Precise GDP Spatialization and Analysis in Built-Up Area by Combining the NPP-VIIRS-like Dataset and Sentinel-2 Images.

Spatialization and analysis of the gross domestic product of second and tertiary industries (GDP23) can effectively depict the socioeconomic status of regional development. However, existing studies mainly conduct GDP spatialization using nighttime light data; few studies specifically concentrated on the spatialization and analysis of GDP23 in a built-up area by combining multi-source remote sensing images. In this study, the NPP-VIIRS-like dataset and Sentinel-2 multi-spectral remote sensing images in six years were combined to precisely spatialize and analyze the variation patterns of the GDP23 in the built-up area of Zibo city, China. Sentinel-2 images and the random forest (RF) classification method based on PIE-Engine cloud platform were employed to extract built-up areas, in which the NPP-VIIRS-like dataset and comprehensive nighttime light index were used to indicate the nighttime light magnitudes to construct models to spatialize GDP23 and analyze their change patterns during the study period. The results found that (1) the RF classification method can accurately extract the built-up area with an overall accuracy higher than 0.90; the change patterns of built-up areas varied among districts and counties, with Yiyuan county being the only administrative region with an annual expansion rate of more than 1%. (2) The comprehensive nighttime light index is a viable indicator of GDP23 in the built-up area; the fitted model exhibited an R2 value of 0.82, and the overall relative errors of simulated GDP23 and statistical GDP23 were below 1%. (3) The year 2018 marked a significant turning point in the trajectory of GDP23 development in the study area; in 2018, Zhoucun district had the largest decrease in GDP23 at -52.36%. (4) GDP23 gradation results found that Zhangdian district exhibited the highest proportion of high GDP23 (>9%), while the proportions of low GDP23 regions in the remaining seven districts and counties all exceeded 60%. The innovation of this study is that the GDP23 in built-up areas were first precisely spatialized and analyzed using the NPP-VIIRS-like dataset and Sentinel-2 images. The findings of this study can serve as references for formulating improved city planning strategies and sustainable development policies.

Estimating sectoral COVID-19 economic losses in the Philippines using nighttime light and electricity consumption data.

Introduction: Economic loss estimation is critical for policymakers to craft policies that balance economic and health concerns during pandemic emergencies. However, this task is time-consuming and resource-intensive, posing challenges during emergencies. Method: To address this, we proposed using electricity consumption (EC) and nighttime lights (NTL) datasets to estimate the total, commercial, and industrial economic losses from COVID-19 lockdowns in the Philippines. Regression models were employed to establish the relationship of GDP with EC and NTL. Then, models using basic statistics and weather data were developed to estimate the counterfactual EC and NTL, from which counterfactual GDP was derived. The difference between the actual and the counterfactual GDP from 2020 to 2021 yielded economic loss. Results: This paper highlights three findings. First, the regression model results established that models based on EC (adj-R2 ≥ 0.978) were better at explaining GDP than models using NTL (adj-R2 ≥ 0.663); however, combining both EC and NTL improved the prediction (adj-R2 ≥ 0.979). Second, counterfactual EC and NTL could be estimated using models based on statistics and weather data explaining more than 81% of the pre-pandemic values. Last, the estimated total loss amounted to 2.9 trillion PhP in 2020 and 3.2 trillion PhP in 2021. More than two-thirds of the losses were in the commercial sector as it responded to both policies and the COVID-19 case surge. In contrast, the industrial sector was affected primarily by the lockdown implementation. Discussion: This method allowed monitoring of economic losses resulting from long-term and large-scale hazards such as the COVID-19 pandemic. These findings can serve as empirical evidence for advocating targeted strategies that balance public health and the economy during pandemic scenarios.

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.

Detection of harmful algal blooms from satellite-based inherent optical properties of the ocean in Paracas Bay - Peru.

