with FRP both in Australia and globally. Both Australian and global fire relationships depend strongly on land cover type. These relationships can be applied to emission inventories to improve wildfire emission representation in models.

FOREST FIRE BURNT AREA EXTRACTION USING FUZZY INTEGRATION OF MULTI-SENSOR SATELLITE DATA FOR THE HIMALAYAN STATE

Abstract. Burnt area assessment due to forest fires is an important aspect to estimate the extent of loss of biodiversity which has become feasible even in hilly and inaccessible areas with the help of geospatial technologies. But satellite data also has some limitations as it increases commission error by misclassifying non-burnt areas as burnt areas. To reduce this commission error, present study has attempted to integrate multi-sensor satellite data to characterize and extract forest fire burnt areas in Uttarakhand which is a fire prone hilly state in Western Himalaya. Landsat-8 and Sentinel-2 optical datasets have been used to calculate eleven vegetation/burn indices to identify burn patches for fire season of 2022 (February to June). These vegetation/burn indices have been calculated from Landsat-8 and Sentinel-2 datasets and integrated using Fuzzy Logic Modelling to get characterized forest fire burnt area maps. Accuracy assessment has been done using Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) active fire points for the characterized map of burnt area by Landsat-8, Sentinel-2 and combining indices from both sensors. The fuzzy map of burnt area using Landsat-8 showed the accuracy of 66.25%, while Sentinel-2 showed accuracy of 59.79% and the integration of fuzzy burnt area maps of both sensors showed the highest accuracy of 79.66%. This information of characterized burnt areas of a region can help forest managers to identify high vulnerable areas to focus on during the fire season to prevent the losses to natural resources, life and property in the region.

Post-launch evaluation and improvements of National Oceanic and Atmospheric Administration-20 Visible-Infrared Imaging Radiometer Suite sensor data record geolocation accuracy

Post-launch evaluation and improvements of National Oceanic and Atmospheric Administration-20 Visible-Infrared Imaging Radiometer Suite sensor data record geolocation accuracy

Continuity of Global MODIS Terrestrial Primary Productivity Estimates in the VIIRS Era Using Model‐Data Fusion

AbstractThe NASA Terra and Aqua satellites have been successfully operating for over two decades, exceeding their original design life. However, the era of NASA's Earth Observing System may be coming to a close as early as 2023. Similarities between the Moderate Resolution Imaging Spectroradiometer (MODIS), aboard Aqua and Terra, and the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors aboard the Suomi NPP, NOAA‐20 and NOAA‐21 satellites enable potential continuity of long‐term earth observational records in the VIIRS era. We conducted a comprehensive calibration and validation of the MODIS MOD17 product, which provided the first global, continuous, weekly estimates of ecosystem gross primary productivity (GPP) and annual estimates of net primary productivity (NPP). Using Bayesian model‐data fusion, we combined 18 years of tower fluxes with prior data on plant traits and hundreds of field measurements of NPP to benchmark MOD17 and to develop the first terrestrial productivity estimates from VIIRS. The updated mean global GPP (NPP) flux from the future MOD17 Collection 7 product and new VNP17 product for 2012–2018 is 127 ± 2.8 Pg C year−1 (58 ± 1.1 Pg C year−1), which compares well with independent top‐down and bottom‐up estimates. MOD17 and VNP17 depict upward productivity trends over recent decades, with 2000–2018 MOD17 GPP (NPP) rising by 0.47 (0.25) Pg C year−2 but slowing to 0.35–0.44 (0.11–0.13) Pg C year−2 over 2012–2021, with a greater reduction in the NPP growth rate. The new VIIRS VNP17 product has the potential to extend these estimates of global, terrestrial primary productivity beyond 2030.

