NPP-VIIRS Data Research Articles

Analysis of Spatial and Temporal Changes and Drivers of Urban Sprawl in Xinjiang Based on Integrated DMSP-OLS and NPP-VIIRS Data

The accelerated urbanization taking place across Xinjiang in recent years has vastly improved the quality of life for people living in the region. However, to achieve rational urban growth and sustainable regional development, a deeper understanding of the spatial and temporal patterns, spatial morphology, and driving factors of urban sprawl is crucial. Nighttime light (NTL) data provide a novel approach for studying the spatial and temporal changes in urban expansion. In this study, based on DMSP-OLS and NPP-VIIRS data, we analyze the spatiotemporal characteristics of urban changes using the standard deviation ellipse and employ the geographical detector to analyze the impact of natural environmental and socioeconomic factors on the dynamic rate of urban expansion. The results reveal the following. (1) The overall accuracy of urban area extraction is above 80%, and the urban area of Xinjiang has expanded about 9.1 times over the past 30 years. Further, the growth rate from 2007 to 2017 exceeds the growth rate from 1992 to 1997, with the center of gravity of urban development shifting to the southwest. (2) The 5a sliding average temperature and average annual precipitation in the study area in 1992–2022 are 6.08 °C and 169.72 mm, respectively, showing a decrease in the urbanization rate followed by an increase, due to a rise in temperature and precipitation levels. (3) By combining the results of geographical detector factor detection and interaction detection, precipitation is determined to be the main controlling factor, while air temperature and GDP are secondary factors. This study presents new findings on the correlation between urban spatial and temporal changes and climate in Xinjiang, thus providing a scientific reference for future research on urban expansion and natural environment evolution.

How to accurately assess the spatial distribution of energy CO2 emissions? Based on POI and NPP-VIIRS comparison

Timely and accurately estimating the spatial distribution of CO2 emissions is crucial for formulating energy conservation and emission reduction policies. Although nighttime light data has been proved to be effective in estimating the spatial distribution of CO2 emissions, it cannot estimate the spatial distribution of different types of CO2 emissions (commercial CO2 emissions, residential CO2 emissions, light industry CO2 emissions, heavy industry CO2 emissions, and agricultural CO2 emissions). Based on the local adaptive method, this study compares the potential of POI data and NPP-VIIRS data for modeling different types of carbon emissions in China to analyze the spatial structure of carbon emissions within cities. The results showed that: (1) POI data is much more powerful and reliable than NPP-VIIRS data regarding monitoring ability at the suburbs and mountainous areas. (2) From the point of view of the estimation ability of different types of carbon emissions, in the commercial CO2 emissions and residential CO2 emissions, although the correlation coefficient between the estimation results of POI data and statistical data is not significantly improved compared with that of NPP-VIIRS data, the accuracy of the estimation results is significantly improved in terms of the spatial distribution; POI data has a significantly stronger ability to estimate industrial carbon emissions than nighttime light data. (3) From the spatial distribution structure of urban carbon emission, urban carbon emission presents a “V”-shaped distribution, with two high-value areas located in the central urban area and the industrial zone in the suburbs. This study confirms that POI data is a potential and promising data source for accurately modeling different types of carbon emissions and will help support low-carbon city management and energy allocation optimization.

An Adaptive Method for the Estimation of Snow-Covered Fraction with Error Propagation for Applications from Local to Global Scales

Snow can cover over 50% of the landmass in the Northern Hemisphere and has been labelled as an Essential Climate Variable by the World Meteorological Organisation. Currently, continental and global snow cover extent is primarily monitored by optical satellite sensors. There are, however, no large-scale demonstrations for methods that (1) use all the spectral information that is measured by the satellite sensor, (2) estimate fractional snow and (3) provide a pixel-wise quantitative uncertainty estimate. This paper proposes a locally adaptive method for estimating the snow-covered fraction (SCF) per pixel from all the spectral reflective bands available at spaceborne sensors. In addition, a comprehensive procedure for root-mean-square error (RMSE) estimation through error propagation is given. The method adapts the SCF estimates for shaded areas from variable solar illumination conditions and accounts for different snow-free and snow-covered surfaces. To test and evaluate the algorithm, SCF maps were generated from Sentinel-2 MSI and Landsat 8 OLI data covering various mountain regions around the world. Subsequently, the SCF maps were validated with coincidentally acquired very-high-resolution satellite data from WorldView-2/3. This validation revealed a bias of 0.2% and an RMSE of 14.3%. The proposed method was additionally tested with Sentinel-3 SLSTR/OLCI, Suomi NPP VIIRS and Terra MODIS data. The SCF estimations from these satellite data are consistent (bias less than 2.2% SCF) despite their different spatial resolutions.

