Anthropogenic CO2 emissions assessment of Nile Delta using XCO2 and SIF data from OCO-2 satellite

Shekhar, Ankit; Chen, Jia; Paetzold, Johannes C.; Dietrich, Florian; Zhao, Xiangdong; Bhattacharjee, Shrutilipi; Ruisinger, Veronika; Wofsy, Steven C.

doi:10.1088/1748-9326/ab9cfe

Cited by 39 publications

(21 citation statements)

References 71 publications

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“…The two datasets showed a good correlation with a deter-mined coefficient (R 2 ) of 0.60. Several studies have correlated satellite-based XCO 2 anomalies with CO 2 emissions (Fu et al, 2019;Shekhar et al, 2020). Yang et al (2019) performed a correlation analysis between the GOSAT-based XCO 2 anomalies and the ODIAC CO 2 emissions over China and found a significant correlation with a determined coefficient (R 2 ) of 0.82 which increased up to 0.95 if the analysis was carried out with higher CO 2 emission values.…”

Section: Correlation Analysis Between Oco-2 Xco 2 Anomalies and Odiac Emissionsmentioning

confidence: 99%

Neural-network-based estimation of regional-scale anthropogenic CO2 emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia

Mustafa

Wang

et al. 2021

Atmos. Meas. Tech.

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Abstract. Atmospheric carbon dioxide (CO2) is the most significant greenhouse gas, and its concentration is continuously increasing, mainly as a consequence of anthropogenic activities. Accurate quantification of CO2 is critical for addressing the global challenge of climate change and for designing mitigation strategies aimed at stabilizing CO2 emissions. Satellites provide the most effective way to monitor the concentration of CO2 in the atmosphere. In this study, we utilized the concentration of the column-averaged dry-air mole fraction of CO2, i.e., XCO2 retrieved from a CO2 monitoring satellite, the Orbiting Carbon Observatory-2 (OCO-2), and the net primary productivity (NPP) provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate the anthropogenic CO2 emissions using the Generalized Regression Neural Network (GRNN) over East and West Asia. OCO-2 XCO2, MODIS NPP, and the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) CO2 emission datasets for a period of 5 years (2015–2019) were used in this study. The annual XCO2 anomalies were calculated from the OCO-2 retrievals for each year to remove the larger background CO2 concentrations and seasonal variability. The XCO2 anomaly, NPP, and ODIAC emission datasets from 2015 to 2018 were then used to train the GRNN model, and, finally, the anthropogenic CO2 emissions were estimated for 2019 based on the NPP and XCO2 anomalies derived for the same year. The estimated and the ODIAC CO2 emissions were compared, and the results showed good agreement in terms of spatial distribution. The CO2 emissions were estimated separately over East and West Asia. In addition, correlations between the ODIAC emissions and XCO2 anomalies were also determined separately for East and West Asia, and East Asia exhibited relatively better results. The results showed that satellite-based XCO2 retrievals can be used to estimate the regional-scale anthropogenic CO2 emissions, and the accuracy of the results can be enhanced by further improvement of the GRNN model with the addition of more CO2 emission and concentration datasets.

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Section: Correlation Analysis Between Oco-2 Xco 2 Anomalies and Odiac Emissionsmentioning

confidence: 99%

Neural-network-based estimation of regional-scale anthropogenic CO2 emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia

Mustafa

Wang

et al. 2021

Atmos. Meas. Tech.

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“…The major advantage of OCO-2 SIF includes a roughly 100-fold increase in data acquisition frequency over GOSAT and finer spatial resolution (1.3 × 2.25 km 2 ). This enables OCO-2 to acquire more than 10 5 clear-sky soundings on land per day, thus providing the opportunity to perform in-depth SIF based analysis, such as regional ecophysiological change detection [42,43]. However, OCO-2 SIF soundings do not have full spatial coverage ( Figure S1).…”

Section: Sif Datamentioning

confidence: 99%

Capturing the Impact of the 2018 European Drought and Heat across Different Vegetation Types Using OCO-2 Solar-Induced Fluorescence

et al. 2020

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The European heatwave of 2018 led to record-breaking temperatures and extremely dry conditions in many parts of the continent, resulting in widespread decrease in agricultural yield, early tree-leaf senescence, and increase in forest fires in Northern Europe. Our study aims to capture the impact of the 2018 European heatwave on the terrestrial ecosystem through the lens of a high-resolution solar-induced fluorescence (SIF) data acquired from the Orbiting Carbon Observatory-2 (OCO-2) satellite. SIF is proposed to be a direct proxy for gross primary productivity (GPP) and thus can be used to draw inferences about changes in photosynthetic activity in vegetation due to extreme events. We explore spatial and temporal SIF variation and anomaly in the spring and summer months across different vegetation types (agriculture, broadleaved forest, coniferous forest, and mixed forest) during the European heatwave of 2018 and compare it to non-drought conditions (most of Southern Europe). About one-third of Europe’s land area experienced a consecutive spring and summer drought in 2018. Comparing 2018 to mean conditions (i.e., those in 2015–2017), we found a change in the intra-spring season SIF dynamics for all vegetation types, with lower SIF during the start of spring, followed by an increase in fluorescence from mid-April. Summer, however, showed a significant decrease in SIF. Our results show that particularly agricultural areas were severely affected by the hotter drought of 2018. Furthermore, the intense heat wave in Central Europe showed about a 31% decrease in SIF values during July and August as compared to the mean over the previous three years. Furthermore, our MODIS (Moderate Resolution Imaging Spectroradiometer) and OCO-2 comparative results indicate that especially for coniferous and mixed forests, OCO-2 SIF has a quicker response and a possible higher sensitivity to drought in comparison to MODIS’s fPAR (fraction of absorbed photosynthetically active radiation) and the Normalized Difference Vegetation Index (NDVI) when considering shorter reference periods, which highlights the added value of remotely sensed solar-induced fluorescence for studying the impact of drought on vegetation.