Spatial assessment of nighttime anthropization in the Comoé National Park in Côte d’Ivoira (West Africa)

Abstract Protected areas play a crucial role in preserving biological diversity, providing ecosystem services, and regulating hydroclimatic processes. However, the environmental balance of these areas is increasingly under threat due to rapid population growth, high demand for natural resources, and the effects of global warming. To address this, sustainable management of protected areas is essential. This study aims to develop a methodology for monitoring and assessing levels of nocturnal anthropogenic aggression in the Comoé National Park (CNP) in Côte d’Ivoire. By using geospatial data from the Day/Night Band of the Visible Infrared Imaging Radiometer Suite (VIIRS/DNB), the study characterizes the nocturnal anthropization of CNP through the use of several indices. First, light reflectance density is used to identify nighttime light emission foci. Second, the lighted area density index (LADI) is employed to understand the nature of nocturnal activities taking place within CNP, as indicated by artificial light at night (ALAN). Finally, the light intensity index (LII) is used to evaluate the degree of influence of nocturnal anthropogenic activities on biodiversity. The study also found important activities through LADI and observed a high level of light pollution in CNP. Ultimately, the findings of this study indicate that there is a significant level of nocturnal anthropization within CNP, highlighting the need for effective management and conservation efforts in the area.

High Spatial Resolution Nighttime PM2.5 Datasets in the Beijing–Tianjin–Hebei Region from 2015 to 2021 Using VIIRS/DNB and Deep Learning Model

The concentration of particulate matter (PM2.5) can be estimated using satellite data collected during the daytime. However, there are currently no long-term evening PM2.5 datasets, and the application of low-light satellite data to analyze nighttime PM2.5 concentrations is limited. The Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB), meteorology, Digital Elevation Model, moon phase angle, and Normalized Digital Vegetation Index were used in this study to develop a Deep Neural Network model (DNN) for estimating the nighttime concentrations of PM2.5 in the Beijing–Tianjin–Hebei (BTH) region from 2015 to 2021. To evaluate the model’s performance from 2015 to 2021, a ten-fold cross-validation coefficient of determination was utilized (CV − R2 = 0.51 − 0.68). Using a high spatial resolution of 500 m, we successfully generated a PM2.5 concentration map for the BTH region. This finer resolution enabled a detailed representation of the PM2.5 distribution over the area. Interannual and seasonal trends in nighttime PM2.5 concentrations were analyzed. Winter had the highest seasonal spatial PM2.5, followed by spring and autumn, whereas summer had the lowest. The annual concentration of PM2.5 at night steadily decreased. Finally, the estimation of nighttime PM2.5 was applied in scenarios such as continuous day–night changes, rapid short-term changes, and single-point monitoring. A deeper understanding of PM2.5, enabled by nightly PM2.5, will serve as an invaluable resource for future research.

Plume detection and emission estimate for biomass burning plumes from TROPOMI carbon monoxide observations using APE v1.1

Abstract. This paper presents the automated plume detection and emission estimation algorithm (APE), developed to detect CO plumes from isolated biomass burning events and to quantify the corresponding CO emission rate. APE uses the CO product of the Tropospheric Monitoring Instrument (TROPOMI) on board the Copernicus Sentinel-5 Precursor (S5P) satellite, launched in 2017, and collocated active fire data from the Visible Infrared Imaging Radiometer Suite (VIIRS), the latter flying 3 min ahead of S5P. After identifying appropriate fire events using VIIRS data, an automated plume detection algorithm based on traditional image processing algorithms selects plumes for further data interpretation. The approach is based on thresholds optimized for data over the United States in September 2020. Subsequently, the CO emission rate is estimated using the cross-sectional flux method, which requires horizontal wind fields at the plume height. Three different plume heights were considered, and the ECMWF Reanalysis v5 (ERA5) data were used to compute emissions. A varying plume height in the downwind direction based on three-dimensional Lagrangian simulation was considered appropriate. APE is verified for observations over Australia and Siberia. For all fire sources identified by VIIRS, only 16 % of the data corresponded to clear-sky TROPOMI CO data with plume signature. Furthermore, the quality filters of APE resulted in emission estimations for 26 % of the TROPOMI CO data with plume signatures. Visual filtering of the APE's output showed a true-positive confidence level of 97.7 %. Finally, we provide an estimate of the emission uncertainties. The greatest contribution of error comes from the uncertainty in Global Fire Assimilation System (GFAS) injection height that leads to emission errors <100 %, followed by systematic errors in the ERA5 wind data. The assumption of constant emission during plume formation and spatial under-sampling of CO column concentration by TROPOMI yields an error of <20 %. The randomized errors from the ensemble ERA5 wind data are found to be less than 20 % for 97 % of the cases.

