Abstract
This study identified the correlation between OCO-2 XCO2 signatures and night-time light (NTL) dynamics for four different landscapes in India: Megapolis (part of Mumbai), City (part of Raipur), Town (part of Dindigul), and Village (part of Balnoi). The data used for building the correlation were collected from Orbiting Carbon Observatory-2 (OCO-2) XCO2 and Defense Meteorological Satellite Program Operational Line Scanner’s (DMSP/OLS) NTL datasets. The result of the study indicated that CO2 concentration is strongly related to NTL data. Megapolis part was found to exhibit higher values in both CO2 mean concentration (405.8 ppm) and NTL distribution level (41.5-pixel value), while the village part was low in both CO2 mean concentration (400.306 ppm) and NTL distribution level (4.4-pixel values). The city part of the data showed that the highest mean local R2 (0.964) in Geographically Weighted Regression (GWR), while the village part recorded the lowest mean local R2 (0.579) in the given data set. This study further emphasizes that the night light satellite imagery can be used as descriptors and proxies to calculate CO2 emission.
Similar content being viewed by others
References
Tanaka, K., O’Neill, B. C., Rokityanskiy, D., Obersteiner, M., & Tol, R. S. (2009). Evaluating global warming potentials with historical temperature. Climatic Change, 96(4), 443–466.
Sudhira, H. S., Ramachandra, T. V., & Subrahmanya, M. H. B. (2007). Bangalore. Cities, 24(5), 379–390. https://doi.org/10.1016/j.cities.2007.04.003.
Suthar, S., Sharma, J., Chabukdhara, M., & Nema, A. K. (2010). Water quality assessment of river Hindon at Ghaziabad, India: Impact of industrial and urban wastewater. Environmental Monitoring and Assessment, 165(1–4), 103–112. https://doi.org/10.1007/s10661-009-0930-9.
Taubenböck, H., Wegmann, M., Roth, A., Mehl, H., & Dech, S. (2009). Urbanization in India–Spatiotemporal analysis using remote sensing data. Computers, Environment and Urban Systems, 33(3), 179–188. https://doi.org/10.1016/j.compenvurbsys.2008.09.003.
Yin, J., Yin, Z., Zhong, H., Xu, S., Hu, X., Wang, J., et al. (2011). Monitoring urban expansion and land use/land cover changes of Shanghai metropolitan area during the transitional economy (1979–2009) in China. Environmental Monitoring and Assessment, 177(1–4), 609–621. https://doi.org/10.1007/s10661-010-1660-8.
Kilic, S., Evrendilek, F., Berberoglu, S., & Demirkesen, A. C. (2006). Environmental monitoring of land-use and land-cover changes in a Mediterranean Region of Turkey. Environmental Monitoring and Assessment, 114(1–3), 157–168. https://doi.org/10.1007/s10661-006-2525-z.
Huang, Q., Yang, X., Gao, B., Yang, Y., Zhao, Y., Huang, Q., et al. (2014). Application of DMSP/OLS nighttime light images: A meta-analysis and a systematic literature review. Remote Sensing, 6(8), 6844–6866. https://doi.org/10.3390/rs6086844.
Golkar, F., Al-Wardy, M., Saffari, S. F., Al-Aufi, K., & Al-Rawas, G. (2020). Using OCO-2 satellite data for investigating the variability of atmospheric CO2 concentration in relationship with precipitation, relative humidity, and vegetation over Oman. Water, 12(1), 101–101.
Siabi, Z., Falahatkar, S., & Alavi, S. J. (2019). Spatial distribution of XCO2 using OCO-2 data in growing seasons. Journal of environmental management, 244, 110–118.
Zhang, X., Wu, J., Peng, J., Cao, Q., Zhang, X., Wu, J., et al. (2017). The uncertainty of nighttime light data in estimating carbon dioxide emissions in China: A comparison between DMSP-OLS and NPP-VIIRS. Remote Sensing, 9(8), 797–797. https://doi.org/10.3390/rs9080797.
