Infrared Imaging Radiometer Suite Research Articles

Inversion and Analysis of Global Ocean Chlorophyll-a Concentration Based on Temperature Zoning

In recent years, the frequent occurrence of eutrophication problems in water bodies has been caused by changes in the climate environment and overexploitation of natural resources by humans. Chlorophyll-a, as a key indicator for water body assessment, plays an important role in eutrophication research and has a profound impact on the global biogeochemical cycle of the climate process. Studies have shown that temperature can directly or indirectly affect the concentration of chlorophyll-a by influencing the growth of algae and water quality indicators in water bodies. Considering the temperature factor in the inversion of chlorophyll-a concentration is a novel research approach. Based on the influence of temperature on chlorophyll-a concentration, we propose the idea of inverting global ocean chlorophyll-a concentration based on temperature zoning. Using monthly average remote sensing reflectance data from VIIRS (Visible and Infrared Imaging Radiometer Suite), combined with the results of temperature zoning, the OC3V(SST) model was constructed to invert the monthly average chlorophyll-a concentration in the global ocean in October 2018. The OC3V(SST) model has been validated by applying it to the remaining 11 months of January, April, July, and October in 2017, 2018, and 2019, as well as the entire 31-day dataset of October 2018. The results indicate that temperature zonation can significantly improve the inversion accuracy of chlorophyll-a and further explore the spatial distribution patterns of global chlorophyll-a concentrations across various temperature ranges based on monthly averages from the global ocean. Additionally, the study investigates the continuity issues of various models and the correlation between temperature and chlorophyll-a.

VIIRS Night-Time Light Image in the Spatial Structure Identification of Banyuwangi Regency

A spatial structure is a hierarchical arrangement of settlement centers and a network system of infrastructure and facilities that support the community’s socio-economic activities. Spatial structure is important because the city’s rapid growth requires regional service center planning. In general, the method for determining the spatial structure uses the Centrality Index (IS) with a variable number of facilities, infrastructure, and people to obtain a hierarchical arrangement of regional service centers. However, this method is often constrained by data limitations, so in this study remote sensing technology was used through night light imagery in identifying potential centers. This study used the Visible and Infrared Imaging Radiometer Suite (VIIRS) sensor, one of the sensors carried by the Suomi-NPP (National Polar-orbiting Operational Environmental Satellite System Preparatory Project) satellite. Identification is done by overlaying the night light image with the existing spatial structure to produce potential centers. Based on the analysis results, eight classifications are based on the light emission interval class, where the higher the class, the higher the light emission value, which indicates the potential for a new center. The Pesanggaran sub-district is in class eight, the Tegaldimo sub-district is in class six, and the Banyuwangi sub-district is in class one.

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.

Large‐scale human celebrations increase global light pollution

Abstract Culturally dependent human social behaviours involving artificial light usage can potentially affect light pollution patterns and thereby impact the night‐time ecology in populated areas, although to date this has not been examined globally. By analysing continuous (monthly), highly resolved, spatially explicit data on global night lights (Visible and Infrared Imaging Radiometer Suite–Day/Night Band‐VIIRS‐DNB; 2014–2019) with circular statistical techniques, we evaluated whether macro‐cultural activities involving social aggregations and the use of artificial lights shape annual lighting patterns globally. Scheduled routines associated with cultural‐specific festivities appear to be important drivers of observed seasonal patterns in urban night‐time lights. For instance, the display of Christmas lights between Christmas and Epiphany Day celebrations (December–January) coincides with the annual peak in urban night‐time light intensity in Christian countries. Analogously, night celebrations during the Holy Month of Ramadam (from May to July) or the month‐long period of Karthika Masam (from October to November) fits with annual night light peaks in Muslim and Hindu countries. Annual peaks of urban light intensity in China and Vietnam also match with Chinese and Vietnamese (Tê't) New Year celebrations (January–February). In contrast, predominantly Buddhist countries, which do not have such prominent and prolonged celebrations involving artificial lights, show a relatively uniform distribution of night light peaks throughout the annual cycle. Social behaviour and sociocultural contexts help explain how people modify the global nightscape and contribute to light pollution globally. Understanding the cultural contexts responsible for peaks in artificial light usage is an important first step if humans are to mitigate any deleterious effects associated with global increases in night‐time light pollution. Read the free Plain Language Summary for this article on the Journal blog.