Calibrating Nighttime Satellite Imagery with Red Photometer Networks

The data retrieved from satellite imagery and ground-based photometers are the two main sources of information on light pollution and are thus the two main tools for tackling the problem of artificial light pollution at night (ALAN). While satellite data offer high spatial coverage, on the other hand, photometric data provide information with a higher degree of temporal resolution. Thus, studying the proper correlation between both sources will allow us to calibrate and integrate them to obtain data with both high temporal resolution and spatial coverage. For this purpose, more than 15,000 satellite measurements and 400,000 measurements from 72 photometers for the year 2022 were used. The photometers used were the Sky-Glow Wireless Autonomous Sensor (SG-WAS) and Telescope Encoder and Sky Sensor WIFI (TESS-W) types, located at different ground-based locations, mainly in Spain. These photometers have a spectral sensitivity closer to that of VIIRS than to the Sky Quality Meter (SQM). In this study, a good correlation of data from the Day–Night Band (DNB) from the Visible Infrared Imaging Radiometer Suite (VIIRS) with a red photometric network between 19.41 mag/arcsec2 and 21.12 mag/arcsec2 was obtained.

SDGSAT-1 nighttime light data improve village-scale built-up delineation

While previous urban delineation studies have been based mainly on Defense Meteorological Satellite Program (DMSP) and Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light (NTL) data at a 1-km or 500-m spatial resolution, the higher 10-m-spatial-resolution Sustainable Development Science Satellite-1 (SDGSAT-1) NTL data offer the possibility of sophisticated village-scale analysis. In this study, we developed the quantile method with downward heuristic rules for built-up delineation based on SDGSAT-1 NTL data and proposed a new threshold method, named the trend method framework, for urban dynamic delineation in combination with SDGSAT-1 NTL data and DMSP-VIIRS NTL data. Then, the methods developed in this study were applied to five urban agglomerations (the Beijing-Tianjin-Hebei Urban Agglomeration (BTH), Yangtze River Delta (YRD), Guangdong–Hong Kong–Macao Greater Bay Area (GHM), Chengdu–Chongqing Economic Circle (CCC), and Hainan Free Trade Port (HTP)) in China. The results showed that both the downward quantile method and the trend method can be used to delineate village-scale built-up areas. In the delineation of built-up areas based on SDGSAT-1 NTL data, the trend method provides clearer information on the built-up areas within lighted areas, leading to higher accuracy than the downward quantile method. In the built-up area delineation based on DMSP and VIIRS NTL data at 1-km resolution, the trend method combines SDGSAT-1 NTL data and accounts for information on time-series changes in urbanization, thus allowing for the more accurate extraction of built-up areas. In 2020, the trend method had higher delineation accuracies than the quantile method, with effectiveness accuracies (EAs) of 0.91, 0.77, 0.67, 0.94, and 0.65 for the YRD, BTH, CCC, GHM, and HTP, respectively, compared with the European Space Agency (ESA) land use and land cover (LULC) product. The trend method has very good application prospects for delineating built-up areas using the high-resolution SDGSAT-1 NTL dataset.

Retrieval of nighttime aerosol optical depth by simultaneous consideration of artificial and natural light sources