Harmful algal bloom (HAB) events in front of Pisco River, inside Paracas Bay and Lagunillas inlet on the southern coast of Peru was identified from a satellite index (IOPifa) generated with daily high-resolution satellite data of phytoplankton absorption (aphy,GIOP) and non-algal detrital material plus CDOM (adCDOM,GIOP) from the Generalized Inherent Optical Properties (GIOP) model of Modis-Aqua, Viirs-Snpp and Viirs-Jpss1 satellites were used. Phytoplankton density field data sampling from HAB's monitoring programs of IMARPE of 2018 and 2019 were used to validate and identify the extent and spatio-temporal variability of these events. The satellite index (IOPifa) identified for Modis-Aqua 9 active HABs, 8 events in final conditions and 6 events that do not represent HAB conditions, while for Viirs-Snpp found 14 active HABs, 7 events in decaying bloom conditions and 13 events that do not represent HABs; and for Viirs-Jpss1 the index identified 7 active events, 14 in final bloom conditions and 6 that do not represent HABs conditions. The one-factor anova model was applied (p-value = 0.32 > 0.05), indicating that there is no evidence of a difference in the population means of the indices for each sensor. Subsequently, the pairwise multiple comparisons analysis with a 95 % confidence level of Tukey's test confirmed that there are no significant differences in the satellite index value, the differences could be associated with the spectral characteristics of the cell density of the species community and the oceanographic and environmental conditions. The spatial overlap between the in situ harmful algal blooms areas and the calculated satellite index, shows the capacity of the IOP satellite data for the HABs detection. However, it was also evidenced that some HAB events with high phytoplankton cell density had low IOPifa values, while other events with lower cell density were easily identified by the satellite index. This would indicate the ability of the ocean inherent optical properties to differentiate the phytoplankton types that cause algal blooms.

Fine simulation of PM2.5 combined with NPP-VIIRS night light remote sensing and mobile monitoring data.

Human activity plays a crucial role in influencing PM2.5 concentration and can be assessed through nighttime light remote sensing. Therefore, it is important to investigate whether the nighttime light brightness can enhance the accuracy of PM2.5 simulation in different stages. Utilizing PM2.5 mobile monitoring data, this study introduces nighttime lighting brightness as an additional factor in the PM2.5 simulation model across various time periods. It compares the differences in simulation accuracy, explores the impact of nocturnal human activities on PM2.5 concentrations at different periods of the following day, and analyzes the spatial and temporal pollution pattern of PM2.5 in urban functional areas. The results show that (1) the incorporation of nighttime lighting brightness effectively enhances the model's accuracy (R2), with an improvement ranging from 0.04 to 0.12 for different periods ranges. (2) the model's accuracy improves more prominently during 8:00-12:00 on the following day, and less so during 12:00-18:00, as the PM2.5 from human activities during the night experiences a strong aggregation effect in the morning of the next day, with the effect on PM2.5 concentration declining after diffusion until the afternoon. (3) PM2.5 is primarily concentrated in urban functional areas including construction sites, roads, and industrial areas during each period. But in the period of 8:00-12:00, there is a significant level of PM2.5 pollution observed in commercial and residential areas, due to the human activities that occurred the previous night.

Where does floating Sargassum in the East China Sea come from?

Floating macroalgae of Sargassum horneri (S. horneri) in the East China Sea (ECS) has increased in recent years, with ocean warming being one of the driving factors. Yet their possible origins, based on a literature review, are unclear. Here, using multi-sensor high-resolution remote sensing data and numerical experiments for the period of 2015-2023, we show two possible origins of the ECS floating S. horneri, one being local near the Zhejiang coast with initiation in January-February and the other being remote (> 800 km from the first) in the Bohai Sea with initiation in June-November. While their drifting pathways are revealed in the sequential remote sensing imagery, numerical experiments suggest that S. horneri from the remote origin (Bohai Sea) can hardly meander through the strong Yangtze River frontal zone, which may serve as a "wall" to prevent trespassing of surface floating seaweed to the south of the frontal zone, where S. horneri has a local origin. PLAIN LANGUAGE SUMMARY: Sargassum horneri (S. horneri) is a brown macroalgae (seaweed) abundant in surface waters of the East China Sea (ECS), which can serve as a moving habitat, but can also cause major beaching events and environmental problems. Knowledge of its origins is important to help implement mitigation strategies and understand possible ecological impacts along its drifting pathways. Using high-resolution remote sensing images and numerical experiments, we track floating S. horneri in space and time between 2015 and 2023. Two possible origins are identified, one being far away from the ECS and the other being local, both of which are known to have benthic S. horneri. The study also reveals how S. horneri are transported from their source regions resulting in large-scale distributions previously observed in medium-resolution satellite imagery.

Smoke emissions from biomass burning in Central Mexico and their impact on air quality in Mexico City: May 2019 case study.

