Space–time analysis using a general product–sum model

Iaco, Sandra De; Myers, Donald E.; Posa, D.

doi:10.1016/s0167-7152(00)00200-5

Cited by 161 publications

(150 citation statements)

References 9 publications

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Section: Characterization Of Spatio-temporal Covariancementioning

confidence: 99%

“…Models used include the metric model (Dimitrakopoulos and Luo, 1994), linear model (Rouhani and Hall, 1989), product model (De Cesare et al, 1996), non-separable model (Cressie and Huang, 1999), and generalized product-sum covariance model (De Iaco et al, 2001). The approach developed in this paper uses a generalized product-sum covariance model (De Iaco et al, 2001). This model affords a number of advantages relative to other covariance models: (1) a product sum covariance model outperformed other models in terms of prediction accuracy in a recent study using GOSAT satellite data (Guo et al, 2013), (2) it is relatively easy to implement (De Iaco et al, 2001), and (3) it is more flexible than a non-separable covariance model (De Cesare et al, 2001a).…”

Section: Characterization Of Spatio-temporal Covariancementioning

confidence: 99%

“…It can (1) fill in temporal gaps in the observations, (2) create maps at higher temporal resolutions than a purely spatial approach, (3) produce more accurate estimates when observations have variable spatio-temporal coverage, and (4) predict future values (i.e., extrapolate temporally). Among other improvements, we develop an efficient method for subsampling satellite observations and utilize the product-sum covariance model (e.g., De Iaco et al, 2001) that is easy to parameterize, which makes it applicable to both dense and sparse datasets. The entire work has been conducted in Matlab 2012a.…”

Section: Introductionmentioning

confidence: 99%

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Spatio-temporal approach to moving window block kriging of satellite data v1.0

et al. 2017

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Abstract. Numerous existing satellites observe physical or environmental properties of the Earth system. Many of these satellites provide global-scale observations, but these observations are often sparse and noisy. By contrast, contiguous, global maps are often most useful to the scientific community (i.e., Level 3 products). We develop a spatio-temporal moving window block kriging method to create contiguous maps from sparse and/or noisy satellite observations. This approach exhibits several advantages over existing methods: (1) it allows for flexibility in setting the spatial resolution of the Level 3 map, (2) it is applicable to observations with variable density, (3) it produces a rigorous uncertainty estimate, (4) it exploits both spatial and temporal correlations in the data, and (5) it facilitates estimation in real time. Moreover, this approach only requires the assumption that the observable quantity exhibits spatial and temporal correlations that are inferable from the data. We test this method by creating Level 3 products from satellite observations of CO 2 (XCO 2 ) from the Greenhouse Gases Observing Satellite (GOSAT), CH 4 (XCH 4 ) from the Infrared Atmospheric Sounding Interferometer (IASI) and solar-induced chlorophyll fluorescence (SIF) from the Global Ozone Monitoring Experiment-2 (GOME-2). We evaluate and analyze the difference in performance of spatio-temporal vs. recently developed spatial kriging methods.

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Section: Characterization Of Spatio-temporal Covariancementioning

confidence: 99%

Section: Characterization Of Spatio-temporal Covariancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatio-temporal approach to moving window block kriging of satellite data v1.0

et al. 2017

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“…where [19][20][21] and an extra global nugget N ST to capture the nugget effect [13], and is given by…”

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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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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“…Reference [17] derived a spectral approach that allowed one to obtain many classes of non-separable, spatio-temporal stationary covariance functions, and [18] worked a natural generalization by using completely monotone functions and functions whose first derivative was completely monotone [16]. References [19] and [20] introduced a product-sum model. A Bayesian maximum entropy (BME) method was used for the space/time estimation of Petrachloroethylene in New Jersey [21].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Variability of Shallow Groundwater Quality in a Hilly Red-Soil Agricultural Catchment in Subtropical Central China

Luo¹,

Li²,

Li³

et al. 2015

IJG

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Groundwater quality varies not only in space but also in time. In order to analyze the spatiotemporal variety of ground water quality, the concentration of ammonium nitrogen (NH 4 N), nitrate nitrogen (NO 3 N), total nitrogen (TN) and total phosphorus (TP) in very shallow groundwater were investigated in a red-soil catchment in subtropical central China, based on a three-dimensional kriging method. The spatio-temporal analysis demonstrated that NH 4 N, NO 3 N and TP presented strong spatio-temporal autocorrelation (with a nugget-to-sill ratio of <25%) and that TN presented a moderate spatio-temporal autocorrelation (with a nugget-to-sill ratio between 25% and 75%). According to the Chinese Groundwater Quality Standards, the ratio of areas contaminated by NH 4 N, NO 3 N, TN and TP to the whole catchment was 20.05%, 1.46%, 5.07%, 5.98%, respectively. The 3D delineation of continuously dynamic variation of contaminated area indicated that the catchment's very shallow groundwater had a moderate contamination by NH 4 N, slight by TN and TP, and almost non by NO 3 N. Although the contaminated area was very small, only occurring in small dispersed patches, a close attention should be paid to the shallow groundwater quality because local farmers obtain their domestic drinking water directly from this shallow groundwater without any treatment prior to consuming and the potential health hazard is considerable. The findings from this study highlight the importance of surveillance of the contaminated area over time for decision making to protect public health and maintain sustainable development of * Corresponding author. Q. Luo et al.2 the catchment.

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Space–time analysis using a general product–sum model

Cited by 161 publications

References 9 publications

Spatio-temporal approach to moving window block kriging of satellite data v1.0

Spatio-temporal approach to moving window block kriging of satellite data v1.0

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

Spatio-Temporal Variability of Shallow Groundwater Quality in a Hilly Red-Soil Agricultural Catchment in Subtropical Central China

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