Aerosol Optical Depth (AOD) is a critical optical parameter that quantifies the degree of light attenuation by aerosols and serves as a fundamental indicator of atmospheric quality. Therefore, accurate quantification and retrieval of AOD is crucial for relevant studies. However, current satellite-based AOD retrieval algorithms suffer from inapplicability under low-light conditions, limiting the development of nighttime AOD retrieval. Under this context, we proposed a novel algorithm, namely Simultaneous Consideration of Artificial and Natural light Sources (SCANS), to obtain nighttime AOD. The core of the SCANS algorithm is considering the synergy of both the natural and artificial light sources to obtain nighttime AOD by integrating atmospheric radiative transfer simulation into an extinction method and performing multiple iterations. SCANS was applied to the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) and the retrieved nighttime AOD was validated with in-situ measurements from five AERONET sites. Results indicate that the Mean Bias Errors (MBEs) of the retrieved nighttime AOD range from 0.0 to 0.08 and the corresponding Root Mean Square Errors (RMSEs) range from 0.11 to 0.17, which exhibit better accuracy than that of the nighttime MERRA-2 AOD. We further compared the retrieved nighttime AOD with the corresponding Air Quality Index (AQI) measurements at six environment monitoring stations and obtained high correlation coefficients (i.e., ranging from 0.733 to 0.940), indicating SCANS's reliability and high accuracy. The proposed SCANS algorithm can effectively obtain nighttime AOD with high quality, thereby advancing research on the diurnal variation of crucial Earth's key elements.

Discovering Hidden Offshore Lighting Structures with Multiyear Low-Light Imaging Satellite Data

The NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB) has a capability to detect lighting present at the earth’s surface. This low-light imaging capability was built to satisfy a requirement from meteorologists to detect clouds at night in the visible based on reflected moonlight. On land, human settlements are the dominant source of DNB detected surface lighting. Lights are also detected offshore primarily arising from fishing boats using lights to attract catch. In 2015, the Earth Observation Group developed the VIIRS Boat Detection (VBD) product and today several fishery agencies use VBD data to monitor fishing activity and compliance with closures. Recently EOG compiled the full record VBD detections as a 15 arc second global grid. The record spans 2012-2021 in Asia and 2017-2021 elsewhere. Upon reviewing the multiyear accumulation of VBD detections we were surprised to find a diversity of previously unseen lighting features [1]. These structures are not evident in VBD from single nights but are only revealed by accumulating the detections across a year or more. The additional features include lit platforms, transit lanes, and vessel anchorages associated with ports and passage straits. Another surprising finding is geopolitical divisions in the density of cumulative vessel detections. In this paper we provide a look into one of the densest areas of VBD detections in the world, from the Yellow Sea, East China Sea and Sea of Japan (East Sea).

Burned area detection and mapping using time series Sentinel-2 multispectral images

Accurate mapping of burned areas (BAs) is essential for post-disaster reconstruction, air quality assessment, and estimation of fire emissions. Remote sensing has become the most practical means to map global/regional BAs. Space-borne coarse-resolution (250–1000 m) BA products play an essential role in fire-related science and applications, but small BAs are typically poorly represented due to resolution limitations. Moderate-resolution (10–30 m) imagery provides a more detailed spatial extent of BAs, but the various disturbances (e.g., plant phenology, cloud shading, flooding, harvesting, grazing, pests, and diseases) make application on a global scale challenging. Here, we propose an automatic BA detection algorithm based on time series images acquired by the Sentinel-2 MultiSpectral Instrument (MSI) and the Google Earth Engine. Specifically: (i) We determined an optimal set of spectral indices for MSI images based on global sampling to mitigate the background heterogeneity and BA spectral diversity. (ii) We jointly used three temporal scales, i.e., the single-temporal, dual-temporal, and short-time series MSI images, to identify potential BA regions (i.e., BA candidates). (iii) We integrated multi-source active fire (AF) products derived from the Moderate-resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Operational Land Imager (OLI), and MSI instead of single-source products to refine BA candidates. (iv) We constructed an interannual time series to improve the BA candidates not confirmed by AF products. The proposed BA detection method was applied to different land cover and regional scales, including forests, grasslands, savannas, shrublands, and croplands at the regional (scene) and national levels (Portugal). Validation and inter-comparison with similar products demonstrated that the proposed method is reliable: the Dice coefficient of our detection results was improved by ∼7% and ∼ 9%, respectively, compared to the moderate-resolution BA products (e.g., LBA_CU for the conterminous United States and FireCCISFD20 for sub-Saharan Africa in 2019). These results suggest that the proposed method can accurately and automatically map BAs, providing a foundation for fire monitoring programs and climate change research.