Modelling spatiotemporal carbon dioxide emission at the urban scale based on DMSP-OLS and NPP-VIIRS data: A case study in China

Carbon emissions from urban areas are pivotal in meeting emission reduction targets. While carbon emissions in the coastal areas of urban China have been clarified, those for central cities in the west remain limited, despite rapid economic development likely raising them. Here, we developed a model to estimate carbon emissions from land under construction in urban areas of Chengdu, China, using remote sensing data and carbon emission statistics. The spatial distribution of carbon emissions was quantified from spatial analyses using a geographic information system (GIS). The feasibility of the proposed model for estimating urban carbon emissions was supported by the high R2 for estimated and statistical carbon emissions (>0.87) and the F-test (F-statistic >112.59). During 2000–2015, carbon emissions from construction land increased substantially (by 19.09 Mg C), with emissions increasing from core cities and beyond. Carbon emissions clearly differed among construction areas in Chengdu's districts, adjacent counties, and outer counties. The results of this study could help guide the development of policies and plans for managing carbon emissions, emission density, and urban sustainability.

Consistent nighttime light time series in 1992–2020 in Northern Africa by combining DMSP-OLS and NPP-VIIRS data

ABSTRACT Human activities modulate the impact of environmental forcing in general and of climate in particular. Information on the spatial and temporal patterns of human activities is in high demand, but scarce in sparsely populated and data-poor regions such as Northern Africa. The intensity and spatial distribution of nighttime lights provide useful information on human activities and can be observed by space-borne imaging radiometers. Our study helps to bridge the gap between the DMSP-OLS data available until 2013 and the NPP-VIIRS data available since 2013. The approach to calibrate the OLS data includes three steps: a) inter-calibrate the OLS DN data acquired by different sensors in 1992–2013; b) calibrate the OLS DN data using VIIRS data in 2013; c) generate synthetic OLS radiance data by degrading the VIIRS data in 2013–2020. We generated a) a time series of calibrated OLS nighttime light radiance data (1992–2013); b) mean annual VIIRS radiance on stable lights at the OLS spatial resolution for 2013–2020; c) synthetic OLS radiance data generated using VIIRS radiance data degraded to match the radiometric specifications of OLS for 2013–2020. The evaluation of these data products in 2013 documented their accuracy and consistency.

Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data to Evaluate Poverty in Southwestern China

Poverty alleviation is one of the most important tasks facing human social development. It is necessary to make accurate monitoring and evaluations for areas with poverty to improve capability of implementing poverty alleviation policies. Here, this study introduced nighttime light (NTL) data to estimate county-level poverty in southwest China. First, this study used particle swarm optimization-back propagation hybrid algorithm to explore the potential relationship between two NTL data (the Defense Meteorological Satellite Program’s Operational Line Scan System data and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite data). Then, we integrated two NTL data at the pixel level to establish a consistent time-series of NTL dataset from 2000 to 2019. Next, an actual comprehensive poverty index (ACPI) was employed as an indicator of multidimensional poverty at county level based on 11 socioeconomic and natural variables, and which could be the reference to explore the poverty evaluation using NTL data. Based on the correlation between the ACPI and NTL characteristic variables, a poverty evaluation model was developed to evaluate the poverty situation. The result showed the great matching relationship between DMSP-OLS and NPP-VIIRS data (R2 = 0.84). After calibration, the continuity and comparability of DMSP-OLS data were significantly improved. The integrated NTL data also reflected great consistency with socioeconomic development (r = 0.99). The RMSE between ACPI and the estimated comprehensive poverty index (ECPI) based on the integrated NTL data is approximately 0.19 (R2 = 0.96), which revealed the poverty evaluation model was feasible and reliable. According to the ECPI, we found that the magnitude of poverty eradication increased in southwest China until 2011, but slowed down from 2011 to 2019. Regarding the spatial scale, geographic barriers are a key factor for poverty, with high altitude and mountainous areas typically having a high incidence of poverty. Our approach offers an effective model for evaluation poverty based on the NTL data, which can contribute a more reliable and efficient monitoring of poverty dynamic and a better understanding of socioeconomic development.

