Visible Infrared Imaging Radiometer Research Articles

Application of multiplatform remote sensing data over East Asia Ocean: aerosol characteristics and aerosol types.

It is important to explore the characteristics and rules of atmospheric aerosol in the East Asian Sea for monitoring and evaluating atmospheric environmental quality. Based on Aerosol Robot Network (AERONET), Visible Infrared Imaging Radiometer (VIIRS), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data, the temporal and spatial variation characteristics and differences of aerosol parameters and types in the East Asian Sea were studied by using figure classification method (FIGCM), aerosol optical depth (AOD)440-Angstrom exponent (AE)440-870 method (AA1M), and AOD550-AE490-670 method (AA2M). The results show that the seasonal variation trend of aerosol characteristics and types is obvious in East Asia Sea. AOD, volume concentration (Cv), and aerosol effective radius (reff) in the Bohai-Yellow Sea and the Sea of Japan in autumn are lower than those in other seasons, and the occurrence frequency of ocean-type aerosols is high. Different from the Bohai-Yellow Sea and Sea of Japan, human activities in winter, summer, and autumn seriously affect the air quality in the East China Sea and South China Sea. Especially at the Taipei CWB site, from aerosol parameters and high biomass burning/urban industrial (BB/UI) aerosol, human activity is an important factor for high pollution at the Taipei CWB site. Aerosol types of AA1M, FIGCM, AA2M, and CALIPSO were compared at Anmyon and Yonsei University sites in the Bohai-Yellow Sea in March 2020. The results show that aerosol types based on threshold classification methods generally have higher mixed aerosol results, and the marine (MA) results of AA1M, FIGCM, and AA2M are close to the clean marine aerosol results of CALIPSO. Comparing the results of AA 2M and CALIPSO on a spatial scale, it is found that the clean marine aerosol proportion identified by CALIPSO (0.38, 0.48, 0.82) is consistent with the MA proportion identified by AA 2M (0.43, 0.46, 0.97) in the East China Sea, South China Sea, and Western Pacific Ocean.

Suitability of Sentinel-3 chlorophyll a products based on optical water types

ABSTRACT Analysis of the suitability of water colour products for the reliability of subsequent applications, or improvements of the algorithm at later stages, is important. We used three types of water bodies: Type 1 (Southern Ocean and Qinghai Lake) clean oceanic water bodies dominated by chlorophyll and clean inland water bodies dominated by chlorophyll and coloured dissolved organic matter; Type 2 (Taihu Lake and Chagan Lake) turbid inland water bodies dominated by suspended matter and chlorophyll and Type 3 (Xiaoxingkai Lake and Daxingkai Lake) turbid inland water bodies dominated by suspended matter. The accuracies of two Chl a concentration products data for synchronous Sentinel-3 OLCI Neural Network (NN) and ocean colour 4 band ratio (OC4Me) algorithms were verified using two metrics combined with in situ data (to compare the applicability of Chl a concentration algorithms for the three types of water bodies). The optimal Chl-a concentration algorithm was selected to analyse the spatial and temporal variation characteristics of monthly concentrations of Chl a. Based on the analysis, (1) the NN algorithm had better applicability to Type 1 water bodies ; the NN algorithm had better applicability to Type 2 water bodies, ; and both algorithms had poor applicability to Type 3 water bodies, (2) Limitations: An overall low derivation value was obtained for the Sentinel-3 OLCI NN algorithm with water concentrations of Chl a, higher than 25 mg m−3. The OC4Me Chl a concentration product only applies to the concentrations of Chl a in Type 2 during the summer. (3) When the Sentinel-3 OLCI Chl a concentration product was compared with the moderate-resolution imaging spectroradiometer (MODIS) and visible infrared imaging radiometer (VIIRS) Chl a concentration products, the distribution trends of the Chl a concentrations were consistent. Furthermore, the Sentinel-3 OLCI Chl a concentration product was more dominant in spatial resolution.