Analyzing the Spatially Heterogeneous Relationships between Nighttime Light Intensity and Human Activities across Chongqing, China

Nighttime light (NTL) intensity is highly associated with the unique footprint of human activities, reflecting the development of socioeconomic and urbanization. Therefore, better understanding of the relationship between NTL intensity and human activities can help extend the applications of NTL remote sensing data. Different from the global effect of human activities on NTL intensity discussed in previous studies, we focused more attention to the local effect caused by the spatial heterogeneity of human activities with the support of the multiscale geographically weighted regression (MGWR) model in this study. In particular, the Suomi National Polar Orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) NTL data within Chongqing, China were taken as example, and the point of interest (POI) data and road network data were adopted to characterize the intensity of human activity type. Our results show that there is significant spatial variation in the effect of human activities to the NTL intensity, since the accuracy of fitted MGWR (adj.R2: 0.86 and 0.87 in 2018 and 2020, respectively; AICc: 4844.63 and 4623.27 in 2018 and 2020, respectively) is better than that of both the traditional ordinary least squares (OLS) model and the geographically weighted regression (GWR) model. Moreover, we found that almost all human activity features show strong spatial heterogeneity and their contribution to NTL intensity varies widely across different regions. For instance, the contribution of road network density is more homogeneous, while residential areas have an obviously heterogeneous distribution which is associated with house vacancy. In addition, the contributions of the commercial event and business also have a significant spatial heterogeneity distribution, but show a distinct decrement when facing the COVID-19 pandemic. Our study successfully explores the relationship between NTL intensity and human activity features considering the spatial heterogeneity, which aims to provide further insights into the future applications of NTL data.

Spatial Expansion and Correlation of Urban Agglomeration in the Yellow River Basin Based on Multi-Source Nighttime Light Data

The Chinese government proposed a major national strategy for ecological protection and high-quality development in the Yellow River Basin. The Framework of the Plan for Ecological Protection and High-Quality Development of the Yellow River Basin proposes building a dynamic development pattern characterized by “one axis, two regions and five poles” in the Yellow River Basin with high-quality and high-standard urban agglomerations along the Yellow River. The urban agglomeration is the economic growth pole of the Yellow River Basin and the main carrier of the population and productivity. This study integrates DMSP/OLS (Defense Meteorological Satellite Program/Operational Linescan System) and NPP/VIIRS (Suomi National Polar-Orbiting Partnership/Visible Infrared Imaging Radiometer Suite) night light remote sensing data from 2000 to 2020 and uses methods such as spatial expansion measurement, the center of gravity offset, urban primacy, and the gravity model to study the spatial expansion and correlation characteristics of five urban agglomerations. The results show that: (1) From 2000 to 2020, urban agglomeration in the Yellow River Basin continued to expand, and the area increased by 6.4 times. The total amount of nighttime lights in the city presents a spatial distribution pattern that is high in the east and low in the west. (2) The expansion centers of the five major urban agglomerations all shifted. The centers of gravity of the Shandong Peninsula urban agglomeration, the Jiziwan urban agglomeration of the Yellow River, the Guanzhong Plain urban agglomeration, and the Lanzhou–Xining urban agglomeration all shifted westward, while the center of gravity of the Central Plains urban agglomeration shifted to the southeast. (3) Qingdao, Zhengzhou, Xi’an and Lanzhou are the primate cities of the four urban agglomerations of the Shandong Peninsula, Central Plains, Guanzhong Plain, and Lanzhou–Xining, respectively. The primate city in the Jiziwan urban agglomeration of the Yellow River was changed from Taiyuan to Yinchuan and then to Yulin. (4) The density of the gravitational network of the urban agglomeration in the Yellow River Basin and the distribution of the maximum gravitational line show the spatial differentiation characteristics of being dense in the east and sparse in the west.

