Abstract
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.
Highlights
Land carbon storage is strongly influenced by ecosystem disturbances, which influence species composition and structure and cause a net carbon stock reduction [1]
By contrast with biomass burning model (BBM), which calculates CO2 emissions based on the bottom–top mode, the Orbiting Carbon Observatory-2 (OCO-2) model estimates CO2 emissions based on the top–bottom mode
We looked at actively burning forest and freshly burned areas that could correspond with smoke plumes, meaning we could not use any other higher spatial-resolution images aside from those of Uncertainties in the OCO-2 model may come from the smoke plume area, the modeled XCO2 value of each pixel, as well as the bias of OCO-2 retrieval XCO2 values
Summary
Land carbon storage is strongly influenced by ecosystem disturbances, which influence species composition and structure and cause a net carbon stock reduction [1]. Wildfires play a key role in global biogeochemical cycles, atmospheric composition, and land ecosystem attributes, all of which influence climate systems [1,2]. With increasing temperatures due to global warming and the accompanying drying trends, wildfires have increased in boreal forests over the last few decades [6]. Boreal forests are the most important global terrestrial carbon stocks, and CO2 released into the atmosphere by wildfires may convert forests from carbon sinks into net sources, which in turn contributes to global warming and affects the carbon cycle [7]. Siberia’s boreal forests cover more than 60% of the total
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