Performance assessment of the Sentinel-2 LAI products and data fusion techniques for developing new LAI datasets over the high-altitude Himalayan forests

The present study evaluates the accuracy of SNAP-Sentinel-2 Prototype Processor (SL2P) derived Leaf Area Index (LAI) and proposes a new simple method to generate new datasets of LAI through data fusion. Rigorous optimization of the data fusion approaches (Kalman filter and Linear weighted) were performed for the generation of new LAI products over the complex hilly terrain of the Himalayan region. The results showed a good correlation (r = 0.79) and low error (RMSE = 1.63) between SNAP-derived (at 20 m) and ground-observed LAI. A lower correlation was obtained between the ground observed LAI data and the corresponding global LAI products for the Moderate Resolution Imaging Spectroradiometer (MODIS) (r = 0.1, RMSE = 1.19), Copernicus Global Land Service (CGLS) (r = 0.1, RMSE = 0.61) and the Visible Infrared Imaging Radiometer Suite (VIIRS) (r = 0.04, RMSE = 1.25). Notably, after implementing the data fusion, both SNAP-derived LAI and Global LAI products exhibited much-improved performance statistics with ground observed data sets.

Comparative Analysis of the Impact of Two Common Residue Burning Parameters on Urban Air Quality Indicators

Crop residue burning produces a lot of polluting gases and fine particles, endangering human health, damaging soil structure, and causing fire accidents. In addition to the impact of residue burning on the local environment, pollutants can spread with the wind to more distant areas and impact their air quality. Nevertheless, a comparative analysis of the impact of two common residue burning parameters, the number of residue fire points, and residue burned area on urban air quality indicators has not been reported. In this study, the correlation between these two different residue burning parameters on air quality in Daqing City (Western Heilongjiang Province, China) was investigated comparatively using the Visible Infrared Imaging Radiometer Suite (VIIRS) fire point product, the Moderate-resolution Imaging Spectroradiometer (MODIS) burned area product, and buffer zone analysis. The association between MODIS burned area products and air quality index (AQI) was found to be around 0.8. Meanwhile, it was found that the correlation between the number of residue fire points extracted from the VIIRS active fire products and air quality was above 0.6, again with a maximum of 0.75 at a buffer radius of 50 km. Within other levels of buffer zones, the correlation between residue burned area and AQI was consistently higher than that between residue fire points and AQI. By comparing the correlation between VIIRS fire points, MODIS burned area, and the concentration of each AQI pollutant, it can be found that the correlation between the concentration of each AQI pollutant and the residue burned area was higher than that and the fire points number. MODIS burned area monitoring, on the other hand, detects changes in the time series of images taken by satellite at two transit moments to obtain a new burned area and cumulative burned area during this period, allowing the monitoring of fire traces caused by fire points at non-transit moments. From analyzing the correlation between residue fire points, residue burned area, and the concentration of each pollutant (PM2.5, PM10, CO, NO2, SO2, and O3), we found significant correlations between residue burning and PM2.5, PM10, CO, and NO2 concentrations, with the highest correlation (R2) of 0.81 for PM2.5. Moreover, the correlation between residue burned area and PM2.5, PM10, CO, and NO2 concentrations was significantly higher than that between the number of residue fire points and their concentrations.

Characterization of ocean color retrievals and ocean diurnal variations using the Geostationary Ocean Color Imager (GOCI)

Using measurements from the Geostationary Ocean Color Imager (GOCI) on the Communication, Ocean, and Meteorological Satellite (COMS), we characterize and quantify some advantages and applications of the satellite geostationary measurements, compared with those of the polar-orbiting satellites. With eight daily measurements in the western Pacific Ocean, the average GOCI daily coverage of ocean color retrievals reached ∼43.81%, compared to ∼21.77% from the Visible Infrared Imaging Radiometer Suite (VIIRS). The GOCI-measured ocean property data, such as normalized water-leaving radiance [nLw(λ)] and diffuse attenuation coefficient at 490 nm [Kd(490)], show significant diurnal variability over the region, particularly over highly turbid coastal regions. It is noted that GOCI-derived nLw(λ) spectra and Kd(490) in the region have been compared with those from VIIRS in 2012, 2016, and 2019, and shown consistent results (within ∼5–10%). Using the GOCI daily statistical results from its hourly data, i.e., standard deviations (STDs) and coefficient of variations (CVs) of nLw(λ) and Kd(490), the diurnal variability has been demonstrated and quantified. Over turbid coastal regions, STDs and CVs of nLw(λ) at GOCI 555 and 660 nm bands are important for characterizing diurnal variability, while for open oceans STDs and CVs of nLw(λ) at 412, 443, and 555 nm are useful. Specifically, using the four GOCI daily examples in 2019 on January 24, May 21, August 16, and November 15, we have carried out quantitative analyses for understanding regional diurnal variations. In addition, using the GOCI measurements from 2011 to 2020 in the region, hourly climatology Kd(490) data in four seasons and quantitative results are derived and presented. Results show important characteristics in ocean diurnal variabilities over this highly dynamic and complex western Pacific Ocean region. Indeed, the regional diurnal variability has strong spatial and temporal (seasonal) dependences, which are mostly related to regional ocean optical and bio-optical properties.

Mapping high-resolution energy consumption CO2 emissions in China by integrating nighttime lights and point source locations

High-resolution CO2 emission inventories are essential to accurately assess spatiotemporal patterns of carbon emissions, analyze factors affecting carbon emissions, and develop sound emission reduction policies. The top-down approach is often used to map CO2 emissions from energy consumption due to its simplicity. However, the spatial proxy variables commonly used in this method, such as nighttime light (NL), land use, and population, are difficult to reflect the spatial distribution of CO2 emissions from large point sources. Therefore, this study uses the active fire product provided by Visible Infrared Imaging Radiometer Suite (VIIRS) sensors on Suomi National Polar-Orbiting Partnership (Suomi-NPP) satellite to extract the location of industrial heat sources in China, and then develops an improved CO2 emission estimation model by integrating industrial heat sources, Global Energy Monitor (GEM) power plant location and nighttime lights. The model is used to map CO2 emissions from energy consumption at a resolution of 1 km*1 km from 2012 to 2019 in China. It is found that the overall accuracy of the model is greatly improved at the provincial level, the R2 value is >0.75, and RMSE is distributed in 40–110 Mt. At the grid level, the improved model allocates more carbon emissions to the grid where the point source is located, which makes the spatial distribution of CO2 emissions more reasonable.

Calibration of the SNPP and NOAA 20 VIIRS sensors for continuity of the MODIS climate data records

Accurate long-term sensor calibration and periodic re-processing to ensure consistency and continuity of atmospheric, land and ocean geophysical retrievals from space within the mission period and across different missions is a major requirement of climate data records. In this work, we applied the Multi-Angle Implementation of Atmospheric Correction (MAIAC)-based vicarious calibration technique over Libya-4 desert site to perform calibration analysis of Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 satellites. For both VIIRS sensors we characterized residual linear calibration trends and cross-calibrated both sensors to MODerate resolution Imaging Spectroradiometer (MODIS) Aqua regarded as a calibration standard. The relative spectral response (RSR) differences were accounted for using the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS) hyperspectral surface reflectance data. Our results agree with independent vicarious calibration results of both the MODIS/VIIRS Characterization Support Team as well as the CERES Imager and Geostationary Calibration Group within estimated uncertainty of 1–2%. Analysis of MAIAC geophysical products with the new calibration shows a high level of agreement of MAIAC aerosol, surface reflectance and NDVI records between MODIS and VIIRS. Excluding high aerosol optical depth (AOD), all three sensors agree in AOD with mean difference (MD) less than 0.01 and residual mean squared difference rmsd ∼ 0.04. Spectral geometrically normalized surface reflectance agrees within rmsd of 0.003–0.005 in the visible and 0.01–0.012 at longer wavelengths. The residual surface reflectance differences are fully explained by differences in spectral filter functions. Finally, difference in NDVI is characterized by rmsd ∼ 0.02 and MD less than 0.003 for NDVI based on VIIRS imagery bands I1/I2 and less than 0.01 for NDVI based on VIIRS radiometric bands M5/M7. In practical sense, these numbers indicate consistency and continuity in MAIAC records ensuring the smooth transition from MODIS to VIIRS.