Smoke emissions from biomass burning considerably influence regional and local air quality. Many natural wildfires and agricultural burns occur annually in Central Mexico during the hot, dry season (March to May), potentially leading to air quality problems. Nevertheless, the impact of these biomass burning emissions on Mexico City's air quality has not been investigated in depth. This study examines a severely deteriorated air quality case from 11 to 16 May 2019, during which fine particle concentrations (PM2.5) exceeded the 99th percentile of the available official dataset (2005-2019). Specifically, this work aims to highlight the role of fires and regional pollution in the severe episode observed in Mexico City, identifying the fires that were the sources of regional pollution, the type of fuel burned in those fires, and the dominant atmospheric transport pattern. Biomass burning emissions were calculated for different land cover types using satellite data from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Moderate-Resolution Imaging Spectroradiometer (MODIS). PM2.5 increased by a factor of 2 at some monitoring sites, and ozone concentration increased to 40 % in Mexico City during the poor air quality episode. Our results indicate that over 50 % of the fire activity observed during the 2019 fire season was concentrated in May in Central Mexico. The burning activity was mainly seen over shrubland and forest between 10 and 15 May. Moreover, the fire radiative power analysis indicates that most energy was associated with burning shrubland and forests. Organic carbon emissions were estimated highest on 14 and 15 May, coinciding with the largest number of fires. Back trajectory analysis indicates that enhanced concentration of air pollutants in Mexico City originated from biomass burning detected in neighboring states: Guerrero, Michoacán, and the State of Mexico. Smoke from fires on the specific vegetation cover was advected into Mexico City and contributed to the bad air quality episode. Further meteorological analysis evidenced that the fire intensity and emissions were worsened by low humidity and the late onset of the rainy season in Central Mexico.

Spatiotemporal changes of aerosol optical depth and its response to urbanization: a case study of Jinan City, China, 2009-2018.

With the insidiously growing impact of urban development on the environment, the issue of air quality has attracted extensive attention nationally and globally. It is of great significance to study the influence of urbanization on air quality for the rational development of cities. MODIS-MAIAC (Moderate Resolution Imaging Spectroradiometer-Multi-Angle Implementation of Atmospheric Correction) Aerosol optical depth (AOD) product, DMSP/OLS (Defense Meteorological Satellite Program/Operational Linescan System) and NPP/VIIRS (Suomi National Polar-orbiting Partnership/Visible Infrared Imaging Radiometer Suite) night-light were used to explore the spatiotemporal variation and correlation between AOD and urbanization development before and after the promulgation of environmental governance policies in Jinan City from 2009 to 2018. Results show that (1) the spatial distribution of AOD in Jinan had the characteristics of high in the north and low in the south, high in the west and low in the east, and low in some parts of the central region; there was a significant seasonal variation in time, with the highest AOD in summer and the lowest in winter. During 2009-2013, the annual average variation of AOD increased by 20.6%, while during 2014-2018, it decreased by 35.3%; (2) The distribution of night-light in Jinan City has progressively expanded, mirroring the city's ongoing development. The spatial distribution of aerosols in urban areas was relatively low compared to the surrounding areas of the city. (3) From 2009 to 2013, there existed a significant positive correlation between the spatial and temporal distribution of AOD and night-light. However, from 2014 to 2018, with the implementation of environmental governance policies, this relationship shifted to a significant negative correlation between the spatial and temporal distribution of AOD and night-light. Through an analysis of the correlation between urban development and aerosol depth in Jinan City over the past decade, it can be concluded that urban development does not inevitably result in elevated AOD levels. Notably, the Jinan government has achieved remarkable results in controlling the atmospheric environment, as evidenced by recent years' improvements.

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.

Prediction of fishing intensity and trends across South China Sea biogeographic zones.

The volume of industrial fishing in the South China Sea ranks among the top global sustainable fisheries concerns of the Food and Agriculture Organization (FAO). To better understand the scale of management challenges, biogeographic zones of the SCS were characterized, and within each a multivariate GAM (General Additive Model) was fitted to predict and map the complete fishing activities from 2017 to 2020. Model variables, some incomplete or with gaps, included: VIIRS DNB night-time light imagery; Global Fisheries Watch (GFW) data; satellite Ocean Colour; Sea Surface Temperature; and bathymetry data. Four biogeographic zones with differing fishing patterns and trends were identified. We used cross-validation and the GAM model's own tuning method for model prediction accuracy determination, which performed well in four biogeographic zones (R2 respectively: 0.62, 0.68, 0.74 and 0.71). High-intensity fishing grounds are mainly distributed in offshore continental shelf areas. From 2017 to 2019, high-intensity fishing grounds were located near the Beibu Gulf of Vietnam, south Vietnam, part of the Gulf of Thailand and the central Java Sea, where fishing effort greater than 50 h exceeded average annual SCS fishing intensity for several years. By season, intensity and extent of fishing in Spring were largest. In 2020, due to the impact of COVID-19, except for Spring, fishing volume generally decreased. Our experimental results provide new insights and an adaptable biogeographic modelling methodology to map the scale and intensity of regional fishing activities more accurately and completely. This more comprehensive database, that takes account of intrinsic biogeographic fishery context, will help improve and strengthen the regulation of fishing activities around the world.

