Estimation of CO2 Emissions from Wildfires Using OCO-2 Data

The biomass burning model (BBM) has been the most widely used method for estimation of trace gas emissions. Due to the difficulty and variability in obtaining various necessary parameters of BBM, a new method is needed to quickly and accurately calculate the trace gas emissions from wildfires. Here, we used satellite data from the Orbiting Carbon Observatory-2 (OCO-2) to calculate CO2 emissions from wildfires (the OCO-2 model). Four active wildfires in Siberia were selected in which OCO-2 points intersecting with smoke plumes identified by Aqua MODIS (MODerate-resolution Imaging Spectroradiometer) images. MODIS band 8, band 21 and MISR (Multi-angle Imaging SpectroRadiometer) data were used to identify the smoke plume area, burned area and smoke plume height, respectively. By contrast with BBM, which calculates CO2 emissions based on the bottom–top mode, the OCO-2 model estimates CO2 emissions based on the top–bottom mode. We used a linear regression model to compute CO2 concentration (XCO2) for each smoke plume pixel and then calculated CO2 emissions for each wildfire point. The CO2 mass of each smoke plume pixel was added to obtain the CO2 emissions from wildfires. After verifying our results with the BBM, we found that the biases were between 25.76% and 157.11% for the four active fires. The OCO-2 model displays the advantages of remote-sensing technology and is a useful tool for fire-emission monitoring, although we note some of its disadvantages. This study proposed a new perspective to estimate CO2 emissions from wildfire and effectively expands the applied range of OCO-2 satellite data.

[1]  P. Crutzen,et al.  Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning , 1980 .

[2]  C. Borrego,et al.  Forest fire emissions in Portugal: a contribution to global warming? , 1994, Environmental pollution.

[3]  Jeff Dozier,et al.  Spectral emissivity measurements of land-surface materials and related radiative transfer simulations , 1994 .

[4]  D. Roy,et al.  The MODIS fire products , 2002 .

[5]  J. Kauffman,et al.  Biomass dynamics associated with deforestation, fire, and, conversion to cattle pasture in a Mexican tropical dry forest , 2003 .

[6]  David J. Diner,et al.  Aerosol source plume physical characteristics from space-based multiangle imaging , 2007 .

[7]  David J. Diner,et al.  A data-mining approach to associating MISR smoke plume heights with MODIS fire measurements , 2007 .

[8]  Judith C. Chow,et al.  Spatial and seasonal distributions of carbonaceous aerosols over China , 2007 .

[9]  C. Wiedinmyer,et al.  Estimates of CO2 from fires in the United States: implications for carbon management , 2007, Carbon balance and management.

[10]  C. Elvidge,et al.  Active forest fire monitoring in Uttaranchal State, India using multi‐temporal DMSP‐OLS and MODIS data , 2007 .

[11]  Jesús San-Miguel-Ayanz,et al.  Assessment of forest fire impacts and emissions in the European Union based on the European forest fire information system , 2008 .

[12]  M. G. Schultz,et al.  The MACC Global Fire Assimilation System : First Emission Products ( GFASv 0 ) , 2009 .

[13]  S. Freitas,et al.  Estimating trace gas and aerosol emissions over South America: Relationship between fire radiative energy released and aerosol optical depth observations , 2009 .

[14]  D. Chang,et al.  Estimates of biomass burning emissions in tropical Asia based on satellite-derived data , 2009 .

[15]  F. Siegert,et al.  Spatiotemporal fire occurrence in Borneo over a period of 10 years , 2009 .

[16]  Luigi Fortuna,et al.  A Tool for Multi-Platform Remote Sensing Processing , 2009 .

[17]  J. Randerson,et al.  Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997-2009) , 2010 .

[18]  Michael J. Garay,et al.  MISR Stereo Heights of Grassland Fire Smoke Plumes in Australia , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Isabel M. D. Rosa,et al.  Atmospheric emissions from vegetation fires in Portugal (1990–2008): estimates, uncertainty analysis, and sensitivity analysis , 2010 .

[20]  Robert E. Haring,et al.  The Orbiting Carbon Observatory instrument: performance of the OCO instrument and plans for the OCO-2 instrument , 2010, Remote Sensing.

[21]  S. K. Akagi,et al.  The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning , 2010 .

[22]  M. Mack,et al.  Quantifying fire severity, carbon, and nitrogen emissions in Alaska's boreal forest. , 2010, Ecological applications : a publication of the Ecological Society of America.

[23]  S. K. Akagi,et al.  Emission factors for open and domestic biomass burning for use in atmospheric models , 2010 .

[24]  M. George,et al.  Satellite- and ground-based CO total column observations over 2010 Russian fires: accuracy of top-down estimates based on thermal IR satellite data , 2011 .

[25]  M. Razinger,et al.  Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power , 2011 .

[26]  David M. Rider,et al.  Preflight Spectral Calibration of the Orbiting Carbon Observatory , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Gareth Roberts,et al.  Field determination of biomass burning emission ratios and factors via open-path FTIR spectroscopy and fire radiative power assessment: headfire, backfire and residual smouldering combustion in African savannahs , 2011 .

[28]  W. Salas,et al.  Benchmark map of forest carbon stocks in tropical regions across three continents , 2011, Proceedings of the National Academy of Sciences.

[29]  Mikhail Sofiev,et al.  Evaluation of the smoke-injection height from wild-land fires using remote-sensing data , 2011 .

[30]  J. Randerson,et al.  Daily and 3‐hourly variability in global fire emissions and consequences for atmospheric model predictions of carbon monoxide , 2011 .