Analysis on process of temporal and spatial evolution of urban built-up area expansion in the Yellow River Basin.

Urban spatial expansion is known as an important indicator of urbanization. In order to provide a reference for urban spatial expansion in the future high-quality development strategy of the Yellow River Basin (YB) cities in China, it is necessary to identify and calculate urban spatial expansion patterns. For this reason, we provide a "Spatiotemporal pattern-Center of gravity migrationt-Expansion pattern" solution to identify and calculate urban spatial expansion patterns in the YB. More specifically, 78 prefecture-level cities in the YB were selected as the subjects of the study, using the Defense Meteorological Satellite Program/Operational Line Scan System (DMSP/OLS) and the National Polarimetric Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) nighttime light data (NTL), together with the center of gravity shift and common edge detection models, to identify the YB urban expansion patterns from 2000–2018. The results suggest that: (1) on the spatial pattern, there is a obvious difference in the expansion intensity and growth rate of the urban built-up (UB) areas of cities in the upper and middle reaches of YB. In addition, there are also certain differences between the expansion patterns of provincial capital cities and non-capital cities; (2) The UB areas of YB has steadily expand from 3,500 km2 in 2000 to 10,600 km2 in 2018, amongst which the expansion of provincial capital cities is the most obvious 1919 km2; (3) Interestingly it is also discovered that urban expansion in Qinghai Province, the sourceland of the YB, takes place in a diffuse way, with the shifting of the centre of gravity for four types of total area, net increase in area, rate of growth and intensity of expansion followed a "northwest to southeast" tendency of development.

City- and county-level spatio-temporal energy consumption and efficiency datasets for China from 1997 to 2017

Understanding the evolution of energy consumption and efficiency in China would contribute to assessing the effectiveness of the government’s energy policies and the feasibility of meeting its international commitments. However, sub-national energy consumption and efficiency data have not been published for China, hindering the identification of drivers of differences in energy consumption and efficiency, and implementation of differentiated energy policies between cities and counties. This study estimated the energy consumption of 336 cities and 2,735 counties in China by combining Defense Meteorological Satellite Program/Operational Line-scan System (DMSP/OLS) and Suomi National Polar-Orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) satellite nighttime light data using particle swarm optimization-back propagation (PSO-BP). The energy efficiency of these cities and counties was measured using energy consumption per unit GDP and data envelopment analysis (DEA). These data can facilitate further research on energy consumption and efficiency issues at the city and county levels in China. The developed estimation methods can also be used in other developing countries and regions where official energy statistics are limited.

Assessing Potential Climatic and Human Pressures in Indonesian Coastal Ecosystems Using a Spatial Data-Driven Approach

Blue carbon ecosystems are key for successful global climate change mitigation; however, they are one of the most threatened ecosystems on Earth. Thus, this study mapped the climatic and human pressures on the blue carbon ecosystems in Indonesia using multi-source spatial datasets. Data on moderate resolution imaging spectroradiometer (MODIS) ocean color standard mapped images, VIIRS (visible, infrared imaging radiometer suite) boat detection (VBD), global artificial impervious area (GAIA), MODIS surface reflectance (MOD09GA), MODIS land surface temperature (MOD11A2), and MODIS vegetation indices (MOD13A2) were combined using remote sensing and spatial analysis techniques to identify potential stresses. La Niña and El Niño phenomena caused sea surface temperature deviations to reach −0.5 to +1.2 °C. In contrast, chlorophyll-a deviations reached 22,121 to +0.5 mg m−3. Regarding fishing activities, most areas were under exploitation and relatively sustained. Concerning land activities, mangrove deforestation occurred in 560.69 km2 of the area during 2007–2016, as confirmed by a decrease of 84.9% in risk-screening environmental indicators. Overall, the potential pressures on Indonesia’s blue carbon ecosystems are varied geographically. The framework of this study can be efficiently adopted to support coastal and small islands zonation planning, conservation prioritization, and marine fisheries enhancement.