Evaluation of the Health Status of Indonesian Watersheds Using Impervious Surface Area as an Indicator.

Impervious surfaces affect the ecosystem function of watersheds. Therefore, the impervious surface area percentage (ISA%) in watersheds has been regarded as an important indicator for assessing the health status of watersheds. However, accurate and frequent estimation of ISA% from satellite data remains a challenge, especially at large scales (national, regional, or global). In this study, we first developed a method to estimate ISA% by combining daytime and nighttime satellite data. We then used the developed method to generate an annual ISA% distribution map from 2003 to 2021 for Indonesia. Third, we used these ISA% distribution maps to assess the health status of Indonesian watersheds according to Schueler's criteria. Accuracy assessment results show that the developed method performed well from low ISA% (rural) to high ISA% (urban) values, with a root mean square difference value of 0.52 km2, a mean absolute percentage difference value of 16.2%, and a bias of -0.08 km2. In addition, since the developed method uses only satellite data as input, it can be easily implemented in other regions with some modifications according to differences in light use efficiency and economic development in each region. We also found that 88% of Indonesian watersheds remain without impact in 2021, indicating that the health status of Indonesian watersheds is not a serious problem. Nevertheless, Indonesia's total ISA increased significantly from 3687.4 km2 in 2003 to 10,505.5 km2 in 2021, and most of the increased ISA was in rural areas. These results indicate that negative trends in health status in Indonesian watersheds may emerge in the future without proper watershed management.

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.

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.

Floating Debris in the Northern Gulf of Mexico after Hurricane Katrina.

Hurricane Katrina (category 5 with maximum wind of 280 km/h when the eye is in the central Gulf of Mexico) made landfall near New Orleans on August 29, 2005, causing millions of cubic meters of disaster debris, severe flooding, and US$125 billion in damage. Yet, despite numerous reports on its environmental and economic impacts, little is known about how much debris has entered the marine environment. Here, using satellite images (MODIS, MERIS, and Landsat), airborne photographs, and imaging spectroscopy, we show the distribution, possible types, and amount of Katrina-induced debris in the northern Gulf of Mexico. Satellite images collected between August 30 and September 19 show elongated image features around the Mississippi River Delta in a region bounded by 92.5°W-87.5°W and 27.8°N-30.25°N. Image spectroscopy and color appearance of these image features indicate that they are likely dominated by driftwood (including construction lumber) and dead plants (e.g., uprooted marsh) and possibly mixed with plastics and other materials. The image sequence shows that if aggregated together to completely cover the water surface, the maximal debris area reached 21.7 km2 on August 31 to the east of the delta, which drifted to the west following the ocean currents. When measured by area in satellite images, this perhaps represents a historical record of all previously reported floating debris due to natural disasters such as hurricanes, floodings, and tsunamis.

Nighttime light perspective in urban resilience assessment and spatiotemporal impact of COVID-19 from January to June 2022 in mainland China.

The outbreak of the coronavirus disease 2019 (COVID-19) epidemic has resulted in large threats and damage to society and the economy. In this study, we evaluate and verify the comprehensive resilience and spatiotemporal impact of the COVID-19 epidemic from January to June 2022 in mainland China based on multisource data. First, we adopt a combination of the mandatory determination method and the coefficient of variation method to determine the weight of the urban resilience assessment index. Furthermore, Beijing, Shanghai, and Tianjin were selected to verify the feasibility and accuracy of the resilience assessment results based on the nighttime light data. Finally, the epidemic situation was dynamically monitored and verified with population migration data. The results show that urban comprehensive resilience of mainland China is shown in the distribution pattern of higher resilience in the middle east and south and lower resilience in the northwest and northeast. Moreover, the average light intensity index is inversely proportional to the number of newly confirmed and treated cases of COVID-19 in the local area. This study provides a scientific reference to improve the comprehensive resilience of cities to achieve the goals of sustainable development (SDGs 11): make cities and human settlements resilient and sustainable.