Evaluating the Ability of NOAA-20 Monthly Composite Data for Socioeconomic Indicators Estimation and Urban Area Extraction

The new visible infrared imaging radiometer suite (VIIRS) onboard the National Oceanic and Atmospheric Administration-20 satellite (NOAA-20) is capable of imaging the Earth during both daytime and nighttime. The NOAA-20 VIIRS’ day/night band (DNB) enables a new generation of nighttime imaging applications. However, few studies investigated the ability of NOAA-20 DNB nighttime light data in modeling socioeconomic indicators [such as the gross domestic product (GDP) and electric power consumption (EPC)] and extracting urban areas. In this article, we first used a simple linear regression model to investigate the potential of NOAA-20 nighttime light data for estimating GDP and EPC at multiple scales; we then extracted urban areas from NOAA-20 nighttime light data by using support vector machines. Taking mainland China as the study area, we found that the correlation coefficient of determination <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> between NOAA-20 nighttime light data and GDP/EPC at both provincial and prefectural scales are higher than 0.7. By comparing the results with NPP-VIIRS nighttime light data, similar <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> values were obtained at both two scales, indicating that NOAA-20 nighttime light data and NPP-VIIRS data are comparable in estimating socioeconomic indicators. Moreover, NOAA-20 also shows a similar detection ability with NPP-VIIRS in extracting urban areas. This article demonstrated that NOAA-20 and NPP-VIIRS are comparable in economic statistics estimation and urban area extraction. The NOAA-20 nighttime light data can be a useful data source for enlightening more applications in the fields of socioeconomic and urban studies.

Detection of County Economic Development Using LJ1-01 Nighttime Light Imagery: A Comparison with NPP-VIIRS Data.

Nighttime light (NTL) remote sensing data have been widely used to derive socioeconomic indicators at the national and regional scales to study regional economic development. However, most previous studies only chose a single measurement indicator (such as GDP) and adopted simple regression methods to investigate the economic development of a certain area based on DMSP-OLS or NPP-VIIRS stable NTL data. The status quo shows the problems of using a single evaluation index—it has a low evaluation precision. The LJ1-01 satellite is the first dedicated NTL remote sensing satellite in the world, launched in July 2018. The data provided by LJ1-01 have a higher spatial resolution and fewer blooming phenomena. In this paper, we compared the accuracy of the LJ1-01 data and NPP-VIIRS data in detecting county-level multidimensional economic development. In three provinces in China, namely, Hubei, Hunan and Jiangxi, 20 socioeconomic parameters were selected from the following five perspectives: economic conditions, people’s livelihood, social development, public resources and natural vulnerability. Then, a County-level Economic Index (CEI) was constructed to evaluate the level of multidimensional economic development, with the spatial pattern of the multidimensional economic development also identified across the study area. The present study adopted the random forest (RF) and linear regression (LR) algorithms to establish the regression model individually, and the results were evaluated by cross-validation. The results show that the RF algorithm greatly improves the accuracy of the model compared with the LR algorithm, and thus is suitable for the study of NTL data. In addition, a better determinate coefficient (R2) based on the LJ1-01 data (0.8168) was obtained than that from the NPP-VIIRS data (0.7245) in the RF model, which reflects that the LJ1-01 data offer better potential in the evaluation of socioeconomic parameters and can be used to identify, both accurately and efficiently, multidimensional economic development at the county level.

Amazon Fire Monitoring and Analysis Based on Multi-source Remote Sensing Data

In August 2019, a large-scale fire broke out in the Amazon rainforest, bringing serious harm to the ecosystem and human beings. In order to accurately monitor the dynamic change of forest fire in Amazon rainforest and analyse the impact of fire spreading and extinction on the environment, firstly, based on NPP VIIRS data covering the Amazon fire area, the sliding window threshold method is adopted to extract the fire point, and the cause of fire change is monitored and analysed according to the time series. Secondly, based on the time series of CALIPSO data, the vertical distribution changes of atmospheric pollutants in the amazon fire area are analysed, and the comprehensive analysis is carried out by combining NPP VIIRS data. The experimental results show that only NPP VIIRS data is used to predict the fire, and the combination of CALIPSO data can better monitor the forest fire and predict the fire development trend. The combination of optical image and laser radar has greater advantages in dynamic fire monitoring and fire impact analysis. The method described in this paper can provide basic data reference for real-time and accurate prediction of forest fires and provide new ideas for dynamic fire monitoring.

Evaluation of Luojia 1-01 nighttime light imagery for impervious surface detection: A comparison with NPP-VIIRS nighttime light data

Impervious surface detection is significant to urban dynamic monitoring and environment management. One of the most effective approaches to evaluating the impervious surface is the use of nighttime light imagery. However, little work on this subject was carried out with the new generation nighttime light data from Luojia 1-01 satellite, which has a finer spatial resolution than the predecessors such as the nightlight data from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite. Therefore, this study conducted the first investigation of the capacity of Luojia 1-01 nighttime light data in detecting the extent and degree of impervious surfaces. Focusing on three cities of Beijing, Shanghai, and Guangzhou, several maps of the spatial extent of impervious surface areas were first extracted from two types of nighttime lights data (Luojia 1-01 and NPP-VIIRS data) by applying a dynamic threshold segmentation method. Meanwhile, a series of polynomial regression models were adopted to estimate the relation between imperiousness degree and light intensity. The results compared with the reference data derived from Landsat 8 Operational Land Imager (OLI) show that Luojia 1-01 data can produce a more precise map of the spatial extent of impervious surfaces than NPP-VIIRS data owing to the finer spatial resolution and the wider measurement range. Nevertheless, Luojia 1-01 data failed to provide reliable estimates of the imperviousness degree in comparison with NPP-VIIRS data as this nighttime light imagery with finer spatial resolution can better discriminate the surfaces that have the same imperviousness degree but are illuminated with different light intensities, consequently resulting in a weak correlation between imperviousness degree and light intensity. The over- and under-estimates of imperviousness degree suggested an increase in spatial resolution of nightlight imagery does not always improve the accuracy and reliability of nighttime light-based estimations. These study results confirmed that Luojia 1-01 nightlight imagery is a potential and promising data source for mapping the spatial extent of impervious surface areas, but difficult to accurately estimate the imperviousness degree. Future research may improve the accuracy of imperviousness degree estimation by integrating the Luojia 1-01 nightlight imagery with other useful data sources.

Measurement of blooming effect of DMSP-OLS nighttime light data based on NPP-VIIRS data

ABSTRACTNighttime light (NTL) imagery offers important data for socio-economic research since it depicts human activities at night. Blooming effect is one of the main problems of DMSP-OLS NTL data, but the problem has been largely mitigated in NPP-VIIRS data. Blooming effect smooths light intensity through diffusing the brightness from the bright area to the dark areas. This effect could be quantified by the alteration of spatial heterogeneity in light intensity which can be represented by spatial texture indices. Taking NPP-VIIRS data as the standard, this study calculates relative difference of spatial texture indices between NPP-VIIRS and DMSP-OLS images to measure the blooming effect of DMSP-OLS data at pixel scale in mainland China. Results show that within the real urban area, spatial texture indices of DMSP-OLS pixels are generally smaller than that of NPP-VIIRS, suggesting that blooming effect underestimates spatial heterogeneity and it is more serious towards to urban centres where nighttime light is stronger. DMSP-OLS underestimates 90% spatial heterogeneity when NPP-VIIRS value >10. On the contrary, in the region outside real urban area, spatial texture indices of DMSP-OLS data are greater than that of NPP-VIIRS, suggesting that blooming effect introduces fake spatial heterogeneity outside the real urban area.Abbreviations: NTL: Nighttime light; DMSP: Defense Meteorological Satellite Program; OLS: Operational Linescan System; NPP: National Polar-orbiting Partnership; VIIRS: Visible Infrared Imaging Radiometer Suite; NOAA: The National Oceanic and Atmospheric Administration; NGDC: The National Geophysical Data Center; NCV: Normalized coefficient of variation; DN: Digital number

Residential carbon dioxide emissions at the urban scale for county-level cities in China: A comparative study of nighttime light data