Estimating Nighttime PM2.5 Concentration in Beijing Based on NPP/VIIRS Day/Night Band

Nighttime PM2.5 detection by remote sensing can expand understanding of PM2.5 spatiotemporal patterns due to wider coverage compared to ground monitors and by supplementing traditional daytime detection. However, using remote sensing data to invert PM2.5 at night is still challenging. Compared with daytime detection, which operates on sunlight, nighttime detection operates on much weaker moonlight and artificial light sources, complicating signal extraction. Moreover, as the attempts to sense PM2.5 remotely using satellite data are relatively recent, the existing nighttime models are still not mature, overlooking many important factors such as stray light, seasonality in meteorological effects, and observation angle. This paper attempts to improve the accuracy of nighttime PM2.5 detection by proposing an inversion model that takes these factors into consideration. The Visible Infrared Imaging Radiometer Suite/Day/Night Band (VIIRS/DNB) on board the polar-orbiting Suomi National Polar-orbiting Partnership (Suomi NPP) and National Oceanic Atmospheric Administration-20 (NOAA-20) was used to establish a nighttime PM2.5 inversion model in the Beijing area from 1 March 2018 to 28 February 2019. The model was designed by first studying the effects of these factors through a stepwise regression, then building a multivariate regression model to compensate for these effects. The results showed that the impact of satellite viewing zenith angle (VZA) was strongest, followed by seasonality and moonlight. Total accuracy was measured using correlation coefficient (R) compared to ground measurements, achieving 0.87 over the urban area and 0.74 over the suburbs. Specifically, the proposed method works efficiently at subsatellite points, which in this case correspond to VZA from 0 and 5°. In spring, summer, autumn, and winter, the R reached 0.95, 0.93, 0.94, and 0.97 at subsatellite points in the urban area, while it was 0.88, 0.82, 0.85, and 0.77 in the suburbs.

Evaluation Model of Location Light Pollution Level based on Analytic Hierarchy Process and Entropy Weight Method

With the development of society, the impact of light pollution has gradually intensified. It not only endangers human health and animal and plant activities but also indirectly damages the climate and environment. In the past, the research on the evaluation of urban light pollution often focused on the comparison between the light pollution level measured by the relevant photometry equipment and the natural light level. This means that many sites need to be selected for measurement in the evaluation city. To save resources and ensure the evaluation results have a certain validity, we built a Location Light Pollution Risk Index (LLPRI) model. First, we selected 16 indicators from 6 aspects and built a three-level indicator evaluation system. After that, based on combining the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) to calculate the index weight, we also use the Weight Combination Method to obtain the final weight of the index. Finally, we will bring the collected site sample data into the model and then use K-means Algorithm to cluster the model results and obtain the model metric We compare the results of the assessment of location light pollution risk level by the model with the data from the Military Meteorological Satellite Program (DMSP) and Visible Infrared Imaging Radiometer (VIIRS). The experimental results show that the LLRPI model has good effectiveness.

Nighttime fishing vessel observation in Bohai Sea based on VIIRS fishing vessel detection product (VBD)

Remote sensing of night light can be used to detect lights at night and identify the location of fishing boats at sea. Visible Infrared Imaging Radiometer (VIIRS) onboard NASA's SNPP (Suomi National Polar Partnership) satellite has remarkably improved the detection capabilities compared to previous satellites, therefore enabling the production of VBD (VIIRS Boat Detection) with better quality. In this study, we investigated the relationship between fishing activities and environmental factors using generalized additive models (GAMs). We utilized three spatial indexes: cluster index, heterogeneity index, and geographical center, to study the spatiotemporal distribution of fishing boats at night and their seasonal variation in the Bohai Sea based on the 12-months data in 2020. We observed that fishing activities fluctuated over time, with troughs in February and from May to August, which were respectively influenced by the lunar new year and summer fishing moratorium. At the end of the lunar new year and the moratorium, the fishing activities rebounded significantly. Returning boats from Shandong province caused a secondary peak in December. Spatially, detected fishing activities mostly took place in near-shore fishing grounds, such as Bohai Bay and Liaodong Bay, but were rare in central Bohai Sea. The convex hull of monthly geographical centers resembles a triangle. The shifting monthly geographical centers within the triangle was caused by the clustering of fishing boats in three fishing grounds: Liaodong Bay, Bohai Bay and Laizhou Bay, at different time. The results of this study can be used as a reference for the monitoring of fishery activities and fishery economic research in the Bohai Sea. The research methodology provides a low-cost way to track and monitor fishing vessels, therefore providing necessary information for decision making.