Crisp, D., Fisher, B. M., O’Dell, C., Frankenberg, C., Basilio, R., Bösch, H., et al. (2012). The ACOS CO 2 retrieval algorithm- - Part II: Global X CO2 data characterization. Atmospheric Measurement Techniques, 5(4), 687–707. https://doi.org/10.5194/amt-5-687-2012.
O’Brien, D. M. (2002). Global observations of the carbon budget, 2, CO2 column from differential absorption of reflected sunlight in the 1.61 μm band of CO2. Journal of Geophysical Research, 107, 4354–4354. https://doi.org/10.1029/2001JD000617.
Chevallier, F., Deutscher, N. M., Conway, T. J., Ciais, P., Ciattaglia, L., Dohe, S., et al. (2011). Global CO2 fluxes inferred from surface air-sample measurements and from TCCON retrievals of the CO2 total column. Geophysical Research Letters. https://doi.org/10.1029/2011GL049899.
Randerson, J. T., Gurney, K. R., Law, R. M., & Denning, A. S. (2002). Towards robust regional estimates of CO2 sources and sinks using atmospheric transport models. Nature, 415, 626–630.
Falahatkar, S., Mousavi, S. M., & Farajzadeh, M. (2017). Spatial and temporal distribution of carbon dioxide gas using GOSAT data over IRAN. Environmental Monitoring and Assessment, 189(12), 627–627. https://doi.org/10.1007/s10661-017-6285-8.
Pandey, B., Joshi, P. K., & Seto, K. C. (2013). Monitoring urbanization dynamics in india using DMSP/OLS night time lights and SPOT-VGT data. International Journal of Applied Earth Observation and Geoinformation, 23(1), 49–61. https://doi.org/10.1016/j.jag.2012.11.005.
Doll, C. H., Muller, J.-P., & Elvidge, C. D. (2000). Night-time imagery as a tool for global mapping of socioeconomic parameters and greenhouse gas emissions. AMBIO A Journal of the Human Environment, 29(3), 157–162. https://doi.org/10.1579/0044-7447-29.3.157.
Elvidge, C. D., Imhoff, M. L., Baugh, K. E., Hobson, V. R., Nelson, I., Safran, J., et al. (2001). Night-time lights of the world: 1994–1995. ISPRS Journal of Photogrammetry and Remote Sensing, 56(2), 81–99. https://doi.org/10.1016/S0924-2716(01)00040-5.
Amaral, S., Monteiro, A. M. V., Camara, G., & Quintanilha, J. A. (2006). DMSP/OLS night-time light imagery for urban population estimates in the Brazilian Amazon. International Journal of Remote Sensing, 27(5), 855–870. https://doi.org/10.1080/01431160500181861.
Sutton, P. (1998). Modeling population density with night-time satellite imagery and GIS. Computers, Environment and Urban Systems, 21(3–4), 227–244. https://doi.org/10.1016/S0198-9715(97)01005-3.
de Leon, P., Barido, D., & Marshall, J. D. (2014). Relationship between urbanization and CO2 emissions depends on income level and policy. Environmental Science and Technology, 48(7), 3632–3639. https://doi.org/10.1021/es405117n.
Shobairi, S. O. R., Environmental, L. M. Y. A. J., & o., & Undefined, . (2016). Analysis of relationships between night-time imageries and greenhouse gases emissions based on RS and GIS. American Journal of Environmental Engineering, 6(5), 140–147. https://doi.org/10.5923/j.ajee.20160605.02.
Ghosh, T., Elvidge, C. D., Sutton, P. C., Baugh, K. E., Energies, D. Z., & Undefined, . (2010). Creating a global grid of distributed fossil fuel CO2 emissions from night-time satellite imagery. Energies, 3(12), 1895–1913. https://doi.org/10.3390/en3121895.
Poumanyvong, P., & Kaneko, S. (2010). Does urbanization lead to less energy use and lower CO2 emissions? A cross-country analysis. Ecological Economics, 70(2), 434–444. https://doi.org/10.1016/j.ecolecon.2010.09.029.
United Nations, (2017). World population prospects: The 2017 Revision. United Nations Department of Economic and Social Affairs Population Division.