Abstract Carbon emissions from residential energy consumption in urban areas play a pivotal role in meeting emission targets. It is more beneficial to decomposing the emission reduction target to subnational units. This study aims to map urban residential carbon emissions at a finer spatial resolution to offer reference to disaggregating the carbon-reduction targets down to each sub-unit. With the launch of Suomi National Polar-orbiting Partnership (NPP) satellite, the day-night band of Visible Infrared Imaging Radiometer Suite (VIIRS) onboard represents a major advancement compared with Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) in terms of radiometric accuracy, spatial resolution and geometric quality. Therefore, the useful proxy was utilized to model the spatial distribution of residential carbon emissions at the urban scale for county-level cities in China. Firstly, the dynamic optimal threshold method was used to extract urban built-up areas based on NPP-VIIRS data and the statistical data. Secondly, comparison was conducted between the NPP-VIIRS data and both original and saturation-corrected DMSP-OLS data to explore the performance of NPP-VIIRS data. Finally, the spatial characteristics were analyzed in detail at the regional, provincial and prefectural level, respectively. The results show that the NPP-VIIRS data performed better in both statistic regression and spatial comparison. The spatial patterns indicated that there was an obvious north-south differentiation, especially the carbon emission density (defined as carbon emissions per grid), which was significantly higher in Northeastern China than that of other regions. The climate elements positively contributed to the increase of carbon emissions in residential sector, especially stimulating emissions related to building heating.

基于6S模型NPP VIIRS数据快速大气校正及精度评估

基于6S模型NPP VIIRS数据快速大气校正及精度评估

Spatial Downscaling of Suomi NPP–VIIRS Image for Lake Mapping

Capturing the dynamics of a lake-water area using remotely sensed images has always been an essential task. Most of the fine spatial resolution data are unsuitable for this purpose because of their low temporal resolution and limited scene coverage. A Visible Infrared Imaging Radiometer Suite on board the Suomi National Polar-orbiting Partnership (Suomi NPP–VIIRS) is a newly-available and appropriate sensor for monitoring large lakes due to its frequent revisits and wide swath (more than 3000 km). However, it provides visible and infrared images at relatively coarse spatial resolutions, which would sometimes hamper the accurate mapping of lake shorelines. This study, therefore, proposes a two-step downscaling method that combines spectral unmixing and subpixel mapping to produce a finer resolution lake map from NPP–VIIRS imagery, which is then applied to delineate the shorelines of five plateau lakes in Yunnan Province, as well as the shoreline dynamics of Poyang Lake at three separate times. A newly published global water dynamic dataset is employed in this study to improve the downscaling method. Results suggest that the proposed method can generate a finer resolution lake map that exhibits more details of the shoreline than hard classification. The downscaling results of the Suomi NPP–VIIRS generally achieve higher than 75% accuracy, while the downscaling results of a Landsat-simulated fraction map could have accuracy higher than 85%. This reveals that errors and uncertainties exist in both procedures, but mainly come from the spectral unmixing procedure which retrieves water fractions from NPP–VIIRS data.

Spatialization of GDP in Beijing using NPP-VIIRS data

Spatialization of GDP in Beijing using NPP-VIIRS data

Poverty Evaluation Using NPP-VIIRS Nighttime Light Composite Data at the County Level in China

Poverty has appeared as one of the long-term predicaments facing development of human society during the 21st century. Estimation of regional poverty level is a key issue for making strategies to eliminate poverty. This paper aims to evaluate the ability of the nighttime light composite data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) carried by the Suomi National Polar-orbiting Partnership (NPP) Satellite in estimating poverty at the county level in China. Two major experiments are involved in this study, which include 1) 38 counties of Chongqing city and 2) 2856 counties of China. The first experiment takes Chongqing as an example and combines 10 socioeconomic variables into an integrated poverty index (IPI). IPI is then used as a reference to validate the accuracy of poverty evaluation using the average light index (ALI) derived from NPP-VIIRS data. Linear regression and comparison of the class ranks have been employed to verify the correlation between ALI and IPI. The results show a good correlation between IPI and ALI, with a coefficient of determination ( $R^2$ ) of 0.8554, and the class ranks of IPI and API show relative closeness at the county level. The second experiment examines all counties in China and makes a comparison between ALI values and national poor counties (NPC). The comparison result shows a general agreement between the NPC and the counties with low ALI values. This study reveals that the NPP-VIIRS data can be a useful tool for evaluating poverty at the county level in China.