Mapping of satellite Earth observations using moving window block kriging

Abstract

Abstract. Global gridded maps (a.k.a. Level 3 products) of Earth system properties observed by satellites are central to understanding the spatiotemporal variability of these properties. They also typically serve either as inputs into biogeochemical models, or as independent data for evaluating such models. Spatial binning is a common method for generating contiguous maps, but this approach results in a loss of information, especially when the measurement noise is low relative to the degree of spatiotemporal variability. Such "binned" fields typically also lack a quantitative measure of uncertainty. Geostatistical mapping has previously been shown to make higher spatiotemporal resolution maps possible, and also provides a measure of the uncertainty associated with the gridded products. This study proposes a flexible moving window block kriging method that can be used as a tool for creating high spatiotemporal resolution maps from satellite data. It relies only on the assumption that the observed physical quantity exhibits spatial correlation that can be inferred from the observations. The method has several innovations relative to previously applied methods: (1) it provides flexibility in the spatial resolution of the contiguous maps (2) it is applicable for physical quantities with varying spatiotemporal coverage (i.e., density of measurements) by utilizing a more general and versatile data sampling approach, and (3) it provides rigorous assessments of the uncertainty associated with the gridded products. The method is demonstrated by creating Level 3 products from observations of column-integrated carbon dioxide (XCO2) from the GOSAT satellite, and solar induced fluorescence (SIF) from the GOME-2 instrument.

Global land mapping of satellite-observed CO2 total columns using spatio-temporal geostatistics

“…Spatial-only kriging, a conventional geostatistical method, was widely adopted to generate the XCO 2 mapping data products (Tomosada et al, 2008(Tomosada et al, , 2009Liu et al, 2012;Watanabe, 2015). Hammerling et al (2012aHammerling et al ( , 2012b and Tadić et al (2014) extended the spatial-only kriging method to a global scale using a moving window technique. This conventional spatial-only geostatistical method, which only makes use of spatial correlation, does not take into account the temporal correlation structure of the CO 2 data, and therefore the dynamic CO 2 temporal variations including the annual increase and seasonal cycles (WMO, 2014;Schneising et al, 2014) are not fully considered.…”

Prediction of Satellite-Based Column CO2 Concentration by Combining Emission Inventory and LULC Information

“…In contrast to the deterministic spatial prediction methods, the geostatistical interpolation methods or kriging are reported to be more accurate. Among the geostatistical interpolation methods, the variants of kriging, for example, simple kriging (SK) [13], ordinary kriging (OK) [14], universal kriging (UK) [15], spatial block kriging (BK) [16], fixed rank kriging (FRK) [17], [18], ordinary cokriging (OCK) [19], and spatio-temporal kriging (STK) [20] are popular spatial and spatiotemporal methods to generate a Level-3 mapping from the satellite-based Level-2 retrievals and other related applications. Among other nongeostatistical interpolation methods, inverse distance weighting (IDW), nearest neighbor (NN), thin-plate spline (TPS), and trend surface analysis (TSA) are a few popular methods [21]].…”

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

“…However, most studies only consider spatial relationships when evaluating temporal changes (Bhat et al, ; Yao et al, ). Data are usually aggregated into temporal bins where the same time stamp is given, even though observations may not be from the same time period (Tadić et al, ). This approach is appropriate if the same locations are sampled in each temporal period (Li & Revesz, ).…”

“…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, ).…”