A XCO Retrieval Algorithm Coupled Spatial Correlation for the Aerosol and Carbon Detection Lidar

Pei, Zhipeng; Han, Ge; Shi, Tianqi; Ma, Xin; Gong, Wei

doi:10.1016/j.atmosenv.2023.119933

Cited by 7 publications

(6 citation statements)

References 52 publications

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“…The rapid development of techniques such as satellite-based estimation of atmospheric CO 2 concentrations and monitoring pf CO 2 emissions using mobile devices has enabled accurate measurements of CO 2 concentrations at various scales. This advancement provides robust support for numerous ecological studies [4,5]. Carbon uptake from terrestrial ecosystems has significantly slowed the rate of climate change.…”

Section: Introductionmentioning

confidence: 92%

Relationship between CO2 Fertilization Effects, and Stand Age, Stand Type, and Site Conditions

Bian,

Wang,

et al. 2023

Remote Sensing

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The CO2 fertilization effect (CFE) plays a crucial role in the amelioration of climate change. Many physiological and environmental factors, such as stand age, stand type, and site conditions, may affect the extent of the CFE. However, the relationship between the CFE and these factors remains elusive. In this study, we used the emerging gross primary production (GPP) remote sensing products, with GPP predicted using eddy covariance–light use efficiency models (EC-LUE GPP) based on satellite near-infrared reflectance of vegetation (NIRv GPP) and assessed with a random forest model to explore the CFE trends with stand age in a coniferous forest and a broad-leaved forest in Heilongjiang Province, China. We additionally compared the differences among the CFEs under different site conditions. The CFEs in coniferous forests and broad-leaved forests both showed a rapid increase in stands of 10 to 20 years of age, followed by a decline after reaching a maximum, with the rate of decline reducing with age. Eventually, CFE remained stable in stands near 100 years of age. However, the CFE in coniferous forests exhibited more extended periods of rapid increase and a higher maximum than in broad-leaved forests. Moreover, in this study, we used the site class index (SCI) to grade site conditions. The results demonstrate that the CFE differed significantly under different levels of site conditions, and these differences gradually decreased with age. The site with the highest SCI had fewer environmental restrictions on the CFE, and consequently, the CFE rate of decline was faster. Our results are of significance in understanding the CFE and adapting to future changes in atmospheric CO2 concentration.

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Section: Introductionmentioning

confidence: 92%

Relationship between CO2 Fertilization Effects, and Stand Age, Stand Type, and Site Conditions

Bian,

Wang,

et al. 2023

Remote Sensing

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“…The MAE is the average value of the absolute value of the deviation between the estimated value and the real value. The calculation formula is shown in (13). The weight of the difference of all sub items on the average value is equal, which can reflect the actual situation of the difference between the estimated value and the real value.…”

Section: Accuracy Evaluation Indexmentioning

confidence: 99%

“…Compared with ground-based observation networks, satellite remote sensing observation can cover the Earth's surface to a great extent, but there are also some deficiencies [11,12]. For example, due to the observation limitations of passive satellites, the atmospheric carbon dioxide column concentration (XCO 2 ) can only be obtained under weather conditions of no cloud cover or low aerosol concentration [13,14]. Due to the restriction of the solar altitude angle, it is also difficult for satellites to observe effective data in high latitudes.…”

Section: Introductionmentioning

confidence: 99%

XCO2 Fusion Algorithm Based on Multi-Source Greenhouse Gas Satellites and CarbonTracker

Liang,

Pang,

Chen

et al. 2023

Atmosphere

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In view of the urgent need for high coverage and high-resolution atmospheric CO2 data in the study of carbon neutralization and global CO2 change research, this study combines the Kriging interpolation and the Triple Collision (TC) algorithm to fuse three XCO2 datasets, OCO-2, GOSAT, and CarbonTracker, to obtain a 1° × 1° half-monthly average XCO2 dataset. Through a sub division of the Kriging interpolation, the average coverages of the OCO-2 and GOSAT XCO2 interpolating datasets are increased by 53.65% and 48.5%, respectively. In order to evaluate the accuracy of the TC fusion dataset, this study used a reliable reference dataset, TCCON data, as the verification data. Through comparative analysis, the MAE of the fusion dataset is 0.6273 ppm, RMSE is 0.7683 ppm, and R2 is 0.8279. It can be seen that the combination of Kriging interpolation and TC algorithm can effectively improve the coverage and accuracy of the XCO2 dataset.