[31]  Rebecca Castano,et al.  The ACOS CO 2 retrieval algorithm – Part 1: Description and validation against synthetic observations , 2011 .

[32]  C. Justice,et al.  Vegetation fires in the himalayan region – Aerosol load, black carbon emissions and smoke plume heights , 2012 .

[33]  Fred Moshary,et al.  Smoke plume optical properties and transport observed by a multi-wavelength lidar, sunphotometer and satellite , 2012 .

[34]  Hartmut Boesch,et al.  First satellite measurements of carbon dioxide and methane emission ratios in wildfire plumes , 2013 .

[35]  Michael J. Garay,et al.  Stereoscopic Height and Wind Retrievals for Aerosol Plumes with the MISR INteractive eXplorer (MINX) , 2013, Remote. Sens..

[36]  Christopher W. O'Dell,et al.  Semi-autonomous sounding selection for OCO-2 , 2013 .

[37]  J. Rogan,et al.  Evaluating MODIS active fire products in subtropical Yucatán forest , 2013 .

[38]  Satoru Suzuki,et al.  Estimates of carbon emissions from forest fires in Japan, 1979–2008 , 2013 .

[39]  S. Panigrahy,et al.  Spectral modelling near the 1.6 μm window for satellite based estimation of CO2. , 2014, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[40]  Johannes W. Kaiser,et al.  Constraining CO 2 emissions from open biomass burning by satellite observations of co-emitted species: a method and its application to wildfires in Siberia , 2014 .

[41]  Y. Yamaguchi,et al.  Spatio-temporal evaluation of carbon emissions from biomass burning in Southeast Asia during the period 2001–2010 , 2014 .

[42]  Yasushi Yamaguchi,et al.  A high-resolution and multi-year emissions inventory for biomass burning in Southeast Asia during 2001–2010 , 2014 .

[43]  Jan-Peter Muller,et al.  Automated Stereo Retrieval of Smoke Plume Injection Heights and Retrieval of Smoke Plume Masks From AATSR and Their Assessment With CALIPSO and MISR , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[44]  David Crisp,et al.  The Orbiting Carbon Observatory (OCO-2): spectrometer performance evaluation using pre-launch direct sun measurements , 2014 .

[45]  P. Hari,et al.  Prescribed burning of logging slash in the boreal forest of Finland: emissions and effects on meteorological quantities and soil properties , 2014 .

[46]  Nicholas C. Coops,et al.  Detecting forest damage after a low-severity fire using remote sensing at multiple scales , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[47]  Y. Yamaguchi,et al.  High-Resolution Mapping of Biomass Burning Emissions in Three Tropical Regions. , 2015, Environmental science & technology.

[48]  M. Mack,et al.  A Canopy Shift in Interior Alaskan Boreal Forests: Consequences for Above- and Belowground Carbon and Nitrogen Pools during Post-fire Succession , 2015, Ecosystems.

[49]  Xin Huang,et al.  Estimating emissions from agricultural fires in the North China Plain based on MODIS fire radiative power , 2015 .

[50]  Rui Zhang,et al.  A Comparison and Validation of Atmosphere CO 2 Concentration OCO-2-Based Observations and TCCON-Based Observations , 2016 .

[51]  James McDuffie,et al.  Quantification of uncertainties in OCO-2 measurements of XCO 2 :simulations and linear error analysis , 2016 .

[52]  B. Connor,et al.  Quantification of uncertainties in OCO-2 measurements of XCO 2 : simulations and linear error analysis , 2016 .

[53]  David Crisp,et al.  The on-orbit performance of the Orbiting Carbon Observatory-2 (OCO-2) instrument and its radiometrically calibrated products , 2016 .

[54]  P. Formenti,et al.  Probing into the aging dynamics of biomass burning aerosol by using satellite measurements of aerosol optical depth and carbon monoxide , 2016 .

[55]  Jianlei Lang,et al.  A comprehensive biomass burning emission inventory with high spatial and temporal resolution in China , 2016 .

[56]  Jeffrey G. Masek,et al.  Disturbance and the carbon balance of US forests: A quantitative review of impacts from harvests, fires, insects, and droughts , 2016 .

[57]  David Crisp,et al.  Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) X CO 2 measurements with TCCON , 2016 .

[58]  Rebecca Castano,et al.  The Orbiting Carbon Observatory-2: first 18 months of science data products , 2016 .

[59]  A. Masiero,et al.  Mapping fire regimes in China using MODIS active fire and burned area data , 2017 .

[60]  Johannes W. Kaiser,et al.  Historic global biomass burning emissions for CMIP6 (BB4CMIP) based on merging satellite observations with proxies and fire models (1750-2015) , 2017 .

[61]  O. Schneising,et al.  CO2 emission of Indonesian fires in 2015 estimated from satellite‐derived atmospheric CO2 concentrations , 2017 .

[62]  Jing Li,et al.  A Review of Wetland Remote Sensing , 2017, Sensors.

[63]  David M. Rider,et al.  Preflight Spectral Calibration of the Orbiting Carbon Observatory 2 , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[64]  D. Wunch,et al.  The Orbiting Carbon Observatory (OCO-2) tracks 2–3 peta-gram increase in carbon release to the atmosphere during the 2014–2016 El Niño , 2017, Scientific Reports.

[65]  Xiufeng Wang,et al.  CO2 emissions from the 2010 Russian wildfires using GOSAT data. , 2017, Environmental pollution.

[66]  R. Kahn,et al.  Biomass-burning smoke heights over the Amazon observed from space , 2019, Atmospheric Chemistry and Physics.