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

Abstract

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.

A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks

“…Neural networks (NNs), together with many other machine learning algorithms, have been used with remote sensing datasets in the Earth sciences, especially for carbon and water fluxes estimation (Alemohammad et al, 2017;Jung et al, 2011;Tramontana et al, 2016), land cover mapping (Kussul et al, 2017;Zhu et al, 2017), soil moisture retrievals and downscaling (Alemohammad et al, 2018;Kolassa et al, 2018) or to bypass parameterization . These studies mostly attempted to link the satellite signals with limited in situ observation or model simulations for model training, while taking advantage of the large amount of data in remote sensing observations; they applied the trained algorithm to generate a regional or global dataset.…”

Evaluation of Groundwater Levels in the Arapahoe Aquifer Using Spatiotemporal Regression Kriging

“…Despite the limited applications to groundwater, spatiotemporal kriging is common to a number of other environmental studies. These studies include the analysis and mapping of precipitation (Martínez et al, ); MODIS temperature and precipitation (Hengl et al, ; Hu et al, ); soil moisture, temperature, and electrical conductivity (Gasch et al, ; Wang et al, ); satellite‐observed CO 2 (Tadic et al, ; Tadić et al, ; Zeng et al, ); ozone data (Xu & Shu, ); NO 2 pollutants (Beauchamp et al, ; De Iaco & Posa, ); standardized precipitation index (Bayat et al, ); gamma dose rates (Heuvelink & Griffith, ); solar irradiance forecasting (Aryaputera et al, ); and soil heavy metal distribution (Yang et al, ).…”

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

“…Spatial-only geostatistical methods do not take into account the temporal correlation structure of CO 2 data [19], which may provide extra information. In order to make full use of spatio-tempal correlation of atmospheric CO 2 , a new spatio-temporal kriging method was developed for the global mapping of XCO 2 [19,36]. Because these methods were previously used for observations from a single satellite, measurement error could be assumed to be uniform and not interfere with the kriging approach.…”