Validation, inter-comparison, and usage recommendation of six latest VIIRS and MODIS aerosol products over the ocean and land on the global and regional scales.

MODIS and VIIRS aerosol products have been used extensively by the scientific community. Products in operation include MODIS Dark Target (DT), Deep Blue (DB), and Multi-Angle Implementation of Atmospheric Correction (MAIAC) and VIIRS DT, DB, and NOAA Environmental Data Record products. This study comprehensively validated and inter-compared aerosol optical depth (AOD) and Ångstrom exponent (AE) over land and the ocean of these six products (seven different algorithms) on regional and global scales using AErosol RObotic NETwork (AERONET) and Maritime Aerosol Network (MAN) observations. In particular, we used AERONET inversions to classify AOD and AE biases into different scenarios (depending on absorption and particle size) to obtain retrieval error characteristics. The spatial patterns of the products and their differences were also analyzed. Collectively, although six satellite AODs are in good agreement with ground observations, VIIRS DB (land and ocean) and MODIS MAIAC (land only) AODs show better validation metrics globally and better performance in 8/10 world regions. Therefore, they are more recommended for usage. Although land AE retrievals are not capable of quantitative application at both instantaneous and monthly scales, their spatial patterns show qualitative potential. Ocean AE shows a relatively high correlation coefficient with ground measurements (>0.75), meeting the fraction of expected accuracy (> 0.70). Error characteristic analyses emphasize the importance of aerosol particle size and absorption-scattering properties for land retrieval, indicating that improving the representation of aerosol types is necessary. This study is expected to facilitate the usage selection of operating VIIRS and MODIS products and their algorithm improvements.

Estimation of agricultural burned affected area using NDVI and dNBR satellite-based empirical models.

One of the major crucial issues that need worldwide attention is open stubble burning, which imposes a variety of adverse impacts on nature and human society, destroying the world's biodiversity. Many earth observation satellites render information to monitor and assess agricultural burning activities. In this study, different remotely sensed data (Sentinel-2A, VIIRS) has been employed to estimate the quantitative measurements of agricultural burned areas of the Purba Bardhaman district from October-December 2018. The multi-temporal image differencing techniques and indices (NDVI, NBR, and dNBR) and VIIRS active fires data (VNP14IMGT) have been utilized to spot agricultural burned areas. In the case of the NDVI technique, a prominent area, 184.82 km2 of agricultural burned area (7.85% of the total agriculture), was observed. The highest (23.04 km2) burned area was observed in the Bhatar block, located in the middle part of the district, and the lowest (0.11 km2) burned area was observed in the Purbasthali-II block, which is located in the eastern part of the district. On the other hand, the dNBR technique revealed that the agricultural burned areas enwrap 8.18% of the total agricultural area, which is 192.45 km2. As per the earlier NDVI technique, the highest agricultural burned areas (24.82 km2) were observed in the Bhatar block, and the lowest (0.13 km2) burn area occurred in the Purbashthali-II block. In both cases, it is observed that agricultural residue burning is high in the western part of the Satgachia block and the adjacent areas of the Bhatar block, which is in the middle part of Purba Bardhaman. The agricultural burned area was extracted using different spectral separability analyses, and the performance of dNBR was the most effective in spectral discrimination of burned and unburned surfaces. This study manifested that agricultural residue burning started in the central part of Purba Bardhaman. Later it spread all over the district due to the trend of early harvesting rice crops in this region. The performance of different indices for mapping the burned areas was evaluated and compared, revealing a strong correlation (R2) = 0.98. To estimate the campaign's effectiveness against the dangerous practice and plan the control of the menace, regular monitoring of crop stubble burning using satellite data is required.

Retrieval uncertainty and consistency of Suomi-NPP VIIRS Deep Blue and Dark Target aerosol products under diverse aerosol loading scenarios over South Asia.