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“…In the context of global warming, the climate problems caused by greenhouse gas (GHG) emissions have received widespread attention in recent years [1][2][3][4][5][6]. In December 2020, at the 75th Session of the United Nations General Assembly, China formally and explicitly proposed reducing the intensity of carbon emissions and formulating an action program for carbon emissions peaking by 2030, followed by a steady decline after peaking [7].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis and High−Precision Modeling of Tropospheric CH4 in the Yangtze River Delta of China Obtained from the TROPOMI and GOSAT

Cai,

Xiang

2024

Atmosphere

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Remote sensing satellite monitoring involving the use of shortwave infrared (SWIR) solar backscatter radiation to measure atmospheric CH4 column concentrations provides wide−ranging and accurate data for quantitatively determining atmospheric CH4 emissions and is highly important for human studies of atmospheric composition and environmental protection. The ESA−launched Sentinel−2 satellite equipped with a tropospheric monitoring instrument (TROPOMI) can provide the concentration of CH4 columns in every piece of the global atmosphere every day. However, these data may be affected by surface albedo, SWIR, aerosols, cirrus cloud scattering, and other factors. The greenhouse gas observing satellite (GOSAT) launched by Japan has fairly accurate data that are minimally affected by the aforementioned factors; however, its data density is much less than that of the TROPOMI. In this study, we propose a CH4 model that combines the TROPOMI and GOSAT data. We construct the model by analyzing the data from the TROPOMI and GOSAT at the same location at the same time. Then, we apply the proposed model to a certain location at a certain time with TROPOMI data but without GOSAT data to obtain a large range of high−precision CH4 data. The most developed urban agglomeration in the Yangtze River Delta in China was selected for model construction and the correlations between the TROPOMI and GOSAT data and their spatial and temporal trends were analyzed. First, we analyzed the CH4 concentrations in the same area measured by both models. The results revealed a high degree of temporal and spatial correlation in the YRD region. The correlation coefficient reached 0.71 in the metropolitan area of the YRD. At the small−city scale, the correlation is much more significant, with the correlation reaching 0.80, 0.79, and 0.71 for Nanjing, Shanghai, and Ningbo, respectively. The most accurate model was screened through comparative construction to calibrate the TROPOMI data and high−precision and high−coverage CH4 concentration information was obtained for the study area. Five models (linear model, quadratic term model, cubic term model, lognormal model, and logistic model) were used to select the best−fitting model. The magnitudes of the differences in the CH4 concentrations calculated by each model were compared. The final results showed that the linear model, as the prediction model, had the highest accuracy, with a coefficient of determination (R22) of 0.542. To avoid the specificity of the constructed model, we used the same method in several simulations to validate. The coefficient of determination of the model constructed with different stochastic data was greater than 0.5. Subsequently, we used Nanjing as the study area and applied the same method to construct the model. The coefficient of determination of the model (R22) was approximately 0.601. The model constructed in this research can be used not only for data conversion between the same products from different sensors to obtain high−precision data products but also for calibrating newly developed satellite data products that utilize mature data products.

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A XCO Retrieval Algorithm Coupled Spatial Correlation for the Aerosol and Carbon Detection Lidar

Cited by 7 publications

References 52 publications

Relationship between CO2 Fertilization Effects, and Stand Age, Stand Type, and Site Conditions

Relationship between CO2 Fertilization Effects, and Stand Age, Stand Type, and Site Conditions

XCO2 Fusion Algorithm Based on Multi-Source Greenhouse Gas Satellites and CarbonTracker

Comparative Analysis and High−Precision Modeling of Tropospheric CH4 in the Yangtze River Delta of China Obtained from the TROPOMI and GOSAT

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