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Author (up) Chen, H.; Xiong, X.; Geng, X.; Twedt, K. url  doi
openurl 
  Title Stray-light correction and prediction for Suomi National Polar-orbiting Partnership visible infrared imaging radiometer suite day-night band Type Journal Article
  Year 2019 Publication Journal of Applied Remote Sensing Abbreviated Journal J. Appl. Rem. Sens.  
  Volume 13 Issue 02 Pages 1  
  Keywords Instrumentation; Remote Sensing  
  Abstract The Suomi National Polar-orbiting Partnership visible infrared imaging radiometer suite instrument has successfully operated since its launch in October 2011. Stray-light contamination is much larger than prelaunch expectations, and it causes a major decrease in quality of the day-night band night imagery when the spacecraft is crossing the Northern or Southern day-night terminators. The stray light can be operationally estimated using Earth-view data that are measured over dark surfaces during the new moon each month. More than 7 years of nighttime images have demonstrated that the stray-light contamination mainly depends on the Earth–Sun–spacecraft geometry, so its intensity is generally estimated as a function of the satellite zenith angle. In practice, stray-light contamination is also detector- and scan-angle-dependent. Previous methods of stray-light prediction generally rely on using the known stray light level from the same month in the previous year, when the Earth–Sun–spacecraft geometries had been similar. We propose a new method to predict stray-light contamination. The Kullback–Leibler similarity metric is used as a method to combine data from multiple years with appropriate adjustments for degradation and geometry drifts in order to calculate a fused stray-light contamination correction. The new method provides an improved prediction of stray-light contamination compared to the existing methods and may be considered for future use in the real-time NASA Level-1B products.  
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  ISSN 1931-3195 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2517  
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Author (up) Chen, H.; Zhang, X.; Wu, R.; Cai, T. url  doi
openurl 
  Title Revisiting the environmental Kuznets curve for city-level CO2 emissions: based on corrected NPP-VIIRS nighttime light data in China Type Journal Article
  Year 2020 Publication Journal of Cleaner Production Abbreviated Journal Journal of Cleaner Production  
  Volume Issue Pages 121575  
  Keywords Remore Sensing; China; carbon emissions; CO2 emissions; night lights; NPP-VIIRS; VIIRS-DNB; VIIRS-DNB; Kuznets curve  
  Abstract With the increasing trend of global warming, the Chinese government faces tremendous pressure to reduce CO2 emissions. The purpose of this study is to accurately measure CO2 emissions at the city scale in China and examine the environmental Kuznets curve, thereby providing a reference for decision-making. Corrected NPP-VIIRS nighttime light data were used to accurately estimate carbon dioxide emissions at the provincial and city scales in China. Then, based on the STRIPAT model, 291 cities in China were used to verify the environmental Kuznets curve. Our results show that on the provincial scale, the R2 between the estimated value and the statistical value of carbon dioxide reaches 0.85. Western cities in China emit more CO2, as do economically developed cities and industry- and mining-dominated cities. There are two CO2 emission hot spots in the north and one cold spot in the south. It was found that the environmental Kuznets curve on the city scale exists. This study has practical value in utilizing NPP-VIIRS data for the estimation of city CO2 emissions. The results also have academic value for determining factors that contribute to carbon dioxide emissions and can provide a reference for relevant decision makers. This study could be considered the first to simulate CO2 emissions at the provincial and city levels in China based on a NPP-VIIRS nighttime light model to explore the associated geographical distribution characteristics and potential influencing factors.  
  Address State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0959-6526 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number IDA @ john @ Serial 2917  
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Author (up) Chen, J., & Li, L. url  doi
openurl 
  Title Regional Economic Activity Derived From MODIS Data: A Comparison With DMSP/OLS and NPP/VIIRS Nighttime Light Data Type Journal Article
  Year 2019 Publication IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Abbreviated Journal  
  Volume Issue Pages 1-11  
  Keywords Remote Sensing; Economics  
  Abstract Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) and Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) nighttime light data are the two most commonly used indicators of gross domestic product (GDP) estimation. Few studies explore the potential of daytime satellite data for estimating GDP. This study demonstrates a linear support vector machine (Linear-SVM) model to estimate GDP over Hubei province and Guangdong province, China, in 2013 from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Also, a comparison of MODIS data with DMSP/OLS and NPP/VIIRS nighttime light data was conducted. Results show that the Linear-SVM model (Hubei: R2 = 0.66, 0.71, 0.92; Guangdong: R2 = 0.37, 0.32, 0.67) has better model performance than simple linear regression (R2 = 0.54, 0.59, 0.86; R2 = 0.23, 0.23, 0.63) based on DMSP/OLS nighttime lights, DMSP/OLS corrected nighttime lights, and NPP/VIIRS nighttime lights, respectively, while MODIS data has model performance of R2 = 0.77 (Hubei) and R2 = 0.55 (Guangdong) based on the Linear-SVM model, further indicating that MODIS data improves the accuracy of GDP estimation compared to DMSP/OLS nighttime lights. In addition, MODIS data produced finer GDP estimation than DMSP/OLS nighttime lights, especially in dark and light saturated areas. Although MODIS data is not as accurate as the NPP/VIIRS nighttime lights for estimating GDP, the proposed method could be applicable to other daytime satellite data and has broad prospects for improving the spatial and temporal resolution of regional economic activity and improving estimation accuracy.  
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  Notes Approved no  
  Call Number IDA @ intern @ Serial 2630  
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Author (up) Chen, J.; Fan, W.; Li, K.; Liu, X.; Song, M. url  doi
openurl 
  Title Fitting Chinese cities’ population distributions using remote sensing satellite data Type Journal Article
  Year 2019 Publication Ecological Indicators Abbreviated Journal Ecological Indicators  
  Volume 98 Issue Pages 327-333  
  Keywords Remote Sensing  
  Abstract Remote sensing satellite data from 2012 to 2013 are used to fit the Chinese cities’ population distributions over the same period in order to verify the population distribution in China from a relatively objective perspective. Most scholars have used nighttime light data and vegetation indexes to fit the population distribution, but the fitting effect has not been satisfactory. In this paper, processed Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data, net primary productivity of vegetation (NPP), and average slope data were used to fit the population distribution from the three dimensions of economic growth, ecological environment, and topographic factors, respectively. The fitting effect was significantly improved compared with other studies (R2 values of 0.9244 and 0.9253 in 2012 and 2013, respectively). Therefore, this method provides a practical and effective way to fit the population distribution for remote cities or areas lacking census data. Furthermore, there is important practical significance for the government to formulate its population policies rationally, optimize the spatial distribution of population, and improve the ecological quality of the city.  
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  ISSN 1470160X ISBN Medium  
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  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2071  
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Author (up) Chen, J.; Zhao, F.; Zeng, N.; Oda, T. url  doi
openurl 
  Title Comparing a global high-resolution downscaled fossil fuel CO2 emission dataset to local inventory-based estimates over 14 global cities Type Journal Article
  Year 2020 Publication Carbon Balance and Management Abbreviated Journal Carbon Balance Manag  
  Volume 15 Issue 1 Pages 9  
  Keywords Remote Sensing; City CO2 emissions; Emission inventory; Fossil fuel CO2 emissions; In-boundary; Odiac  
  Abstract BACKGROUND: Compilation of emission inventories (EIs) for cities is a whole new challenge to assess the subnational climate mitigation effort under the Paris Climate Agreement. Some cities have started compiling EIs, often following a global community protocol. However, EIs are often difficult to systematically examine because of the ways they were compiled (data collection and emission calculation) and reported (sector definition and direct vs consumption). In addition, such EI estimates are not readily applicable to objective evaluation using modeling and observations due to the lack of spatial emission extents. City emission estimates used in the science community are often based on downscaled gridded EIs, while the accuracy of the downscaled emissions at city level is not fully assessed. RESULTS: This study attempts to assess the utility of the downscaled emissions at city level. We collected EIs from 14 major global cities and compare them to the estimates from a global high-resolution fossil fuel CO2 emission data product (ODIAC) commonly used in the science research community. We made necessary adjustments to the estimates to make our comparison as reasonable as possible. We found that the two methods produce very close area-wide emission estimates for Shanghai and Delhi (< 10% difference), and reach good consistency in half of the cities examined (< 30% difference). The ODIAC dataset exhibits a much higher emission compared to inventory estimates in Cape Town (+ 148%), Sao Paulo (+ 43%) and Beijing (+ 40%), possibly related to poor correlation between nightlight intensity with human activity, such as the high-emission and low-lighting industrial parks in developing countries. On the other hand, ODIAC shows lower estimates in Manhattan (- 62%), New York City (- 45%), Washington D.C. (- 42%) and Toronto (- 33%), all located in North America, which may be attributable to an underestimation of residential emissions from heating in ODIAC's nightlight-based approach, and an overestimation of emission from ground transportation in registered vehicles statistics of inventory estimates. CONCLUSIONS: The relatively good agreement suggests that the ODIAC data product could potentially be used as a first source for prior estimate of city-level CO2 emission, which is valuable for atmosphere CO2 inversion modeling and comparing with satellite CO2 observations. Our compilation of in-boundary emission estimates for 14 cities contributes towards establishing an accurate inventory in-boundary global city carbon emission dataset, necessary for accountable local climate mitigation policies in the future.  
  Address Goddard Earth Sciences Research and Technology, Universities Space Research Association, Columbia, MD, USA  
  Corporate Author Thesis  
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  Language English Summary Language Original Title  
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  Series Volume Series Issue Edition  
  ISSN 1750-0680 ISBN Medium  
  Area Expedition Conference  
  Notes PMID:32430547 Approved no  
  Call Number GFZ @ kyba @ Serial 2929  
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