Retrieval accuracy and stability of two operational aerosol retrieval algorithms, Deep Blue (DB) and Dark Target (DT), applied on Visible Infrared Imaging Radiometer Suite (VIIRS) on-board Suomi National Polar-orbiting Partnership (S-NPP) satellite were evaluated over South Asia. The region is reported to be highly challenging to accurate estimation of satellite-based aerosol optical properties due to variations in surface reflectance, complex aerosol system and regional meteorology. Performance of both algorithms were initially evaluated by comparing their ability to retrieve aerosol signal over the complex geographical region under specific air pollution emission scenario. Thereafter, retrieval accuracy was investigated against 10 AERONET sites across South Asia, selected based on their geography and predominance aerosol types, from year 2012-2021. Geo-spatial analysis indicates DB to efficiently retrieve fine aerosol features over bright arid surfaces, and for smoke/dust dominating events whereas DT was better to identify small fire events under dark vegetated surface. Both algorithms however, indicate unsatisfactory retrieval accuracy against AERONET having 56-59% of valid retrievals with high RMSE (0.30-0.33) and bias. Overall, DB slightly underpredicted AOD with -0.02 mean bias (MB) whereas DT overpredicted AOD (MB: 0.13), with seasonality in their retrieval efficiency against AERONET. Time-series analysis indicates stability in retrieving AOD and match-up number for both algorithms. Retrieval bias of DB and DT AOD against AERONET AOD under diverse aerosol loading, aerosol size, scattering/absorbing aerosol, and surface vegetation coverage scenarios revealed DT to be more influenced by these conditions. Error analysis indicates at low AOD (≤0.2), accuracy of both DB and DT were subject to underlying vegetation coverage. At AOD>0.2, DB performed well in retrieving coarse aerosols whereas DT was superior when fine aerosols dominated. Overall, accuracy of both VIIRS algorithms require further refinement to continue MODIS AOD legacy over South Asia.

Multi-scale analysis of China's transportation carbon emissions based on nighttime light data.

This study explores the spatial and temporal evolution characteristics of transportation carbon emissions from multiple scales. Based on the integrated DMSP/OLS-NPP/VIIRS nighttime light data, a transportation carbon emission estimation model was constructed, and the spatial and temporal evolution characteristics of transportation carbon emissions in 30 provinces and some counties in China from 2000 to 2019 were analyzed. The main findings are as follows: (1) The goodness-of-fit of the estimation model improved from 51.2 to 87.15% by introducing the GDP variables. (2) At the provincial scale, the provinces with high carbon emissions from transportation were mainly distributed in the eastern region, with the highest value increasing from 19,171.6 million tons in 2000 to 71,545.98 million tons in 2019. The spatial distribution has a significant and positive spatial spillover effect, and the H-H aggregation was mainly distributed in the east-central region, showing a trend of expansion from the coast to the inland. Trend analysis showed that Shandong, Guangdong, Shanghai, and Jiangsu were areas with a rapid growth of high carbon emissions. (3) The county scale displayed a northeast-southwest evolutionary pattern, with the center of gravity in Henan. The spatial distribution showed a significant spatial agglomeration phenomenon. Trend analysis indicated that the transportation carbon emissions in 184 counties need to be controlled urgently, which was the focus of carbon emission reduction. This paper theoretically enriches the measurement method of transportation carbon emissions and overcomes the problem of insufficient spatial information of statistical data. In practice, it provides a scientific basis for accurate emission reduction and low-carbon development of transportation.

EVI and NDVI as proxies for multifaceted avian diversity in urban areas.

Most ecological studies use remote sensing to analyze broad-scale biodiversity patterns, focusing mainly on taxonomic diversity in natural landscapes. One of the most important effects of high levels of urbanization is species loss (i.e., biotic homogenization). Therefore, cost-effective and more efficient methods to monitor biological communities' distribution are essential. This study explores whether the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI) can predict multifaceted avian diversity, urban tolerance, and specialization in urban landscapes. We sampled bird communities among 15 European cities and extracted Landsat 30-meter resolution EVI and NDVI values of the pixels within a 50-m buffer of bird sample points using Google Earth Engine (32-day Landsat 8 Collection Tier 1). Mixed models were used to find the best associations of EVI and NDVI, predicting multiple avian diversity facets: Taxonomic diversity, functional diversity, phylogenetic diversity, specialization levels, and urban tolerance. A total of 113 bird species across 15 cities from 10 different European countries were detected. EVI mean was the best predictor for foraging substrate specialization. NDVI mean was the best predictor for most avian diversity facets: taxonomic diversity, functional richness and evenness, phylogenetic diversity, phylogenetic species variability, community evolutionary distinctiveness, urban tolerance, diet foraging behavior, and habitat richness specialists. Finally, EVI and NDVI standard deviation were not the best predictors for any avian diversity facets studied. Our findings expand previous knowledge about EVI and NDVI as surrogates of avian diversity at a continental scale. Considering the European Commission's proposal for a Nature Restoration Law calling for expanding green urban space areas by 2050, we propose NDVI as a proxy of multiple facets of avian diversity to efficiently monitor bird community responses to land use changes in the cities.