Spatiotemporal dynamics of population density in China using nighttime light and geographic weighted regression method*

ABSTRACT The distribution and dynamic changes of regional or national population data with long time series are very important for regional planning, resource allocation, government decision-making, disaster assessment, ecological protection, and other sustainability research. However, the existing population datasets such as LandScan and WorldPop all provide data from 2000 with limited time series, while GHS-POP only utilizes land use data with limited accuracy. In view of the limited remote sensing images of long time series, it is necessary to combine existing multi-source remote sensing data for population spatialization research. In this research, we developed a nighttime light desaturation index (NTLDI). Through the cross-sensor calibration model based on an autoencoder convolutional neural network, the NTLDI was calibrated with the same period Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS-DNB) data. Then, the geographically weighted regression method is used to determine the population density of China from 1990 to 2020 based on the long time series NTL. Furthermore, the change characteristics and the driving factors of China’s population spatial distribution are analyzed. The large-scale, long-term population spatialization results obtained in this study are of great significance in government planning and decision-making, disaster assessment, resource allocation, and other aspects.

The Fire Inventory from NCAR version 2.5: an updated global fire emissions model for climate and chemistry applications

Abstract. We present the Fire Inventory from National Center for Atmospheric Research (NCAR) version 2.5 (FINNv2.5), a fire emissions inventory that provides publicly available emissions of trace gases and aerosols for various applications, including use in global and regional atmospheric chemistry modeling. FINNv2.5 includes numerous updates to the FINN version 1 framework to better represent burned area, vegetation burned, and chemicals emitted. Major changes include the use of active fire detections from the Visible Infrared Imaging Radiometer Suite (VIIRS) at 375 m spatial resolution, which allows smaller fires to be included in the emissions processing. The calculation of burned area has been updated such that a more rigorous approach is used to aggregate fire detections, which better accounts for larger fires and enables using multiple satellite products simultaneously for emissions estimates. Fuel characterization and emissions factors have also been updated in FINNv2.5. Daily fire emissions for many trace gases and aerosols are determined for 2002–2019 (Moderate Resolution Imaging Spectroradiometer (MODIS)-only fire detections) and 2012–2019 (MODIS + VIIRS fire detections). The non-methane organic gas emissions are allocated to the species of several commonly used chemical mechanisms. We compare FINNv2.5 emissions against other widely used fire emissions inventories. The performance of FINNv2.5 emissions as inputs to a chemical transport model is assessed with satellite observations. Uncertainties in the emissions estimates remain, particularly in Africa and South America during August–October and in southeast and equatorial Asia in March and April. Recommendations for future evaluation and use are given.

Spatio-temporal structures of satellite-derived water quality indicators along the Korean South Coast

The structure of 9-year time series data for Sea Surface Temperature (SST), Chlorophyll a (Chl-a) and Total Suspended Solids (TSS), derived from the Visible Infrared Imaging Radiometer Suite (VIIRS), was examined in this study. Authors found that there exists strong seasonality among the three variables with spatial heterogeneity along the Korean South Coast (KSC). In specific, SST was in phase with Chl-a, but out of phase with TSS by six months. A strong inversed spectral power with six-month phase-lag was found between Chl-a and TSS. This could be attributed to different dynamics and environmental settings. For example, Chl-a concentration seemed to have strong positive correlation with SST indicating typical seasonality of marine biogeochemical processes such as primary production; while a strong negative correlation between TSS and SST might have been influenced by changes in physical oceanographic processes, such as stratification and monsoonal wind-driven vertical mixing. In addition, the strong east–west heterogeneity of Chl-a suggests that the marine coastal environments are predominantly governed by distinct local hydrological conditions and human activities associated with land cover and land use, while the east–west spatial pattern revealed in TSS timeseries was associated with the gradient of tidal forcings and topographical changes keeping tidally induced resuspension low eastward.