Evaluation of GSICS Correction for COMS/MI Visible Channel Using S-NPP/VIIRS

The Global Space-based Inter-Calibration System (GSICS) is an international partnership sponsored by World Meteorological Organization (WMO) to continue and improve climate monitoring and to ensure consistent accuracy between observation data from meteorological satellites operating around the world. The objective for GSICS is to inter-calibration from pairs of satellites observations, which includes direct comparison of collocated Geostationary Earth Orbit (GEO)-Low Earth Orbit (LEO) observations. One of the GSICS intercalibration methods, the Ray-matching technique, is a surrogate approach that uses matched, co-angled and colocated pixels to transfer the calibration from a well calibrated satellite sensor to another sensor. In Korea, the first GEO satellite, Communication Ocean and Meteorological Satellite (COMS), is used to participate in the GSICS program. The National Meteorological Satellite Center (NMSC), which operated COMS/MI, calculated the Radiative Transfer Model (RTM)-based GSICS coefficient coefficients. The L1P reproduced through GSICS correction coefficient showed lower RMSE and Bias than L1B without GSICS correction coefficient applied. The calculation cycles of the GSICS correction coefficients for COMS/MI visible channel are provided annual and diurnal (2, 5, 10, 14-day), but long-term evaluation according to these cycles was not performed. The purpose of this paper is to perform evaluation depending on the annual/diurnal cycles of COMS/MI GSICS correction coefficients based on the ray-matching technique using Suomi-NPP/Visible Infrared Imaging Radiometer Suite (VIIRS) data as reference data. As a result of evaluation, the diurnal cycle had a higher coincidence rate with the reference data than the annual cycle, and the 14-day diurnal cycle was the most suitable for use as the GSICS correction coefficient.

Detection of Fire Smoke Plumes Based on Aerosol Scattering Using VIIRS Data over Global Fire-Prone Regions

Smoke from fires significantly influences climate, weather, and human health. Fire smoke is traditionally detected using an aerosol index calculated from spectral contrast changes. However, such methods usually miss thin smoke plumes. It also remains challenging to accurately separate smoke plumes from dust, clouds, and bright surfaces. To improve smoke plume detections, this paper presents a new scattering-based smoke detection algorithm (SSDA) depending mainly on visible and infrared imaging radiometer suite (VIIRS) blue and green bands. The SSDA is established based on the theory of Mie scattering that occurs when the diameter of an atmospheric particulate is similar to the wavelength of the scattered light. Thus, smoke commonly causes Mie scattering in VIIRS blue and green bands because of the close correspondence between smoke particulate diameters and the blue/green band wavelengths. For developing the SSDA, training samples were selected from global fire-prone regions in North America, South America, Africa, Indonesia, Siberia, and Australia. The SSDA performance was evaluated against the VIIRS aerosol detection product and smoke detections from the ultraviolet aerosol index using manually labeled fire smoke plumes as a benchmark. Results show that the SSDA smoke detections are superior to existing products due chiefly to the improved ability of the algorithm to detect thin smoke and separate fire smoke from other surface types. Moreover, the SSDA smoke distribution pattern exhibits a high spatial correlation with the global fire density map, suggesting that SSDA is capable of detecting smoke plumes of fires in near real-time across the globe.

A Saturated Light Correction Method for DMSP-OLS Nighttime Stable Light Data by Remote and Social Sensing Data

The defense meteorological satellite program- operational line-scan system nighttime stable light (NTL) data have been widely used to evaluate the intensity of human activities. However, the sensor's defects lead to the saturation phenomenon, which greatly limits the reliability of the research results based on NTL data. Thus, this article has attempted to propose a new spectral index—the points of interest, road network and EVI adjusted NTL index (PREANTLI) to effectively correct saturated pixels. To evaluate the desaturation effect, the PREANTLI was compared with existing saturation correction data across three aspects: the capacity to display differences in light intensities; the similarity with National Polar-orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) data; and the capacity to estimate the gross domestic product (GDP) and electric power consumption (EPC). The results showed that the PREANTLI can more easily identify light intensity differences than other indexes. The PREANTLI presents a strong linear correlation with the NPP/VIIRS data and socioeconomic statistics (GDP and EPC) at provincial and municipal scales. It is worth noting that, on the pixel scale, the correlation between PREANTLI and NPP/VIIRS ( R 2 = 0.63) is far higher than that of the other two existing saturation correction index whose R2 are both below 0.45. Thus, the PREANTLI can be considered as a reasonable index that is not only easy to calculate but can also better alleviate light intensity saturation and emphasize light differences within a city than other indexes.

Mapping the Population Density in Mainland China Using NPP/VIIRS and Points-Of-Interest Data Based on a Random Forests Model

Understanding the spatial distribution of populations at a finer spatial scale has important value for many applications, such as disaster risk rescue operations, business decision-making, and regional planning. In this study, a random forest (RF)-based population density mapping method was proposed in order to generate high-precision population density data with a 100 m × 100 m grid in mainland China in 2015 (hereafter referred to as ‘Popi’). Besides the commonly used elevation, slope, Normalized Vegetation Index (NDVI), land use/land cover, roads, and National Polar Orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS), 16,101,762 records of points of interest (POIs) and 2867 county-level censuses were used in order to develop the model. Furthermore, 28,505 township-level censuses (74% of the total number of townships) were collected in order to evaluate the accuracy of the Popi product. The results showed that the utilization of multi-source data (especially the combination of POIs and NPP/VIIRS data) can effectively improve the accuracy of population mapping at a finer scale. The feature importances of the POIs and NPP/VIIRS are 0.49 and 0.14, respectively, which are higher values than those obtained for other natural factors. Compared with the Worldpop population dataset, the Popi data exhibited a higher accuracy. The number of accurately-estimated townships was 19,300 (67.7%) in the Popi product and 16,237 (56.9%) in the Worldpop product. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were 14,839 and 7218, respectively, for Popi, and 18,014 and 8572, respectively, for Worldpop. The research method in this paper could provide a reference for the spatialization of other socioeconomic data (such as GDP).

Using ecosystem service supply and ecosystem sensitivity to identify landscape ecology security patterns in the Lanzhou-Xining urban agglomeration, China

The construction of an ecological security pattern (ESP) is an important way to ensure regional ecological security and to achieve sustainable regional development. It is also one of the hotspot topics of landscape ecology research. This paper identifies the ecological source through the evaluation of the ecosystem service and ecosystem sensitivity of the Lanzhou-Xining (Lan-Xi) urban agglomeration. The minimum cumulative resistance (MCR) model modified by night light data NPP/VIIRS (National Polar-orbiting Operational Environmental Satellite System Preparatory Project/Visible Infrared Imaging Radiometer Suite) was used to measure the relative resistance of the materials and energy circulation between the source areas, and to establish the resistance surface of the ecological source area expansion. Then ecological corridors were identified based on ecological sources and resistance surface. The ecological strategic node is the ecological fragile point in the ecological corridors. The ecological strategic node is identified with hydrological module by superimposing the “ridge line” of cumulative ecological resistance with the ecological corridor. Combined with ecological sources, corridors and strategic nodes, the ESP of the Lan-Xi urban agglomeration can be constructed. The ecological source of the Lan-Xi urban agglomeration accounts for 28.42% of the total area, most of which is distributed within Qinghai Province. The nature reserves in the area are all located within the ecological source area. A total of 41 potential ecological corridors have been identified in the study area. The total length of the potential corridors is 1201.03 km, comprising 23 source corridors and 18 radiation corridors. There are 30 strategic nodes identified in the Lan-Xi urban agglomeration. These locations are the most vulnerable areas of the ecological corridors. Ecological engineering should be applied in the construction of corridors. Affected by the ecological source, the potential ecological corridor extends from the northwest to the southeast, which is basically consistent with the direction trend of the mountains in the region.