Determining pseudo-invariant calibration sites for comparing inter-mission ocean color data

Comparing inter-mission space instrument performance is crucial to accurate satellite measurements which guarantees the quality of ocean color products. However, comparing inter-mission instrument performance is limited by strong dispersion, which clearly originates from the spatial and temporal variability inside the oceanic sampling sites. We designed a novel comprehensive score metric (CSM) to quantitatively identify the candidate pseudo-invariant calibration sites (PICS) for inter-mission comparisons. We calculated the CSM from a year of ocean color and meteorological products from 2018 using a pixel-by-pixel method with a simple average of a temporal meteorological metric, a spatial aerosol metric, a temporal optical metric, a spatial optical metric, a data quality metric, a spectral shape metric, and a directional homogeneity metric. When we filtered the data with the threshold CSM > 0.6, we found that atmospheric and oceanic conditions from two smooth belts in the low latitude open ocean were more clear, stable, and homogeneous than other regions, and we suggested these smooth belts as candidate PICS. With image data from the Visible Infrared Imaging Radiometer (VIIRS) and Medium Resolution Spectral Imager II (MERSI II), we found that our candidate PICS were more effective than regions with low CSM in providing stable synchronous data for inter-mission comparisons. The MERSI II instrument, however, experienced significant degradation in radiance measurements from January to April in 2021, while the VIIRS instrument performed well. These results suggested that the CSM values were “experimental” but, under restrictive conditions, were sufficient for an inter-mission comparison and calibration application.

Effects of human activity intensity on habitat quality based on nighttime light remote sensing: A case study of Northern Shaanxi, China

Habitat quality is a crucial expression of the value of ecosystem services. Habitat quality issues caused by human activities are troubling dilemmas worldwide, and there is an urgent need to assess the impact of the large-scale human activity intensity on habitat quality. In this study, Northern Shaanxi, China, is used as an example to demonstrate how the impact on habitat quality can be explored by simulating the intensity of human activities using nighttime light remote sensing data from 2000 to 2020. Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) data, Visible Infrared Imaging Radiometer Suite-Day-Night Band (VIIRS-DNB) data, and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model were used in this study. The results showed that from 2000 to 2020, human activity intensity in Northern Shaanxi increased by 361.4 %, and the habitat quality decreased by 2.3 %. Habitat quality was more spatially clustered and spatially dependent than habitat degradation. Human activity intensity and habitat quality were significantly correlated. Habitat quality in Yulin city decreased significantly compared with that in Yan'an city. The differences in human activity types and economic structures of Yulin and Yan'an were an important factor for this phenomenon. The assessment of the impact of human activity intensity on habitat quality using nighttime light data is feasible and can be applied in other larger regions worldwide.