Thompson, D. R., Chris Benner, D., Brown, L. R., Crisp, D., Malathy Devi, V., Jiang, Y., et al. (2012). Atmospheric validation of high accuracy CO2 absorption coefficients for the OCO-2 mission. Journal of Quantitative Spectroscopy and Radiative Transfer, 113(17), 2265–2276. https://doi.org/10.1016/J.JQSRT.2012.05.021.
Noaa. (2014). Version 4 DMSP-OLS nighttime lights time series. National Centers For Environmental Information, 5, 1–5.
Guo, M., Wang, X., Li, J., Yi, K., Zhong, G., Tani, H., et al. (2012). Assessment of global carbon dioxide concentration using MODIS and GOSAT data. Sensors, 12(12), 16368–16389. https://doi.org/10.3390/s121216368.
Um, J.-S. (2015). Comparative evaluation of CO2 concentrations across administrative regions with temperate climates in Northeast Asia: Potentials and constraints. Carbon Management, 6(3–4), 89–99. https://doi.org/10.1080/17583004.2015.1090057.
Li, H., Calder, C. A., & Cressie, N. (2007). Beyond Moran’s I: Testing for spatial dependence based on the spatial autoregressive model. Geographical Analysis, 39(4), 357–375. https://doi.org/10.1111/j.1538-4632.2007.00708.x.
Hu, Q., Ma, Y., Xu, B., Song, Q., Tang, H., Wu, W., et al. (2018). Estimating sub-pixel soybean fraction from time-series MODIS data using an optimized geographically weighted regression model. Remote Sensing, 10(4), 491–491. https://doi.org/10.3390/rs10040491.
Jiang, M., Sun, W., Yang, G., & Zhang, D. (2017). Modelling seasonal GWR of daily PM2.5 with proper auxiliary variables for the Yangtze River Delta. Remote Sensing, 9(4), 346–346. https://doi.org/10.3390/rs9040346.
IPCC (2014). Climate change 2014: Synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. In R. K. Pachauri, M. R. Allen, V. R. Barros, J. Broome, W. Cramer, R. Christ, et al. (Eds.), (pp. 151). Geneva, Switzerland: Ipcc.
Li, X., Zhou, W., & Ouyang, Z. (2013). Forty years of urban expansion in Beijing: What is the relative importance of physical, socioeconomic, and neighborhood factors? Applied Geography, 38, 1–10. https://doi.org/10.1016/J.APGEOG.2012.11.004.
Ma, T., Zhou, C., Pei, T., Haynie, S., & Fan, J. (2012). Quantitative estimation of urbanization dynamics using time series of DMSP/OLS night-time light data: A comparative case study from China’s cities. Remote Sensing of Environment, 124, 99–107. https://doi.org/10.1016/J.RSE.2012.04.018.
Raheem, I. D., & Ogebe, J. O. (2017). CO2 emissions, urbanization and industrialization: Evidence from a direct and indirect heterogeneous panel analysis. Management of Environmental Quality An International Journal, 28(6), 851–867. https://doi.org/10.1108/MEQ-09-2015-0177.
Thapa, R. B., & Murayama, Y. (2010). Drivers of urban growth in the Kathmandu valley, Nepal: Examining the efficacy of the analytic hierarchy process. Applied Geography, 30(1), 70–83. https://doi.org/10.1016/j.apgeog.2009.10.002.
Tarongi, J. M., & Camps, A. (2010). Normality analysis for RFI detection in microwave radiometry. Remote Sensing, 2(1), 191–210. https://doi.org/10.3390/rs2010191.
Acknowledgements
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF–2018R1D1A1B07041977). We thank National Aeronautics and Space Administration, United States (NASA) for providing OCO-2 satellite data (https://co2.jpl.nasa.gov). Furthermore, we are grateful to DMSP/OLS for NTL datasets (https://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
In this research article, authors have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Matloob, A., Sarif, M.O. & Um, JS. Exploring correlation between OCO-2 XCO2 and DMSP/OLS nightlight imagery signature in four selected locations in India. Spat. Inf. Res. 29, 123–135 (2021). https://doi.org/10.1007/s41324-021-00381-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41324-021-00381-x