Detection of City Integration Processes in Rapidly Urbanizing Areas Based on Remote Sensing Imagery

Since China’s reform and development commenced, in the context of rapid urbanization and coordinated regional development, Chinese cities with a close geographic proximity and social ties have gradually formed an integrated city development model. As a new phenomenon in China’s urbanization process, existing research on China’s integrated cities mainly focuses on typical case studies, and most research has been limited to literature reviews and theoretical analyses. The growing application of remote sensing technology in urbanization research in recent years has provided new opportunities for the analysis of city integration. Therefore, based on multi-spectral Landsat-8 and nighttime light images (SNPP/VIIRS, Suomi National Polar-orbiting Platform/Visible Infrared Imaging Radiometer Suite), this paper selects four of the most representative integrated cities with different backgrounds in China to analyze the land-use conversion, plot light fluctuation, and light gravity center shift in the boundary zone between cities. The results show that (1) Guangfo has the highest level of integration and urban expansion is mainly concentrated in the south-central part of the boundary area; (2) Guanshen’s level of integration is second to Guangfo’s and is mainly concentrated in the west; (3) HuSu’s integration is still in the initial stage and its increase in light intensity lags behind the expansion of building land during the study period; (4) although the light intensity and building land area increased significantly during the study period in Xixian, the overall development level of Xixian still lagged behind coastal cities due to the restriction of its geographical location. Our application results expand the data sources for integrated city research and the obtained results can potentially support decision-making and planning in the process of urban development.

Development of Geo-KOMPSAT-2A Algorithm for Sea-Ice Detection Using Himawari-8/AHI Data

Sea ice is an important meteorological factor affecting the global climate system, but it is difficult to observe in sea ice ground truth data because of its location mainly at high latitudes and in polar regions. Accordingly, sea-ice detection research has been actively conducted using satellites, since the 1970s. Polar-orbiting and geostationary satellites are used for this purpose; notably, geostationary satellites are capable of real-time monitoring of specific regions. In this paper, we introduce the Geo-KOMPSAT-2A (GK-2A)/Advanced Meteorological Imager (AMI) sea-ice detection algorithm using Japan Meteorological Agency (JMA) Himawari-8/Advanced Himawari Imager (AHI) data as proxy data. The GK-2A/AMI, which is Korea Meteorological Administration (KMA)’s next-generation geostationary satellite launched in December 2018 and Himawari-8/AHI have optically similar channel data, and the observation area includes East Asia and the Western Pacific. The GK-2A/AMI sea-ice detection algorithm produces sea-ice data with a 10-min temporal resolution, a 2-km spatial resolution and sets the Okhotsk Sea and Bohai Sea, where the sea ice is distributed during the winter in the northern hemisphere. It used National Meteorological Satellite Center (NMSC) cloud mask as the preceding data and a dynamic threshold method instead of the static threshold method that is commonly performed in existing sea-ice detection studies. The dynamic threshold methods for sea-ice detection are dynamic wavelength warping (DWW) and IST0 method. The DWW is a method for determining the similarity by comparing the pattern of reflectance change according to the wavelength of two satellite data. The IST0 method detects sea ice by using the correlation between 11.2-μm brightness temperature (BT11.2) and brightness temperature difference (BTD) [BT11.2–BT12.3] according to ice surface temperature (IST). In addition, the GK-2A/AMI sea-ice detection algorithm reclassified the cloud area into sea ice using a simple test. A comparison of the sea-ice data derived the GK-2A/AMI sea-ice detection algorithm with the S-NPP/visible infrared imaging radiometer suite (VIIRS) sea ice characterization product indicates consistency of 99.0% and inconsistency of 0.9%. The overall accuracy (OA) of GK-2A/AMI sea-ice data with the sea ice region of interest (ROI) data, which is constructed by photo-interpretation method from RGB images, is 97.2%.

Exploring Spatial and Temporal Connection Patterns among the Districts in Chongqing Based on Highway Passenger Flow

Investigating regional connections and their influencing factors from the perspective of “flow” space is one of the foundations of promoting regional development. In this article, the data we used includes actual highway passenger flow data, National Polar-orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) nighttime light remote sensing data, and socioeconomic data. We analyzed the spatial distribution pattern, connection intensity and spatial autocorrelation of highway passenger flow in Chongqing during the working day, weekend and May Day and revealed the influencing factors by means of a geographic detector. Three key conclusions resulted from this research. First, highway passenger flow in Chongqing districts exhibits spatial agglomeration that is clearly higher in western Chongqing than in eastern Chongqing and forms an obviously dual-core “star” structure, with the main urban area and Wanzhou serving as the core. Second, a factor detector notes that the nighttime light area index (0.9251, 0.9512, 0.9541) has the strongest explanatory power for the spatial differentiation of passenger traffic in Chongqing districts, which is the key factor. Third, interaction detection shows that the interaction between the two factors displays an enhancement effect at different times. The nighttime light area index shows the strongest explanatory power under the synergy of tourist attractions, which are 0.9850, 0.9903 and 0.9908. But the per capita GDP and highway mileage have the most obvious enhancement effect after interaction (0.9544, 0.9661, 0.9652). Therefore, in future planning and development, we should pay attention to cooperation and exchanges between districts and use the nighttime light area index as an important reference factor to provide a scientific basis for the construction of public transport and economic construction in Chongqing.

An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI).

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

Temporal variations of artificial nighttime lights and their implications for urbanization in the conterminous United States, 2013–2017

Artificial nighttime lights (NTL) generated by human activities offer a unique opportunity to understand urban environments. Although previous studies have widely used NTL images to map urban extent at multiple scales, it remains a challenging task to address how NTL respond exactly to urbanization and thus to map urbanization from NTL. In this study, using monthly Suomi National Polar-orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) NTL images between 2013 and 2017, we developed a method to decompose time-series NTL signal into annual and seasonal components. Further, we proposed an NTL-based indicator for the detection of impervious surfaces change (ISC) by integrating annual increment and seasonal variation of NTL brightness. The indicator was then used to identify ISC by using a thresholding method. The application of the methodology in the conterminous United States (CONUS) revealed a more rapid urbanization in the southern CONUS than the northern states and a northeastern-southwestern gradient of NTL seasonality. It was also found that NTL of November and December provided the most accurate characterization of urban extent for most areas in the CONUS. The detection of ISC in four representative regions (i.e. Dallas-Fort Worth-Arlington, greater Washington D.C., Denver-Aurora, and Atlanta) resulted in a moderate to high accuracy with the overall accuracy of ~80% and the Kappa value ranging from 0.56 to 0.73. Despite of this, the results showed a low accuracy of NTL-derived changing year of ISC (Kappa: 0.28) because of the existence of temporal inconsistency between NTL increase and ISC. The proposed method has the potential to timely map urban expansion at large geographical scales (e.g., continental and global) in a cost-efficient manner.

Exploring the stability and residual response versus scan angle effects in SNPP VIIRS sensor data record reflectance products using deep convective clouds

The reflective solar bands (RSB) of the Visible and Infrared Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership satellite are calibrated using solar diffuser and lunar observations. To evaluate the performance of the VIIRS RSB calibration, the deep convective clouds (DCC) are utilized as invariant targets to assess the stability of NASA’s VIIRS sensor data record (SDR) reflectance product for 10 moderate resolution bands (M-bands, M1 to M5 and M7 to M11) and three imagery resolution bands (I-bands, I1 to I3). All the frames along with each scan are divided into six aggregation zones for the scan angle. An empirical bidirectional reflectance distribution function (BRDF) correction is performed to SDR reflectances over DCC to reduce the scattering anisotropy in view and solar angles. The BRDF-corrected reflectances are used to assess the stability and to explore the residual RVS effects for selected bands. Results of this study show that after more than 6 years of on-orbit operation, the use of the prelaunch-based RVS still meets the radiometric requirement for the VIIRS RSB. Examination of the small but noticeable RVS residuals indicates they occur in a few shortest wavelength bands based on a time-dependent quadratic fit of the reflectance trends obtained at all scan angle zones. As the mission continues, continuing monitoring of on-orbit RVS stability and its effects on VIIRS SDR reflectance products are necessary with the DCC technique in the future VIIRS calibrations to ensure the quality of the RSB SDR products.