Spatial and Temporal Changes of Urban Built-Up Area in the Yellow River Basin from Nighttime Light Data

Nighttime light (NTL) images obtained by the Visible Infrared Imaging Radiometer (VIIRS) mounted on the National Polar-orbiting Partnership (NPP) could objectively represent human activities and instantly identify urban shapes on a temporal and spatial scale. From 2013 to 2020, the built-up areas of eight provincial capital cities were extracted using NPP/VIIRS NTL data to examine the dynamic changes in city expansion and socioeconomic development in the Yellow River Basin during the urbanization process. The spatial characteristics of urban built-up area expansion were generated using the eight-quadrant analysis method and combined with the statistical data of population and (gross domestic product) GDP to analyze the correlations between the light intensity of built-up areas, population and GDP; this enables an understanding of the changes in population and economy in the development of urban built-up area expansion. The findings show that: (1) unbalanced city development existed in the Yellow River Basin’s upper, middle, and lower reaches, and the expansion and light intensity of cities in the upper reaches were slower than those in the middle and lower reaches; (2) the spatial differentiation of urban expansion was significant between each of the reaches in the Yellow River Basin, and greatly influenced by natural geographical elements; and (3) positive correlation exists between light intensity, population, and GDP in the built-up areas of the middle and lower reaches, while the correlations in the upper reaches were not stable. In conclusion, light data indirectly reflects urban development and could be used as a substitute variable for socioeconomic development indicators under certain conditions.

Global 1 km × 1 km gridded revised real gross domestic product and electricity consumption during 1992–2019 based on calibrated nighttime light data

As fundamental data, gross domestic product (GDP) and electricity consumption can be used to effectively evaluate economic status and living standards of residents. Some scholars have estimated gridded GDP and electricity consumption. However, such gridded data have shortcomings, including overestimating real GDP growth, ignoring the heterogeneity of the spatiotemporal dynamics of the grid, and limited time-span. Simultaneously, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) and National Polar-orbiting Partnership’s Visible Infrared Imaging Radiometer (NPP/VIIRS) nighttime light data, adopted in these studies as a proxy tool, still facing shortcomings, such as imperfect matching results, discontinuity in temporal and spatial changes. In this study, we employed a series of methods, such as a particle swarm optimization-back propagation (PSO-BP) algorithm, to unify the scales of DMSP/OLS and NPP/VIIRS images and obtain continuous 1 km × 1 km gridded nighttime light data during 1992–2019. Subsequently, from a revised real growth perspective, we employed a top-down method to calculate global 1 km × 1 km gridded revised real GDP and electricity consumption during 1992–2019 based on our calibrated nighttime light data.

Modeling the direction and magnitude of angular effects in nighttime light remote sensing

Remote sensing of nighttime light (NTL) offers a unique opportunity to monitor urban dynamics and human socioeconomic activities directly from space. However, angular observations lead to inconsistencies among observations over the same area on different days, introducing uncertainty into daily NTL time series. This study aims to investigate this angular effect and its drivers using the Visible Infrared Imaging Radiometer Suite/Suomi (VIIRS) Black Marble NTL dataset. First, we proposed a conceptual model of the angular effect and hypothesized the mechanism of how urban three-dimensional (3D) landscapes form the anisotropic characteristics of artificial light observations. Second, we quantified the spatial patterns of the angular effect within five representative cities, and identified three distinctive types of angular effects: negative, U-shaped, and positive. Subsequently, the contribution of landscape factors to the direction (i.e., the type) and magnitude (i.e., NTL change rate with angle) of the angular effect is quantified using multinomial logistic regression and mediation analysis, respectively. The results show that the direction of the angular effect is mainly controlled by building height which determines the blocked and visible parts of artificial light at different satellite viewing angles. The magnitude of the angular effect is determined by both NTL brightness and landscape factors. The mediation analysis shows that landscape factors can have a direct effect on the magnitude of the angular effect as well as an indirect effect on the magnitude by affecting NTL brightness. Among the landscape factors, both vegetation and buildings are indicated to be significantly influential factors with direct and indirect effects. The findings of this research deepen our understanding of the NTL angular effect, guide the development of technologies for reconstructing high-quality daily NTL time series by correcting the angular effect, and help us better monitor high-frequency socioeconomic activities.