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“…Because they measure the entire atmospheric column, they are also more sensitive to fluxes at regional scales compared to in-situ measurement techniques (Keppel-Aleks et al, 2011;Lauvaux & Davis, 2014). Several studies have demonstrated the usefulness of both ground and satellite-based Xgas measurements to provide top-down estimates of emissions at regional scales (km to 10s of km scales) such as urban regions (Hase et al, 2015;Hedelius et al, 2018;Jones et al, 2021;Makarova et al, 2021;Schwandner et al, 2017;Vogel et al, 2019;Wu et al, 2018Wu et al, , 2020Wunch et al, 2009Wunch et al, , 2019Ye et al, 2020;Zhao et al, 2019), fossil fuel-producing basins (Kort et al, 2014;Luther et al, 2019;Zhang et al, 2020), agricultural regions (Chen et al, 2016;Kille et al, 2019;Viatte et al, 2017) and in state and country-level domains (Shekhar et al, 2020;Turner et al, 2015;Wecht, Jacob, Frankenberg, et al, 2014). This makes Xgas measurements a good complement to other previously used techniques like aircraft and tower-based in-situ measurements for inferring dairy CH 4 emissions in the SJV.…”

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confidence: 99%

Dairy Methane Emissions in California's San Joaquin Valley Inferred With Ground‐Based Remote Sensing Observations in the Summer and Winter

et al. 2021

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Global concentrations of atmospheric CH 4 have substantially increased since 1750 due to the expansion of human agriculture, waste management, and fossil fuel usage (Dlugokencky et al., 2011). CH 4 is a 28 times stronger greenhouse gas than carbon dioxide (CO 2 ) on a century timescale and increased atmospheric CH 4 accounts for 25% of anthropogenic climate forcing to date (Etminan et al., 2016). CH 4 has also contributed to increases in tropospheric ozone, an air pollutant and short-lived greenhouse gas (Fiore et al., 2008), and to stratospheric water vapor, which acts as a sink for stratospheric ozone (Myhre et al., 2013). Additionally, CH 4 has an atmospheric lifetime of 9.8 years, so its atmospheric abundance quickly responds to changes in its emissions (Saunois et al., 2016). Furthermore, there are proven and cost-effective ways to reduce emissions (UNEP, 2011). These characteristics make reducing CH 4 emissions an attractive policy option for mitigating the effects of climate change in the near and intermediate term.The US state of California is committed to reducing its CH 4 emissions to 40% of 2013 levels by 2030 as part of its climate change policy (CARB, 2017). The state has funded specific policies to reduce its emissions, including from its large dairy sector, which makes up 19% of the United States's milk production (CDFA, 2020). CH 4 emissions from the dairy sector are from two main sources: enteric fermentation emissions produced by dairy cows' digestion process and manure management emissions created by the anaerobic decomposition of dairy waste. Emissions from the dairy sector are estimated by the state as 880 Gg/yr, accounting for about half of California's total CH 4 emissions (CARB, 2019). The dairy industry is centered in the San Joaquin Valley (SJV), which contains 87% of the state's 1.7 million large dairy herd (CDFA, 2020). The SJV has also been identified as a large North American CH 4 emissions hotspot in many studies, highlighting the global importance of the region's dairy

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Anthropogenic CO₂ emissions assessment of Nile Delta using XCO₂ and SIF data from OCO-2 satellite

Cited by 39 publications

References 71 publications

Neural-network-based estimation of regional-scale anthropogenic CO<sub>2</sub> emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia

Neural-network-based estimation of regional-scale anthropogenic CO<sub>2</sub> emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia

Capturing the Impact of the 2018 European Drought and Heat across Different Vegetation Types Using OCO-2 Solar-Induced Fluorescence

Dairy Methane Emissions in California's San Joaquin Valley Inferred With Ground‐Based Remote Sensing Observations in the Summer and Winter

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Anthropogenic CO2 emissions assessment of Nile Delta using XCO2 and SIF data from OCO-2 satellite

Cited by 39 publications

References 71 publications

Neural-network-based estimation of regional-scale anthropogenic CO&lt;sub&gt;2&lt;/sub&gt; emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia

Neural-network-based estimation of regional-scale anthropogenic CO&lt;sub&gt;2&lt;/sub&gt; emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia

Capturing the Impact of the 2018 European Drought and Heat across Different Vegetation Types Using OCO-2 Solar-Induced Fluorescence

Dairy Methane Emissions in California's San Joaquin Valley Inferred With Ground‐Based Remote Sensing Observations in the Summer and Winter

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Anthropogenic CO₂ emissions assessment of Nile Delta using XCO₂ and SIF data from OCO-2 satellite

Neural-network-based estimation of regional-scale anthropogenic CO<sub>2</sub> emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia

Neural-network-based estimation of regional-scale anthropogenic CO<sub>2</sub> emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia