Predicting spatio-temporal random fields: Some computational aspects

Iaco, Sandra De; Posa, D.

doi:10.1016/j.cageo.2011.11.014

Cited by 35 publications

(25 citation statements)

References 28 publications

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“…Implementation of the spatial-only geostatistical method for analyzing spatially correlated data is well documented in the literature [13,14] Spatiotemporal geostatistical analysis of data is less common, but has increased in recent years [15][16][17]. The extension of spatial-only geostatistical techniques to the space-time domain is not straightforward, since the behavior of a variable over time differs from its behavior over space.…”

Section: Geostatistical Methodsmentioning

confidence: 99%

“…To reduce computational complexity and preserve local variability, data used in the prediction were searched within an appropriate spatiotemporal neighborhood centered on the predicting point [17]. As in [6], the predicting position is maintained as a missing value if no more than 20 data are found within the neighborhood.…”

Section: S T S T N H H S T H H R S T R S H T H N H Hmentioning

confidence: 99%

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Incorporating temporal variability to improve geostatistical analysis of satellite-observed CO2 in China

et al. 2013

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Observations of atmospheric carbon dioxide (CO 2 ) from satellites offer new data sources to understand global carbon cycling. The correlation structure of satellite-observed CO 2 can be analyzed and modeled by geostatistical methods, and CO 2 values at unsampled locations can be predicted with a correlation model. Conventional geostatistical analysis only investigates the spatial correlation of CO 2 , and does not consider temporal variation in the satellite-observed CO 2 data. In this paper, a spatiotemporal geostatistical method that incorporates temporal variability is implemented and assessed for analyzing the spatiotemporal correlation structure and prediction of monthly CO 2 in China. The spatiotemporal correlation is estimated and modeled by a product-sum variogram model with a global nugget component. The variogram result indicates a significant degree of temporal correlation within satellite-observed CO 2 data sets in China. Prediction of monthly CO 2 using the spatiotemporal variogram model and spacetime kriging procedure is implemented. The prediction is compared with a spatial-only geostatistical prediction approach using a cross-validation technique. The spatiotemporal approach gives better results, with higher correlation coefficient (r 2 ), and less mean absolute prediction error and root mean square error. Moreover, the monthly mapping result generated from the spatiotemporal approach has less prediction uncertainty and more detailed spatial variation of CO 2 than those from the spatial-only approach.CO 2 , Greenhouse Gases Observing Satellite (GOSAT), geostatistical analysis, space-time kriging, product-sum model Citation:Zeng Z C, Lei L P, Guo L J, et al. Incorporating temporal variability to improve geostatistical analysis of satellite-observed

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Section: Geostatistical Methodsmentioning

confidence: 99%

Section: S T S T N H H S T H H R S T R S H T H N H Hmentioning

confidence: 99%

Incorporating temporal variability to improve geostatistical analysis of satellite-observed CO2 in China

et al. 2013

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“…Z can be typically decomposed into a mean component m(s, t) modeling the trend and a stochastic residual component R = {R(s, t), (s, t) ∈ D × T }, which is assumed to be a second-order stationary random field [23], [24]. In this paper, a further decomposition [25] of the deterministic spatiotemporal mean component m(s, t) is adopted…”

Section: A Spatiotemporal Random Field Modelmentioning

confidence: 99%

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

Zeng

Lei

Hou

et al. 2014

IEEE Trans. Geosci. Remote Sensing

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A precise and high-resolution spatiotemporal distribution of atmospheric carbon dioxide (CO 2 ) is important in identifying and quantifying the CO 2 source and sinks on regional scales and emissions from discrete point sources. We propose the use of a regional gap-filling method by modeling the spatiotemporal correlation structures of column-averaged CO 2 dry air mole fractions (Xco 2 ) on a regional scale, using data from the Atmospheric CO 2 Observations from Space retrievals of the Greenhouse Gases Observing Satellite (ACOS-GOSAT) measurements over mainland China. The accuracy of the gap-filling results is verified by cross-validation and comparison with ground-based measurements. As the results of the spatiotemporal gap-filling method are applied to mainland China, the correlation coefficient (r 2 ) between the predicted values and true ones is greater than 0.85, the mean absolute prediction error is less than 1.5 ppm in cross-validation, and the seasonal cycle of the gap-filled data is generally in agreement with ground-based measurements. Finally, we compare the prediction accuracy based on our method with that based on the commonly used spatial-only kriging to further demonstrate the improved prediction accuracy. The applied regional gap-filling method, which makes full use of the multitemporal ACOS-GOSAT data, can generate a regional regular spatial distribution map of (Xco 2 ) at high spatial and temporal resolutions.Index Terms-Atmospheric CO 2 Observations from Space retrievals of the Greenhouse Gases Observing Satellite (ACOS-GOSAT), product-sum model, space-time kriging, Xco 2 .

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“…Obviously, this last model is just a particular case (L = 1) of the spatio-temporal LCM defined in (17) and it is much more restrictive than the linear model of coregionalization since it requires that all the variables have the same correlation function, with possible changes in the sill values. Note that, if a cross-covariance is separable, then it is symmetric.…”

Section: Assumptions In the Spatio-temporal Lcmmentioning

confidence: 99%

“…After fitting a model for γ ST ,w h i c hm u s tb e conditionally negative definite, ordinary kriging can be applied to generate the environmental risk assessment maps. In this case, the GSLib routine "K2ST" [17] can be used for prediction purposes in space and time.…”

Section: Prediction and Risk Assessment In Space-timementioning

confidence: 99%

Advances in Spatio-Temporal Modeling and Prediction for Environmental Risk Assessment

Iaco¹,

Maggio²,

Posa³

2012

Air Pollution - A Comprehensive Perspective

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Predicting spatio-temporal random fields: Some computational aspects

Cited by 35 publications

References 28 publications

Incorporating temporal variability to improve geostatistical analysis of satellite-observed CO2 in China

Incorporating temporal variability to improve geostatistical analysis of satellite-observed CO2 in China

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

Advances in Spatio-Temporal Modeling and Prediction for Environmental Risk Assessment

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