A Regional Gap-Filling Method Based on Spatiotemporal Variogram Model of &lt;inline-formula&gt; &lt;tex-math notation="TeX"&gt;$\hbox{CO}_{2}$&lt;/tex-math&gt;&lt;/inline-formula&gt; Columns

Zeng, Zhao‐Cheng; Lei, Liping; Hou, Shu-Jin; Ru, Fei; Guan, Xianhua; Zhang, Bing

doi:10.1109/tgrs.2013.2273807

Cited by 45 publications

(14 citation statements)

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“…Weights of observations used for interpolation are determined by the geometry of observations and the spatio-temporal correlation structure of the data. Spatial and temporal information would be used for variogram modeling of the correlation structure [19,31] as shown in Equation (6).γ…”

Section: Conventional Spatio-temporal Krigingmentioning

confidence: 99%

“…Once the empirical variogram has been constructed, we need to select a spatio-temporal variogram model to fit it. As shown in Zeng et al [19,31], the spatio-temporal variogram model adopted here (Equation (7)) is a combination of the product-sum model [51,52] and an extra global nugget model to capture the nugget effect [53] (last term in Equation (7)).…”

Section: Conventional Spatio-temporal Krigingmentioning

confidence: 99%

“…They observe XCO 2 but with different spatial/temporal resolution, prior vertical profile estimates, local overpass time, data precision, and observing gaps. As a result, there is an opportunity to generate a long time series of global XCO 2 dataset starting from 2003 using XCO 2 retrievals from these satellites with careful integration and gap-filling.Gap-filling satellite-observed XCO 2 has been investigated in several studies from different perspectives [31][32][33]. Geostatistical approaches, especially kriging, were widely used for GOSAT observed XCO 2 Level 3 product production [17,34,35].…”

mentioning

confidence: 99%

“…Gap-filling satellite-observed XCO 2 has been investigated in several studies from different perspectives [31][32][33]. Geostatistical approaches, especially kriging, were widely used for GOSAT observed XCO 2 Level 3 product production [17,34,35].…”

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confidence: 99%

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Spatio-Temporal Mapping of Multi-Satellite Observed Column Atmospheric CO2 Using Precision-Weighted Kriging Method

Lei

Zhang

et al. 2020

Remote Sensing

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Column-averaged dry air mole fraction of atmospheric CO2 (XCO2), obtained by multiple satellite observations since 2003 such as ENVISAT/SCIAMACHY, GOSAT, and OCO-2 satellite, is valuable for understanding the spatio-temporal variations of atmospheric CO2 concentrations which are related to carbon uptake and emissions. In order to construct long-term spatio-temporal continuous XCO2 from multiple satellites with different temporal and spatial periods of observations, we developed a precision-weighted spatio-temporal kriging method for integrating and mapping multi-satellite observed XCO2. The approach integrated XCO2 from different sensors considering differences in vertical sensitivity, overpass time, the field of view, repeat cycle and measurement precision. We produced globally mapped XCO2 (GM-XCO2) with spatial/temporal resolution of 1 × 1 degree every eight days from 2003 to 2016 with corresponding data precision and interpolation uncertainty in each grid. The predicted GM-XCO2 precision improved in most grids compared with conventional spatio-temporal kriging results, especially during the satellites overlapping period (0.3–0.5 ppm). The method showed good reliability with R2 of 0.97 from cross-validation. GM-XCO2 showed good accuracy with a standard deviation of bias from total carbon column observing network (TCCON) measurements of 1.05 ppm. This method has potential applications for integrating and mapping XCO2 or other similar datasets observed from multiple satellite sensors. The resulting GM-XCO2 product may be also used in different carbon cycle research applications with different precision requirements.