Overview of the performance of satellite fire products in China: Uncertainties and challenges

Satellite fire observations provide an essential constraint for the estimation of global biomass burning emissions. In this study, we present a comprehensive insight into the performance of the common satellite fire products in eastern China. Despite consistent spatial patterns, both polar-orbiting and geostationary satellite observations have large omission errors for the agricultural burning fires. Owing to a coarse resolution of 2 km, approximately 90% of the concurrent 375 m Visible infrared Imaging Radiometer (VIIRS) fires are not detected in Himawari-8 products. Nevertheless, the total amount of daily Himawari-8 fires is much higher than those of VIIRS and 1 km Moderate resolution imaging spectroradiometer (MODIS). The peak time of diurnal fire counts in eastern China has obvious seasonal variations, some of which are missed by polar-orbiting satellite detection. Validation by 3 m PlanetScope images shows that VIIRS and MODIS have a very high accuracy in detecting crop straw burning fires. However, Himawari-8 fires have obvious false alarms due largely to their algorithm defects. Also, the coarse resolution of Himawari-8 tends to make fire detection more likely to be obscured by dense smoke. The unmanned aerial vehicle (UAV) supervisions reveal prevalent small agricultural fires (<0.5 × 104 m2) in the extensive croplands that are not detected in the common satellite fires. As control measures get more stringent, spatial-temporal patterns as well as the scales of biomass burning activities in China have undergone dramatic changes. Considering the crucial role of satellite fires in estimating biomass burning emissions, it is necessary to improve satellite fire detection with more advanced observations and retrieval methods.

Calibration of the DSCOVR EPIC Visible and NIR Channels using Multiple LEO Radiometers

The first five years of operation of the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) at the Lagrange one point have produced results that uniquely complement the data from currently operating low orbit Earth-observing instruments. In this paper we describe an updated unified approach to EPIC calibration. In this approach, calibration coefficients and their trends were obtained by comparing EPIC observations to the measurements from polar orbiting radiometers. In this study L1B reflectances from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua and Terra satellites, Multi-angle Imaging Spectroradiometer (MISR) onboard Terra and Visible Infrared Imaging Radiometer (VIIRS) onboard the Suomi National Polar-orbiting Partnership (Suomi NPP) spacecraft were used to infer calibration coefficients for four EPIC visible and near-infrared channels: 443 nm, 551 nm, 680 nm, and 780 nm. EPIC Version three measurements made between June 2015 and August 2020 were used for comparison. The calibration procedure identifies the most homogeneous low Earth orbit radiometer scenes matching scattering angles that are temporarily and spatially collocated with EPIC observations. These scenes are used to determine reflectance to count (R/C) ratios in spectrally analogous channels. Seasonal average R/C ratios were analyzed to obtain EPIC calibration gains and trends. The trends for the full dataset period are not statistically significant except in the 443 nm channel. No significant changes in calibration were found after the instrument’s exit from safe hold in March 2020. The R/C ratios were also used to determine the differences in EPIC gains resulting from separate calibrations: against MODIS Aqua or Terra, as well as against forward or aftward MISR cameras. Statistical tests indicate that the differences between the two datasets are not significant except in the 780 nm channels where Aqua-derived coefficients may be around 2% lower compared to Terra. The dependence of EPIC calibration gains on the instrument scattering angle and on DSCOVR-Earth distance were investigated. Lastly, model Low Earth Orbit (LEO) reflectances calculated to match the EPIC viewing geometry were employed to study how EPIC calibration coefficients depend on EPIC-LEO viewing geometry differences. The effect of LEO and EPIC angular mismatch on calibration was shown to be small.

An Improved Inherent Optical Properties Data Processing System for Residual Error Correction in Turbid Natural Waters

Being able to accurately estimate inherent optical properties (IOPs) at long time scales is key to comprehending the aquatic biological and biogeochemical responses to long-term global climate change. We employed the near-infrared band and combined it with four “common bands” at visible wavelengths (around 443, 490, 551, and 670 nm) to adjust the IOPs data processing system, IDASv2. We applied the IDASv2 algorithm further to correct for the residual error in images of turbid waters. We evaluated the performance of the IDASv2 algorithm using datasets covering a wide range of natural water types from clear open ocean to turbid coastal and inland waters. Due to the water-leaving signals’ sensitivity to the optically significant constituents of highly turbid waters, the near-infrared band was very important for retrieving IOPs from those waters. In our analysis, we found that the IDASv2 algorithm provided IOPs data with R rs) data because of the strong absorption of pure water. We tested the IDASv2 algorithm with numerically simulated and satellite observed data of turbid water. After applying IDASv2, the IOPs data were accurately determined from R rs data contaminated by the residual error. Furthermore, the mean intermission difference between Medium Resolution Spectral Imager 2 and Visible Infrared Imaging Radiometer R rs data at 443 and 551 nm decreased from 8%–25% to 1%–9%. These results suggest that we can accurately estimate IOPs data for natural waters including naturally clear and turbid waters.

Evolution of the representation of global vegetation by vegetation continuous fields

Two decades of global annual fractional vegetation cover products have been derived using daily surface reflectance and land surface temperature data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard NASA's Terra satellite. These MODIS Vegetation Continuous Fields (VCF) products are unique and a distinct advance over discrete (ordinal) land cover characterizations. VCF consists of three continuous fractional cover layers at 250 m resolution summing to 100%: tree cover, non-tree vegetation and non-vegetated surface plus surface water. This paper describes the fully-automated machine learning algorithm used to create MODIS VCF and details recent algorithm improvements. Comparisons of the latest VCF products in the MOD44B collection 6, with field-based fractional tree cover and other remote sensing fractional cover products demonstrate improved agreement: the root mean square (RMS) deviations range from 9 to 23%. A comparison of MODIS canopy cover values with those from the Global Ecosystem Dynamics Investigation (GEDI) gives an R2 value of 0.53. Automation of the algorithm provides flexibility for readily porting the method to derive fractional cover products from additional instruments with similar radiometric characteristics such as the Visible Infrared Imaging Radiometer (VIIRS) payload aboard Suomi NPP and JPSS platforms, and successors.

Vicarious Radiometric Calibration of Ocean Color Bands for FY-3D/MERSI-II at Lake Qinghai, China.

To calibrate the low signal response of the ocean color (OC) bands and test the stability of the Fengyun-3D (FY-3D)/Medium Resolution Spectral Imager II (MERSI-II), an absolute radiometric calibration field test of FY-3D/MERSI-II at the Lake Qinghai Radiometric Calibration Site (RCS) was carried out in August 2018. The lake surface and atmospheric parameters were mainly measured by advanced observation instruments, and the MODerate spectral resolution atmospheric TRANsmittance algorithm and computer model (MODTRAN4.0) was used to simulate the multiple scattering radiance value at the altitude of the sensor. The results showed that the relative deviations between bands 9 and 12 are within 5.0%, while the relative deviations of bands 8, and 13 are 17.1%, and 12.0%, respectively. The precision of the calibration method was verified by calibrating the Aqua/Moderate-resolution Imaging Spectroradiometer (MODIS) and National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer (VIIRS), and the deviation of the calibration results was evaluated with the results of the Dunhuang RCS calibration and lunar calibration. The results showed that the relative deviations of NPP/VIIRS were within 7.0%, and the relative deviations of Aqua/MODIS were within 4.1% from 400 nm to 600 nm. The comparisons of three on-orbit calibration methods indicated that band 8 exhibited a large attenuation after launch and the calibration results had good consistency at the other bands except for band 13. The uncertainty value of the whole calibration system was approximately 6.3%, and the uncertainty brought by the field surface measurement reached 5.4%, which might be the main reason for the relatively large deviation of band 13. This study verifies the feasibility of the vicarious calibration method at the Lake Qinghai RCS and provides the basis and reference for the subsequent on-orbit calibration of FY-3D/MERSI-II.

An approach to cross-calibrating multi-mission satellite data for the open ocean

Synchronous observations with similar illumination-observation and meteorological conditions are critical components of cross-calibration analysis. This study outlines data quality control criteria for obtaining the stable synchronous data needed for developing and evaluating a cross-calibration algorithm. With image data from the Visible Infrared Imaging Radiometer (VIIRS) and Medium Resolution Spectral Imager II (MERSI II), we developed a cross-calibration algorithm using 35 image pairs of four ocean gyres, and we evaluated the data using 11 image pairs of the global ocean. We found that our new algorithm provided well-calibrated MERSI II reflectance at the top-of-atmosphere. The coefficients of determination (R2) were greater than 0.89, and the mean absolute percent difference (MAPD) varied from 1.13% to 8.37% in the visible bands, which was significantly superior to an algorithm developed from a data set constrained by existing data quality controls. When the satellite data were preprocessed with the new cross-calibration algorithm, the MERSI II instrument provided inter-mission remote sensing reflectance for the North Pacific Gyre that was consistent with the VIIRS instrument. Furthermore, we derived consistent estimates of remote sensing reflectance and backscattering coefficients (bb) from VIIRS and the cross-calibrated MERSI II reflectance data for four typically turbid coastal waters. The VIIRS bb coastal images had special distribution patterns, such as tongue-shaped plumes and mesoscale eddies, which accurately reappeared in the MERSI II images. The inter-mission MAPD values varied from 16%–24% for the coastal waters. This uncertainty level was much lower than the bb data quantified from the original MERSI II data with no cross-calibration. Our results suggest that our data quality control criteria provide good quality synchronous data for cross-calibration analysis. MERSI II could provide good ocean color products for oceanic communications after cross-calibration, even though the radiance calibration for the original MERSI II reflectance data are imperfect.

Santa Catarina visto do espaço à noite e o sistema viário

O produto cartográfico derivado de imagens noturnas (Visible Infrared Imaging Radiometer - VIIRS) do sensor Suomi NPP pode observar pouca luz até a escala de uma lâmpada de estrada isolada ou de um barco de pesca. A proposta foi aplicar este recurso para identificar os espaços luminiosos e opacos, discutidos em Santos e Oliveira (2001). Neste artigo descreve-se a metodologia aplicada. O produto final se mostrou interessante para usos no planejamento e gestão territorial.

Application of Luojia 1-01 Nighttime Images for Detecting the Light Changes for the 2019 Spring Festival in Western Cities, China

This study analyzed changes in nighttime light during the 2019 Spring Festival using Luojia 1-01 nighttime images in six western cities of China (Chengdu, Panzhihua, Kunming, Yuxi, Lhasa, and Jinchang). First, the radiance of the nighttime images was calculated. Second, the light area (LA) and average light intensity (ALI) were estimated for both Spring Festival and non-festival dates. Third, the differences in LA and ALI between the Spring Festival and non-festival were analyzed for all six cities. Migration population data from Baidu Inc. were used to examine the relationship between the changes of nighttime light and the population migration. The results show that, during the non-festival to Spring Festival period, the decrease in LA values coincided with negative net immigration. During the Spring Festival to non-festival period, the LA values increased, which coincided with positive net immigration. The F-test shows that the positive linear relationship between the normalized change in LA and the normalized net immigration is significant at the 0.05 level. This strongly indicates that population migration causes changes in LA. Moreover, while the population is considerably less in these cities during the Spring Festival, the ALI is noticeably higher, which suggests that urban activities are intensified during this period. This study demonstrates the applicability of using Luojia 1-01 nighttime images to detect the nighttime light changes for the Spring Festival in western cities, China, which can then be used to evaluate population migration and urban activities in the Spring Festival. Considering the higher spatial resolution of Luojia 1-01 than NPP (National Polar-orbiting Partnership) / VIIRS (Visible infrared Imaging Radiometer), this study may inspire more applications of Luojia 1-01 to track the activities in a variety of festival-cultures and cities.

Developing Land Surface Directional Reflectance and Albedo Products from Geostationary GOES-R and Himawari Data: Theoretical Basis, Operational Implementation, and Validation

The new generation of geostationary satellite sensors is producing an unprecedented amount of Earth observations with high temporal, spatial and spectral resolutions, which enable us to detect and assess abrupt surface changes. In this study, we developed the land surface directional reflectance and albedo products from Geostationary Operational Environment Satellite-R (GOES-R) Advanced Baseline Imager (ABI) data using a method that was prototyped with the Moderate Resolution Imaging Spectroradiometer (MODIS) data in a previous study, and was also tested with data from the Advanced Himawari Imager (AHI) onboard Himawari-8. Surface reflectance is usually retrieved through atmospheric correction that requires the input of aerosol optical depth (AOD). We first estimated AOD and the surface bidirectional reflectance factor (BRF) model parameters simultaneously based on an atmospheric radiative transfer formulation with surface anisotropy, and then calculated the “blue-sky” surface broadband albedo and directional reflectance. This algorithm was implemented operationally by the National Oceanic and Atmospheric Administration (NOAA) to generate the GOES-R land surface albedo product suite with a daily updated clear-sky satellite observation database. The “operational” land surface albedo estimation from ABI and AHI data was validated against ground measurements at the SURFRAD sites and OzFlux sites and compared with the existing satellite products, including MODIS, Visible infrared Imaging Radiometer (VIIRS), and Global Land Surface Satellites (GLASS) albedo products, where good agreement was found with bias values of −0.001 (ABI) and 0.020 (AHI) and root-mean-square-errors (RMSEs) less than 0.065 for the hourly albedo estimation. Directional surface reflectance estimation, evaluated at more than 74 sites from the Aerosol Robotic Network (AERONET), was proven to be reliable as well, with an overall bias very close to zero and RMSEs within 0.042 (ABI) and 0.039 (AHI). Results show that the albedo and reflectance estimation can satisfy the NOAA accuracy requirements for operational climate and meteorological applications.

Intercomparison of Multiple Satellite Aerosol Products against AERONET over the North China Plain

In this study, using Aerosol Robotic Network aerosol optical depth (AOD) products at three stations in the North China Plain (NCP)—a heavily polluted region in China—the AOD products from six satellite-borne radiometers: the Moderate Resolution Imagining Spectroradiometer (MODIS), the Multiangle Imaging Spectroradiometer (MISR), Ozone Mapping Imaging (OMI), the Visible Infrared Imaging Radiometer (VIIRS), the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), and Polarization and Directionality of the Earth’s Reflectances (POLDER), were thoroughly validated, shedding new light on their advantages and disadvantages. The MODIS Deep Blue (DB) products provide more accurate retrievals than the MODIS Dark Target (DT) and other satellite products at the Beijing site (BJ,a megacity), with higher correlations with AERONET (R &gt; 0.93), lower mean absolute bias (MB &lt; 0.012), and higher percentages (&gt;68%) falling within the expected error (EE). All MODIS DT and DB products perform better than the other satellite products at the Xianghe site (XH, a suburb). The MODIS/Aqua DT products at both 3-km and 10-km resolutions performed better than the other space-borne AOD products at the Xinglong site (XL, a rural area at the top of a mountain). MISR, VIIRS, and SeaWiFS tend to underestimate high AOD values and overestimate AOD values under very low AOD conditions in the NCP. Both OMI and POLDER significantly underestimate the AOD. In terms of data volume, MISR with the limited swath width of 380 km has less data volume than the other satellite sensors. MODIS products have the highest sampling rate, especially the MODIS DT and DB merged products, and can be used for various climate study and air-quality monitoring.