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Section: Conventional Spatio-temporal Krigingmentioning

confidence: 99%

Section: Conventional Spatio-temporal Krigingmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Spatio-Temporal Mapping of Multi-Satellite Observed Column Atmospheric CO2 Using Precision-Weighted Kriging Method

Lei

Zhang

et al. 2020

Remote Sensing

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“…In step 3, we applied a gap-filling interpolation method to the integrated XCO 2 to produce continuous global XCO 2 maps in time and space. The spatio-temporal geostatistical mapping method, GM-XCO 2 , applied in GOSAT observations has been previously used in global carbon research studies [28,31,63,64]. Considering the spatial connectivity used in variogram modelling, we divided global land area (within 45 • S-60 • N) into five continents.…”

Section: Satellite-observed Xcomentioning

confidence: 99%

Evidence of Carbon Uptake Associated with Vegetation Greening Trends in Eastern China

Lei

Baker

et al. 2020

Remote Sensing

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Persistent and widespread increase of vegetation cover, identified as greening, has been observed in areas of the planet over late 20th century and early 21st century by satellite-derived vegetation indices. It is difficult to verify whether these regions are net carbon sinks or sources by studying vegetation indices alone. In this study, we investigate greening trends in Eastern China (EC) and corresponding trends in atmospheric CO2 concentrations. We used multiple vegetation indices including NDVI and EVI to characterize changes in vegetation activity over EC from 2003 to 2016. Gap-filled time series of column-averaged CO2 dry air mole fraction (XCO2) from January 2003 to May 2016, based on observations from SCIAMACHY, GOSAT, and OCO-2 satellites, were used to calculate XCO2 changes during growing season for 13 years. We derived a relationship between XCO2 and surface net CO2 fluxes from two inversion model simulations, CarbonTracker and Monitoring Atmospheric Composition and Climate (MACC), and used those relationships to estimate the biospheric CO2 flux enhancement based on satellite observed XCO2 changes. We observed significant growing period (GP) greening trends in NDVI and EVI related to cropland intensification and forest growth in the region. After removing the influence of large urban center CO2 emissions, we estimated an enhanced XCO2 drawdown during the GP of −0.070 to −0.084 ppm yr−1. Increased carbon uptake during the GP was estimated to be 28.41 to 46.04 Tg C, mainly from land management, which could offset about 2–3% of EC’s annual fossil fuel emissions. These results show the potential of using multi-satellite observed XCO2 to estimate carbon fluxes from the regional biosphere, which could be used to verify natural sinks included as national contributions of greenhouse gas emissions reduction in international climate change agreements like the UNFCC Paris Accord.

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Assessing accuracy and precision for space‐based measurements of carbon dioxide: An associated statistical methodology revisited

Yung

Shia

et al. 2017

Earth and Space Science

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Analyzing retrieval accuracy and precision is an important element of space-based CO 2 retrievals. However, this error analysis is sometimes challenging to perform rigorously because of the subtlety of Multivariate Statistics. To help address this issue, we revisit some fundamentals of Multivariate Statistics that help reveal the statistical essence of the associated error analysis. We show that the related statistical methodology is useful for revealing the intrinsic discrepancy and relation between the retrieval error for a nonzero-variate CO 2 state and that for a zero-variate one. Our study suggests that the two scenarios essentially yield the same-magnitude accuracy, while the latter scenario yields a better precision than the former. We also use this methodology to obtain a rigorous framework systematically and explore a broadly used approximate framework for analyzing CO 2 retrieval errors. The approximate framework introduces errors due to an essential, but often forgotten, fact that a priori climatology in reality is never equal to the true state. Due to the nature of the problem considered, realistic numerical simulations that produce synthetic spectra may be more appropriate than remote sensing data for our specific exploration. As highlighted in our retrieval simulations, utilizing the approximate framework may not be universally satisfactory in assessing the accuracy and precision of Xco 2 retrievals (with errors up to 0.17-0.28 ppm and 1.4-1.7 ppm, respectively, at SNR = 400). In situ measurements of CO 2 are needed to further our understanding of this issue and related implications.

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A Regional Gap-Filling Method Based on Spatiotemporal Variogram Model of <inline-formula> <tex-math notation="TeX">$\hbox{CO}_{2}$</tex-math></inline-formula> Columns

Cited by 45 publications

References 40 publications

Spatio-Temporal Mapping of Multi-Satellite Observed Column Atmospheric CO2 Using Precision-Weighted Kriging Method

Spatio-Temporal Mapping of Multi-Satellite Observed Column Atmospheric CO2 Using Precision-Weighted Kriging Method

Evidence of Carbon Uptake Associated with Vegetation Greening Trends in Eastern China

Assessing accuracy and precision for space‐based measurements of carbon dioxide: An associated statistical methodology revisited

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